Management of Emissions Response Event Generation

Abstract

To provide techniques for performing emission demand response events.SOLUTION: In an example, a cloud-based HVAC control server system receives an emission rate forecast for a predefined future time period. Using the emission rate forecast, multiple emission differential values are created for multiple points in time during the predefined future time period. The emission differential values represent a change in predicted emissions over time. Based on the multiple emission differential values and a predefined maximum number of emission demand response events, an emission demand response event is generated during the predefined future time period. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emission demand response event.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims priority to the following applications, each of which is hereby incorporated by reference in its entirety: U.S. Non - Provisional Application No. 17 / 350,787, filed June 17, 2021, entitled "MANAGING EMISSIONS DEMAND RESPONSE EVENT GENERATION"; U.S. Non - Provisional Application No. 17 / 350,793, filed June 17, 2021, entitled "DYNAMIC ADAPTATION OF EMISSIONS DEMAND RESPONSE EVENTS"; U.S. Non - Provisional Application No. 17 / 350,801, filed June 17, 2021, entitled "MANAGING USER ACCOUNT PARTICIPATION IN EMISSIONS DEMAND RESPONSE EVENTS"; and U.S. Non - Provisional Application No. 17 / 350,808, filed June 17, 2021, entitled "MANAGING EMISSIONS DEMAND RESPONSE EVENT INTENSITY".

Background Art

[0002] Background Thermostats can be used to control heating systems, cooling systems, fans, ventilation systems, dehumidifiers, humidifiers, or any other related systems. Users can benefit from using smart thermostats that can communicate with a cloud - based server via a wireless network. Such wireless network connectivity can enable the thermostat to be remotely controlled by the user or by various services provided by the cloud - based server. Scheduling the electricity consumption of an HVAC system controlled by a thermostat to coincide with times of cleaner electricity availability can reduce carbon emissions.

Summary of the Invention

[0003] overview Various embodiments are described in relation to methods for executing emission demand response events. In some embodiments, methods for executing emission demand response events are described. The method may include receiving emission rate forecasts for a predetermined future period by a cloud-based HVAC control server system. The method may include the cloud-based HVAC control server system using the emission rate forecasts to determine emission differential values ​​for each of several points in time within the predetermined future period, thereby generating a plurality of emission differential values. The emission differential values ​​may represent the change in emissions over time. The method may include the cloud-based HVAC control server system generating emission demand response events having start and end times within a predetermined future period, based on the determined plurality of emission differential values ​​and a predetermined maximum number of emission demand response events. The method may include the cloud-based HVAC control server system causing a thermostat to control the HVAC system in accordance with the generated emission demand response events.

[0004] Embodiments of such a method may include one or more of the following features: the emission differential value for each of the multiple time points may be determined from the difference between a first emission rate before that time point and a second emission rate after that time point; and generated emission demand response events. This may be a preemptive emissions demand response event. For a preemptive emissions demand response event, the cloud-based HVAC control server system may cause the thermostat to adjust the setpoint temperature to increase the utilization of the HVAC system. When the HVAC system is in cooling mode, causing the thermostat to adjust the setpoint temperature for a preemptive emissions demand response event may include causing the setpoint temperature to decrease. When the HVAC system is in heating mode, causing the thermostat to adjust the setpoint temperature for a preemptive emissions demand response event may include causing the setpoint temperature to increase.

[0005] Embodiments of the method may also include one or more of the following features: the generated emission demand response event may be a deferred emission demand response event. For deferred emission demand response events, the cloud-based HVAC control server system may cause the thermostat to adjust a setpoint temperature that reduces the use of the HVAC system. When the HVAC system is in cooling mode, causing the thermostat to adjust the setpoint temperature for a deferred emission demand response event may include increasing the setpoint temperature. When the HVAC system is in heating mode, causing the thermostat to adjust the setpoint temperature for a deferred emission demand response event may include decreasing the setpoint temperature.

[0006] The method may further include determining, for each of several emission differential values, a preemptive event score equal to the emission differential value for preemptive emission demand response events ending at a point in time associated with the emission differential value, thereby generating several preemptive event scores. The method may further include determining, for each of several emission differential values, a deferred event score equal to the negative emission differential value for deferred emission demand response events ending at a point in time associated with the emission differential value, thereby generating several deferred event scores. Generating emission demand response events may be based on a ranking of the several preemptive event scores and several deferred event scores.

[0007] In some embodiments of the method, a predetermined maximum number of emission demand response events may be the maximum number of preemptive emission demand response events within a predetermined future period. Generating emission demand response events may further include limiting the generation of preemptive emission demand response events when the number of previously generated preemptive emission demand response events within a predetermined future period may be equal to the maximum number of preemptive emission demand response events.

[0008] In some embodiments of the method, a predetermined maximum number of emission demand response events may be the maximum number of deferred emission demand response events within a predetermined future period. Generating emission demand response events may further include limiting the generation of deferred emission demand response events when the number of previously generated deferred emission demand response events within a predetermined future period may be equal to the maximum number of deferred emission demand response events.

[0009] In some embodiments, generating an emissions demand response event may further include determining that a previously generated preemptive emissions demand response event has been generated. Generating an emissions demand response event may further include limiting the generation of additional preemptive emissions demand response events to a minimum period after a previously generated preemptive emissions demand response event.

[0010] The method may further include determining that a generated emissions response event may be a deferred emissions response event. The method may further include restricting the generation of new deferred emissions response events within a predetermined minimum period before and after a generated emissions response event. Generating an emissions response event restricts the generation of an emissions response event having an end time later than a predetermined latest time of the day, a predetermined latest time of the day This may further include restricting the generation of emission demand response events that have an earlier start time than earlier times, or both.

[0011] In some embodiments, generating an emissions demand response event may further include comparing the event score for the generated emissions demand response event with a minimum emissions demand response event score. Generating an emissions demand response event may further include determining that the event score for the generated emissions demand response event may be greater than the minimum emissions demand response event score. Causing the thermostat to control the HVAC system in accordance with the generated emissions demand response event may be at least in part based on the determination that the event score may be greater than the minimum emissions demand response event score. A predetermined future period may be 24 hours.

[0012] In several embodiments, a system for executing emission demand response events is described. The system may include a cloud-based power control server system. The cloud-based power control server system may include one or more processors. The cloud-based power control server system may include a memory that is communicatively coupled to and readable by one or more processors and stores processor-readable instructions, which, when executed by one or more processors, cause one or more processors to receive emission rate forecasts for a predetermined future period. One or more processors may use the emission rate forecasts to determine emission differential values ​​for each of a plurality of points in time within a predetermined future period, thereby generating a plurality of emission differential values. The emission differential values ​​may represent the change in emissions over time. One or more processors may generate emission demand response events having start and end times within a predetermined future period, based on the determined plurality of emission differential values ​​and a predetermined maximum number of emission demand response events. One or more processors may cause a thermostat to control the HVAC system according to the generated emission demand response events.

[0013] Embodiments of such a system may further include a plurality of thermostats, including a thermostat. The system may further include an application running on a mobile device configured to control the thermostats via communication with a cloud-based power control server system. In some embodiments, the emission differential value for each of a plurality of time points is determined from the difference between a first emission rate before the time point and a second emission rate after the time point. The generated emission demand response event may be a preemptive emission demand response event. Processor-readable instructions, when executed, further cause one or more processors to cause the thermostats to adjust to a setpoint temperature that increases the use of the HVAC system.

[0014] In some embodiments, a non-transient processor-readable medium is described. The medium may include processor-readable instructions configured to cause one or more processors to receive emission rate forecasts for a predetermined future period. One or more processors may use the emission rate forecasts to determine emission differential values ​​for each of several points in time within the predetermined future period, thereby generating a plurality of emission differential values. The emission differential values ​​may represent the change in emissions over time. One or more processors may generate emission demand response events having start and end times within the predetermined future period, based on the determined plurality of emission differential values ​​and a predetermined maximum number of emission demand response events. One or more processors may cause a thermostat to control the HVAC according to the generated emission demand response events.

[0015] Embodiments of such media may include one or more of the following features: A predetermined maximum number of emission demand response events is a predetermined number of deferred emission demand response events within a predetermined future period. The number may be the maximum number. Processor-readable instructions may be configured to restrict the generation of deferred emission demand response events when the number of previously generated deferred emission demand response events within a given future period may be equal to the maximum number of deferred emission demand response events. Processor-readable instructions may be further configured to restrict the generation of emission demand response events having an end time later than a given latest time of day, to restrict the generation of emission demand response events having an start time earlier than a given earliest time of day, or both.

[0016] Various embodiments of methods for executing emission demand response events are described. In some embodiments, methods for executing emission demand response events are described. The method may include obtaining a plurality of emission rate forecasts by a cloud-based HVAC control server system. Each of the plurality of emission rate forecasts may be received at a different time. The method may include generating an emission demand response event having a start time and an end time based on a first emission rate forecast from the plurality of emission rate forecasts by the cloud-based HVAC control server system. After generating the emission demand response event, the method may include modifying the emission demand response event by the cloud-based HVAC control server system based on subsequent emission rate forecasts from the plurality of emission rate forecasts. The method may include causing a thermostat to control the HVAC system according to the modified emission demand response event by the cloud-based HVAC control server system.

[0017] Embodiments of such a method may include one or more of the following features: A first emission rate forecast may indicate an emission rate change in a first time. A second emission rate forecast, obtained after the first emission rate forecast, may indicate an emission rate change in a second time that is later than the first time. Modifying an emission demand response event may include delaying the emission demand response event based on the difference between the first time and the second time. Modifying an emission demand response event may further include the cloud-based HVAC control server system deciding to obtain a second emission rate forecast from among multiple emission rate forecasts after the start time of the emission demand response event. Modifying an emission demand response event may further include setting the start time of the emission demand response event to start before the second emission rate forecast is received. Modifying an emission demand response event may further include limiting the modification of the start time of the emission demand response event to be after a predetermined minimum time after the end time of a previously generated emission demand response event. Modifying an emissions-demand response event may further include restricting the modification of the end time of the emissions-demand response event so as not to be later than a predetermined latest time of day, restricting the modification of the start time of the emissions-demand response event so as not to be earlier than a predetermined earliest time of day, or both.

[0018] The method may further include having the thermostat control the HVAC system according to a modified emissions demand response event, and then receiving a second emissions rate forecast. The method may further include using the second emissions rate forecast to modify the end time of the emissions demand response event. The method may further include having the thermostat control the HVAC system according to the modified end time of the emissions demand response event. In some embodiments, the emissions demand response event is generated for a duration set to the maximum allowable event duration. The second emissions rate forecast may include the change in emissions rate in the first time. Modifying the end time of the emissions demand response event may further include the cloud-based HVAC control server system deciding to obtain a third emissions rate forecast from a plurality of emissions rate forecasts after the first time. Modifying the end time of the emissions demand response event may include setting the end time of the emissions demand response event to occur before the third emissions rate forecast is received.

[0019] In some embodiments, the emissions demand response event is set to the maximum allowable event duration. Generated for a predetermined duration, the second emission rate forecast may include changes in the emission rate during the first time. Modifying the end time of the emission demand response event may further include the cloud-based HVAC control server system deciding to obtain a third emission rate forecast from among multiple emission rate forecasts within a predetermined minimum period prior to the first time. Modifying the end time of the emission demand response event may further include updating the end time of the emission demand response event to match the first time before the third emission rate forecast from among multiple emission rate forecasts is received.

[0020] In some embodiments, the first emission rate forecast may include the change in the emission rate in a first time period, and the second emission rate forecast may include the change in the emission rate in a second time period that is earlier than the first time period. Modifying the end time of the emission demand response event may further include the cloud-based HVAC control server system deciding to obtain a third emission rate forecast from among the multiple emission rate forecasts after the second time period. Modifying the end time of the emission demand response event may further include setting the end time of the emission demand response event so that it occurs before the third emission rate forecast is received.

[0021] The first emission rate forecast may include the change in the emission rate at a first time. The second emission rate forecast may include the change in the emission rate at a second time, which is later than the first time. Modifying the end time of an emission demand response event may include delaying the end time of the emission demand response event based on the difference between the first time and the second time. Modifying the end time of an emission demand response event may be limited by the maximum allowable event duration.

[0022] In some embodiments, generating emission demand response events may further include, by a cloud-based HVAC control server system, using a first emission rate forecast to determine emission differential values ​​for each of several points in time within a future period covered by the first emission rate forecast, thereby generating a plurality of emission differential values. Emission demand response events may be generated based on the determined plurality of emission differential values.

[0023] In some embodiments, a system for executing emission demand response events is described. The system may include a cloud-based power control server system. The cloud-based power control server system may include one or more processors. The cloud-based power control server system may include a memory that is communicatively coupled to one or more processors and is readable by one or more processors and stores processor-readable instructions, which, when executed by one or more processors, cause one or more processors to obtain a plurality of emission rate forecasts. Each of the plurality of emission rate forecasts may be received at different times. The system may include a memory that is communicatively coupled to one or more processors and is readable by one or more processors and stores processor-readable instructions, which, when executed by one or more processors, causes one or more processors to generate an emission demand response event having a start time and an end time based on a first emission rate forecast among the plurality of emission rate forecasts. The system may include a memory that is communicatively connected to one or more processors and is readable by one or more processors, and which stores processor-readable instructions, and when executed by one or more processors, the processor-readable instructions cause one or more processors to modify an emissions demand response event after the emission demand response event has been generated, based on a subsequent emission rate forecast among a plurality of emission rate forecasts. The system may include a memory that is communicatively connected to one or more processors and is readable by one or more processors, and which stores processor-readable instructions, and when executed by one or more processors, the processors cause one or more processors to modify an emissions demand response event based on a subsequent emission rate forecast among a plurality of emission rate forecasts. The HVAC system will be controlled according to the vents.

[0024] Embodiments of such a system may further include a plurality of thermostats including a thermostat. The system may further include an application executed on a mobile device configured to control the thermostat via communication with a cloud-based power control server system. The system may further include an interface configured to obtain a plurality of emission rate predictions from an emission data system that is remotely accessible via a network. In some embodiments, the first emission rate prediction may indicate a change in the emission rate at a first time. A second emission rate prediction obtained after the first emission rate prediction may indicate a change in the emission rate at a second time that is later than the first time. The emission demand response event may be modified by delaying the emission demand response event based on the difference between the first time and the second time.

[0025] In some embodiments, a non-transitory processor-readable medium is described. The medium may include processor-readable instructions configured to cause one or more processors to obtain a plurality of emission rate predictions. Each of the plurality of emission rate predictions may be received at a different time. The one or more processors may generate an emission demand response event having a start time and an end time based on a first emission rate prediction of the plurality of emission rate predictions. After the emission demand response event is generated, the one or more processors may modify the emission demand response event based on a subsequent emission rate prediction of the plurality of emission rate predictions. The one or more processors may cause a thermostat to control an HVAC system according to the modified emission demand response event.

[0026] Embodiments of such a medium may include one or more of the following features: The processor-readable instruction is further configured to receive a second emission rate forecast after causing the thermostat to control the HVAC system according to a modified emission demand response event. The processor-readable instruction is further configured to modify the end time of the emission demand response event using the second emission rate forecast. The processor-readable instruction is further configured to cause the thermostat to control the HVAC system according to the modified end time of the emission demand response event. In some embodiments, the emission demand response event is generated for a duration set to the maximum allowable event duration. The second emission rate forecast may include an emission rate change in a first time. The processor-readable instruction is further configured to modify the end time of the emission demand response event by setting the end time of the event such that the system decides to obtain a third emission rate forecast after the first time and before the third emission rate forecast is received.

[0027] In some embodiments, a method for performing a demand response event is described. The method may include obtaining a first discharge rate prediction by a cloud-based HVAC control server system. The method may include generating, by the cloud-based HVAC control server system, a demand response event having a start time and an end time based on the first discharge rate prediction. The method may include, prior to the start time, transmitting, over a data network, the generated demand response event from the HVAC control server system to a thermostat located in a structure remote from the HVAC control server system. The method may include storing, by the thermostat, the demand response event in the memory of the thermostat. The method may include, at the start time, controlling the HVAC system in accordance with the generated demand response event by the thermostat. The method may include obtaining, by the cloud-based HVAC control server system, a second discharge rate prediction following the start time and prior to the end time. The method may include generating, by the cloud-based HVAC control server system, a modified demand response event including a modified end time after obtaining the second discharge rate prediction and prior to the end time. The method may include transmitting, by the cloud-based HVAC control server system, the modified demand response event to the thermostat at a time prior to the earlier of the end time and the modified end time. The method may include storing, by the thermostat, the modified demand response event in the memory of the thermostat when the modified demand response event is received by the thermostat. The method may include controlling, by the thermostat, the HVAC system in accordance with the modified EDR event until the modified end time is reached.

[0028] ​Various embodiments of methods for executing emission demand response events are described. In some embodiments, methods for executing emission demand response events are described. The method may include obtaining a history of emission rates by a cloud-based HVAC control server system. The method may include identifying future periods of predicted high emissions based on the history of emission rates by a cloud-based HVAC control server system. The method may include determining the emission demand response event participation level of accounts mapped to thermostats within the future periods of predicted high emissions from a plurality of emission demand response event participation levels by a cloud-based HVAC control server system. The method may include generating emission demand response events within the future periods of predicted high emissions based on the emission demand response event participation levels of accounts by a cloud-based HVAC control server system. The method may include causing thermostats mapped to accounts to control the HVAC system according to the generated emission demand response events by a cloud-based HVAC control server system.

[0029] Embodiments of such a method may include one or more of the following features: Multiple emission demand response event participation levels may include a first participation level and a second participation level; the second participation level may result in a larger amount of emission savings than the first participation level; determining an account's emission demand response event participation level may include outputting a request for selection between the first and second participation levels; determining an account's emission demand response event participation level may further include receiving a selection from the first and second participation levels for the duration of projected high emissions in the future; determining an account's emission demand response event participation level may further include storing instructions for selecting either the first or second participation level for the duration of projected high emissions in the future.

[0030] In some embodiments, a predetermined maximum number of events per day is greater for a second participation level than for a first participation level. Generating emission demand response events may further include determining that the account's emission demand response event participation level may be set to the second participation level. Generating emission demand response events may further include determining that the number of previously generated emission demand response events may be less than a predetermined maximum number of events per day. Causing a thermostat associated with an account to control the HVAC system according to the generated emission demand response events may be at least in part based on the determination that the number of previously generated emission demand response events may be less than a predetermined maximum number of events per day.

[0031] In some embodiments, a predetermined maximum event duration is longer for a second participation level than for a first participation level. Generating an emissions demand response event may further include determining that the account's emissions demand response event participation level may be set to the second participation level. Generating an emissions demand response event may further include increasing the duration of the generated emissions demand response event in response to the determination that the account's emissions demand response event participation level may be set to the second participation level.

[0032] In some embodiments, a thermostat mapped to an account responds to emission demands. Controlling the HVAC system in accordance with a response event includes adjusting the thermostat's set temperature. Generating an emissions demand response event may further include determining that the account's emissions demand response event participation level may be set to a second participation level. Generating an emissions demand response event may further include increasing the adjustment to the thermostat's set temperature in response to the determination that the account's emissions demand response event participation level may be set to a second participation level.

[0033] In some embodiments, causing a thermostat mapped to an account to control the HVAC system in accordance with emission demand response events includes adjusting the thermostat's setpoint temperature. The method may further include receiving an adjustment to the setpoint temperature in the opposite direction after adjusting the setpoint temperature. The method may further include causing the thermostat to stop controlling the HVAC system in accordance with emission demand response events. Causing a thermostat mapped to an account to control the HVAC system in accordance with emission demand response events includes adjusting the thermostat's setpoint temperature. The method may further include receiving an adjustment to the setpoint temperature in the opposite direction after adjusting the setpoint temperature. The method may further include modifying the level of participation in emission demand response events of the account mapped to the thermostat based on the adjustment.

[0034] In some embodiments, modifying the emission demand response event participation level of a thermostat-mapped account includes reducing a predetermined maximum number of events per day. Modifying the emission demand response event participation level of a thermostat-mapped account may include reducing a predetermined maximum event duration. Modifying the emission demand response event participation level of a thermostat-mapped account may include reducing a predetermined maximum setpoint adjustment. The projected future period of high emissions may be one week. The method may further include obtaining a weather forecast for the predetermined future period. Identifying the projected future period of high emissions may further depend on the weather forecast. Generating emission demand response events may further involve determining energy prices within the projected future period of high emissions. The emission demand response event participation level of a thermostat-mapped account may be based on energy prices.

[0035] In several embodiments, a system for executing emission demand response events is described. The system may include a cloud-based power control server system. The cloud-based power control server system may include one or more processors. The cloud-based power control server system may include a memory that is communicatively coupled to one or more processors and is readable by one or more processors, and which stores processor-readable instructions, causing one or more processors to acquire an emission rate history when executed by one or more processors. One or more processors may identify future periods of predicted high emissions based on the emission rate history. One or more processors may determine the emission demand response event participation level of accounts mapped to thermostats during future periods of predicted high emissions from a plurality of emission demand response event participation levels. One or more processors may generate emission demand response events during future periods of predicted high emissions based on the emission demand response event participation levels of accounts. One or more processors may cause thermostats mapped to accounts to control the HVAC system according to the generated emission demand response events.

[0036] Embodiments of such a system may further include a plurality of thermostats, including a thermostat. The system may further include an application running on a mobile device configured to control the thermostats via communication with a cloud-based power control server system. In some embodiments, a plurality of emission demand response event participation levels The configuration includes a first participation level and a second participation level. The second participation level may result in a greater amount of emission savings than the first participation level. A predetermined maximum event duration may be longer for the second participation level than for the first participation level. When executed, a processor-readable instruction causes one or more processors to generate an emission demand response event by further determining that the account's emission demand response event participation level may be set to the second participation level. When executed, a processor-readable instruction causes one or more processors to generate an emission demand response event by further increasing the duration of the generated emission demand response event in response to the determination that the account's emission demand response event participation level may be set to the second participation level.

[0037] In some embodiments, non-transient processor-readable media are described. The media may include processor-readable instructions configured to cause one or more processors to acquire an emission rate history. The media may include processor-readable instructions configured to cause one or more processors to identify future periods of predicted high emissions based on the emission rate history. The media may include processor-readable instructions configured to determine the emission demand response event participation level of an account mapped to a thermostat during the future periods of predicted high emissions from a plurality of emission demand response event participation levels. The media may include processor-readable instructions configured to generate emission demand response events during the future periods of predicted high emissions based on the account's emission demand response event participation level. The media may include processor-readable instructions configured to cause a thermostat mapped to an account to control the HVAC system in accordance with the generated emission demand response events.

[0038] Embodiments of such a medium may include one or more of the following features: Causing a thermostat mapped to an account to control the HVAC system in accordance with emission demand response events may include adjusting the thermostat's setpoint temperature. After adjusting the setpoint temperature, the processor-readable instructions may be further configured to receive adjustments to the setpoint temperature in the opposite direction. The processor-readable instructions may be further configured to modify the emission demand response event participation level of the account mapped to the thermostat based on the adjustments. Modifying the emission demand response event participation level of the account mapped to the thermostat may include reducing a predetermined maximum number of events per day.

[0039] Various embodiments of methods for executing emission demand response events are described. In some embodiments, methods for executing emission demand response events are described. The method may include obtaining emission rate forecasts for a predetermined future period by a cloud-based HVAC control server system. The method may include using the emission rate forecasts by the cloud-based HVAC control server system to identify future emission rate events within the predetermined future period. Future emission rate events may include indications of the predicted magnitude. Future emission rate events may include periods in which the predicted emission rate is at an increased or decreased emission level. The method may include determining confidence values ​​for future emission rate events by the cloud-based HVAC control server system. Confidence values ​​may indicate the certainty that future emission rate events will occur as predicted. The method may include generating emission demand response events with start and end times between future emission rate events, based on the identified future emission rate events and confidence values, by the cloud-based HVAC control server system. The method may include causing a thermostat to control the HVAC system according to the generated emission demand response events by the cloud-based HVAC control server system.

[0040] Embodiments of such methods may include one or more of the following features in the future. The predicted magnitude indication for an emission rate event may include duration and emission differential value. Generating an emission demand response event may further include comparing the predicted magnitude indication for a future emission rate event to a threshold magnitude. Generating an emission demand response event may further include determining that the predicted magnitude indication for a future emission rate event may be greater than the threshold magnitude. Generating an emission demand response event may further include increasing the magnitude of the emission demand response event in response to the determination that the predicted magnitude indication for a future emission rate event may be greater than the threshold magnitude. Increasing the magnitude of the emission demand response event may include increasing the duration of the emission demand response event. Increasing the magnitude of the emission demand response event may include increasing the setpoint temperature offset of the emission demand response event.

[0041] In some embodiments, determining a confidence value for a future emission rate event involves applying a time decay factor to the confidence value based on a time interval between a first time when an emission rate forecast may be received and a second time when the future emission rate event may be predicted to occur. The larger the difference between the first time and the second time, the greater the confidence value may be reduced based on the time decay factor.

[0042] In some embodiments, generating an emissions demand response event further includes comparing a confidence value for a future emission rate event to a minimum confidence value. Generating an emissions demand response event may further include determining that a confidence value for a future emission rate event may be greater than the minimum confidence value. Generating an emissions demand response event may further include increasing the magnitude of the emissions demand response event based on the determination that a confidence value for a future emission rate event may be greater than the minimum confidence value. Generating an emissions demand response event may further include determining an event score for the generated emissions demand response event based on an emissions differential value. Generating an emissions demand response event may further include comparing a confidence value for a future emission rate event to a minimum confidence value. Generating an emissions demand response event may further include determining that a confidence value for a future emission rate event may be greater than the minimum confidence value. Generating an emissions demand response event may further include increasing the event score for the generated emissions demand response event based on the determination that a confidence value for a future emission rate event may be greater than the minimum confidence value.

[0043] In some embodiments, controlling the HVAC system with a thermostat includes adjusting a first hysteresis temperature setpoint and a second hysteresis temperature setpoint of the thermostat. The first hysteresis temperature setpoint may turn the HVAC system on, and the second hysteresis temperature setpoint may turn the HVAC system off. Controlling the HVAC system with a thermostat may include adjusting the thermostat's setpoint temperature by a first amount for a first period shorter than the duration of an emissions demand response event. Controlling the HVAC system with a thermostat may include adjusting the thermostat's setpoint temperature by a second amount smaller than the first amount for the remainder of the emissions demand response event after the first period.

[0044] In some embodiments, generating emission demand response events includes a cloud-based HVAC control server system determining emission rate variability values ​​for a predetermined future period using emission rate forecasts. Generating emission demand response events may include comparing the emission rate variability value to a variability threshold. Generating emission demand response events may include determining that the emission rate variability value is greater than the variability threshold. Generating emission demand response events may include determining a predetermined maximum number of emission demand response events per day in response to the determination that the emission rate variability value may be greater than the variability threshold. This may include increasing the duration of emissions demand response events. Generating emissions demand response events may include reducing a predetermined maximum emissions demand response event duration in response to a decision that the emission rate variability value may be greater than a variability threshold. Generating emissions demand response events may include limiting the generation of emissions demand response events based on a predetermined maximum number of emissions demand response events per day and a predetermined maximum emissions demand response event duration.

[0045] In some embodiments, a system for executing emission demand response events is described. The system may include a cloud-based power control server system. The cloud-based power control server system may include one or more processors. The cloud-based power control server system may include a memory that is communicatively coupled to and readable by one or more processors and stores processor-readable instructions, which, when executed by one or more processors, cause one or more processors to obtain emission rate forecasts for a predetermined future period. One or more processors may use the emission rate forecasts to identify future emission rate events within a predetermined future period. Future emission rate events may include indications of predicted magnitude. Future emission rate events may include periods when the predicted emission rate is at an increased emission level or a decreased emission level. One or more processors may determine confidence values ​​for future emission rate events. Confidence values ​​may indicate the certainty that a future emission rate event will occur as predicted. Based on the identified future emission rate events and confidence values, one or more processors may generate emission demand response events having start and end times between the future emission rate events. One or more processors may cause a thermostat to control the HVAC system in accordance with the generated emission demand response events.

[0046] Embodiments of such a system may further include a plurality of thermostats, including a thermostat. The system may further include an application that runs on a mobile device configured to control the thermostats via communication with a cloud-based power control server system. The system may further include an interface configured to obtain a plurality of emission rate forecasts from an emission data system that is remotely accessible via a network. In some embodiments, the predicted magnitude instruction for a future emission rate event includes duration and emission differential value. Furthermore, a processor-readable instruction that, when executed, generates an emission demand response event may cause one or more processors to compare the predicted magnitude instruction for a future emission rate event with a threshold magnitude. One or more processors may determine that the predicted magnitude instruction for a future emission rate event may be greater than the threshold magnitude. One or more processors may increase the magnitude of the emission demand response event in response to the determination that the predicted magnitude instruction for a future emission rate event may be greater than the threshold magnitude.

[0047] In some embodiments, increasing the magnitude of an emissions demand response event includes increasing the duration of the emissions demand response event. Increasing the magnitude of an emissions demand response event may also include increasing the setpoint temperature offset of the emissions demand response event. Furthermore, when a processor-readable instruction for determining a confidence value for a future emissions rate event is executed, one or more processors may apply a time decay factor to the confidence value based on a time interval between a first time when an emissions rate forecast is received and a second time when the future emissions rate event is predicted to occur. The larger the difference between the first and second times, the greater the confidence value may be reduced based on the time decay factor.

[0048] In some embodiments, a non-temporary processor-readable medium is described. The medium may include processor-readable instructions configured to cause one or more processors to obtain emission rate forecasts for a predetermined future period. The one or more processors obtain emission rate Forecasts may be used to identify future emission rate events within a given future period. Future emission rate events may include indications of their predicted magnitude. Future emission rate events may include periods in which the predicted emission rate is at an increased or decreased emission level. One or more processors may determine confidence values ​​for future emission rate events. Confidence values ​​may indicate the certainty that a future emission rate event will occur as predicted. Based on the identified future emission rate events and confidence values, one or more processors may generate emission demand response events with start and end times between the future emission rate events. One or more processors may cause a thermostat to control the HVAC system according to the generated emission demand response events.

[0049] Embodiments of such a system may include one or more of the following features: Processor-readable instructions for generating emission demand response events may be further configured to cause one or more processors to compare a confidence value for a future emission rate event with a minimum confidence value. One or more processors may determine that the confidence value for a future emission rate event may be greater than the minimum confidence value. One or more processors may increase the magnitude of the emission demand response event based on the determination that the confidence value for a future emission rate event may be greater than the minimum confidence value.

[0050] In some embodiments, processor-readable instructions for generating emissions demand response events are further configured to cause one or more processors to determine an event score for the generated emissions demand response event based on an emissions differential value. One or more processors may compare a confidence value for a future emissions rate event to a minimum confidence value. One or more processors may determine that a confidence value for a future emissions rate event may be greater than the minimum confidence value. Based on the determination that a confidence value for a future emissions rate event may be greater than the minimum confidence value, one or more processors may increment the event score for the generated emissions demand response event.

[0051] In some embodiments, processor-readable instructions for causing a thermostat to control an HVAC system are further configured to cause one or more processors to adjust a first hysteresis temperature setpoint and a second hysteresis temperature setpoint of the thermostat. The first hysteresis temperature setpoint may turn the HVAC system on, and the second hysteresis temperature setpoint may turn the HVAC system off. Processor-readable instructions for causing a thermostat to control an HVAC system are further configured to cause one or more processors to adjust the thermostat's setpoint temperature by a first amount for a first period shorter than the duration of an emissions demand response event. After the first period, one or more processors may adjust the thermostat's setpoint temperature by a second amount less than the first amount for the remainder of the emissions demand response event.

[0052] A further understanding of the properties and advantages of various embodiments may be achieved by referring to the following drawings. In the attached drawings, similar components or features may have the same reference numeral. Furthermore, various components of the same type may be distinguished by the reference numeral being followed by a dash and a second reference numeral for distinguishing similar components. Where only the first reference numeral is used in the specification, the description is applicable to any one of the similar components having the same first reference numeral, regardless of the second reference numeral. [Brief explanation of the drawing]

[0053] [Figure 1] This figure shows an embodiment of a system for managing emission demand response events. [Figure 2] This figure shows an embodiment of a system for managing emission demand response events. [Figure 3] This figure shows an embodiment of a smart thermostat system for managing emission demand response events. [Figure 4] This figure shows a graph of predicted emission data and thermostat set temperature over time. [Figure 5] This graph shows positive emission differential values. [Figure 6] This graph shows negative emission differential values. [Figure 7] This is a graph showing multiple emission differential values. [Figure 8] This is another graph showing the predicted emissions data against the emissions differential value. [Figure 9] This is another graph showing projected emissions data against potential emissions demand response events. [Figure 10] This is another graph of expected emissions data with respect to time constraints. [Figure 11] This is another graph of projected emissions data against previously generated emissions demand response events. [Figure 12] This is a graph of emission demand response events of various sizes and lengths. [Figure 13] This figure shows an embodiment of a method for managing emission demand response events. [Figure 14] This figure shows an embodiment of a method for managing emission demand response events based on event score rankings. [Figure 15] This figure shows an embodiment of a method for managing emission demand response events based on a limited number of permissible events. [Figure 16A] This is a graph of updated emissions forecasts and emissions demand response events sent based on updated emissions forecasts. [Figure 16B] This is a graph of updated emissions forecasts and emissions demand response events sent based on updated emissions forecasts. [Figure 17A] This graph shows updated emissions forecasts and early-shipped emissions demand response events based on changes in the updated emissions forecasts. [Figure 17B] This graph shows updated emissions forecasts and early-shipped emissions demand response events based on changes in the updated emissions forecasts. [Figure 18A] This is a graph of updated emission forecasts with delayed emission demand response events based on updated emission changes. [Figure 18B] This is a graph of updated emission forecasts with delayed emission demand response events based on updated emission changes. [Figure 19A] This is a graph of updated emissions forecasts with constraints on early dispatching of emissions demand response events based on previously dispatched emissions demand response events. [Figure 19B] This is a graph of updated emissions forecasts with constraints on early dispatching of emissions demand response events based on previously dispatched emissions demand response events. [Figure 20A] This is a graph of updated emissions forecasts with constraints on delaying emissions demand response events based on a limited time frame of the day. [Figure 20B]This is a graph of updated emissions forecasts with constraints on delaying emissions demand response events based on a limited time frame of the day. [Figure 21A] This graph shows the updated emissions forecast and the extended end time of dispatched emissions demand response events based on the updated emissions forecast changes. [Figure 21B] This graph shows the updated emissions forecast and the extended end time of dispatched emissions demand response events based on the updated emissions forecast changes. [Figure 22A] This graph shows updated emissions forecasts and early termination emissions demand response events based on changes in updated emissions forecasts. [Figure 22B] This graph shows updated emissions forecasts and early termination emissions demand response events based on changes in updated emissions forecasts. [Figure 23] This figure shows an embodiment of a method for managing emission demand response events based on updated emission forecasts. [Figure 24] This figure shows an embodiment of a method for sending an emissions demand response event immediately before an event based on updated emissions forecasts. [Figure 25] This figure shows an embodiment of a method for correcting emissions demand response events based on updated emissions forecasts. [Figure 26] This is a graph of weather forecasts against historical emission rates for the same period of the year. [Figure 27A] This is a graph of modified event participation levels based on canceled emissions demand response events. [Figure 27B] This is a graph of modified event participation levels based on canceled emissions demand response events. [Figure 28A] This graph shows the adjusted event participation levels based on setpoint adjustments during emissions demand response events. [Figure 28B] This graph shows the adjusted event participation levels based on setpoint adjustments during emissions demand response events. [Figure 29]This figure shows an embodiment of a method for generating emission demand response events based on user account participation levels. [Figure 30] This figure shows an embodiment of a method for correcting user account participation levels based on setpoint adjustments. [Figure 31] This is a graph of emission demand response events based on the magnitude of future emission rate events. [Figure 32] This is another graph of predicted emissions data with decreasing confidence levels. [Figure 33] This is a graph of emission demand response events generated based on confidence levels. [Figure 34] This is a graph showing the end times of multiple emission demand response events based on confidence levels. [Figure 35] This graph shows the emission demand response events in relation to step-by-step adjustments to the set temperature. [Figure 36A] This is a graph of emission demand response events generated based on expected volatility. [Figure 36B] This is a graph of emission demand response events generated based on expected volatility. [Figure 37] This figure shows an embodiment of a method for forming emission demand response events based on the confidence value of the predicted emission rate. [Figure 38] This figure shows an embodiment of a user interface that demonstrates carbon emission savings generated by a user account. [Figure 39] This figure shows an embodiment of a user interface that demonstrates collective carbon emission savings generated by a community. [Figure 40] This figure shows an embodiment of a user interface that demonstrates account settings for managing participation in emission demand response events. [Figure 41A] This figure shows an embodiment of a smart thermostat user interface. [Figure 41B] This figure shows an embodiment of a smart thermostat user interface. [Figure 41C]This figure shows an embodiment of a smart thermostat user interface. [Figure 41D] This figure shows an embodiment of a smart thermostat user interface. [Figure 42] This figure shows an embodiment of a personal device interface for managing EDR events. [Modes for carrying out the invention]

[0054] Detailed explanation Power companies are currently facing the ongoing challenge of consistently meeting electricity demand while reducing overall carbon emissions. Changing consumer demand for electricity, combined with the increasing availability of cleaner electricity, is a challenge to meet consumer demand while maintaining low carbon emissions. This can often make it difficult to maintain a consistent dosage.

[0055] Changes in consumer demand and the availability of cleaner electricity can be attributed to a number of factors. Consumer demand can be driven by factors such as weather, whether consumers are at home or out, the time of day, the day of the week, and the time of year. For example, power companies may experience increased demand during extreme heat or cold snaps or in the evenings when residents return home and increase their electricity consumption. Similarly, the availability of cleaner electricity may depend on factors such as weather, the time of year, and / or the season. For example, the availability of solar power may decrease during storms or during the winter when the days are shorter. Likewise, there may also be seasonal or daily changes in wind patterns that correlate with decreases or increases in the electricity generated by wind turbines.

[0056] If cleaner electricity sources cannot meet demand, power companies may have to rely on sources that tend to produce more pollution, including carbon dioxide. For example, when demand is relatively low, a larger portion of the demand can be met using clean and relatively clean sources such as wind, solar, and hydropower. However, if demand increases and / or if cleaner sources are less available, other, more polluting sources such as diesel generators, coal-fired power plants, and natural gas turbines may have to be utilized.

[0057] Emissions-Demand Response ("EDR") events may be used to reduce electricity consumption when more polluting power sources, which could also be called "dirtier electricity," are in use, thereby reducing pollution. The objective of an EDR event is to reduce the total use of polluting energy and increase the total use of clean energy. An EDR event can achieve this objective by shifting electricity consumption to earlier or later times, coinciding with times when electricity is generated using cleaner energy sources and away from times when electricity is generated using polluting energy sources. For example, an EDR event may attempt to shift the electrical load from times when electricity is generated using oil to times when electricity is generated using wind or solar energy. As another example, in the case of a natural gas and coal power plant, and a grid with minimal carbon-free energy, an EDR event may shift the electrical load from times when coal is used to generate electricity to times when natural gas is used to generate electricity.

[0058] At any given point in time, adjustments to electricity consumption correspond to adjustments in electricity generation by one or more power plants to balance electricity supply with demand. Each of the one or more power plants that generate electricity has its own emission characteristics, which can be measured as the amount of carbon emissions produced per unit of electricity generated. When electricity demand increases, electricity generation, and therefore emissions, may also increase depending on the power source. Similarly, when electricity demand decreases, electricity generation, and therefore emissions, may also decrease depending on the power source. The amount of emissions generated in additional electricity generation is the amount of emissions eliminated by less electricity generation, based on the emission characteristics associated with the power source. The total amount of emissions generated or reduced when the electrical load changes can be expressed by a value called the marginal emission rate ("MER"), which is usually measured by the weight of carbon dioxide per unit of energy consumed or generated, e.g., lbs-CO2 / MWh.

[0059] MER forecasts may be generated to predict MER at various points in the future. By using current and forecast MER data, EDR events may be generated to shift the electrical load from times when electricity consumption produces higher levels of carbon emissions to times when carbon emissions are significantly lower. In some embodiments, the targets are electric cooling (e.g., air conditioning), fan operation, and electric heating systems. The goal is to reduce carbon emissions by shifting electrical loads, including but not limited to HVAC loads. The sum of many small shifts across many structures (e.g., homes, buildings, apartments, offices) can result in a significant change in emissions resulting from electricity use.

[0060] One way to shift the electrical load can be by making adjustments to the user thermostat temperature setpoint. Using current and projected emission rate data, the system can determine when and for how long an adjustment to the user setpoint will achieve an emission reduction. Similarly, knowing whether the emission rate will increase or decrease, the system can determine whether to raise or lower the thermostat setpoint temperature. With projected emission data, the system can generate scheduled events at various points within the time span covered by the projections. However, due to the uncertain nature of the projected data, updated projected and current emission data can be used to periodically or occasionally correct previously generated events, thereby achieving improvements in carbon emission reductions.

[0061] In the past, achieving carbon emission reductions, particularly on an individual basis, could be challenging due to the perceived amount of effort required to reduce one's carbon footprint. Those who might otherwise be reluctant to take proactive steps to reduce their carbon footprint can reduce their emissions with very little effort by allowing automatic adjustments to thermostat settings. However, the perceived amount of discomfort associated with reducing carbon emissions creates an additional barrier to overcome. This is especially true with regard to heating or cooling, as some people may be sensitive even to slight changes in ambient temperature. Similarly, some people may be sensitive to the number of times their thermostat settings are automatically adjusted each day.

[0062] The features described herein advantageously address this sensitivity in many ways. For example, people can have the ability to opt in and / or opt out of various levels of emissions reduction programs at any given time. Furthermore, even when opting in to a program, people can have the ability to make real-time adjustments to the setpoint temperature at any point during the execution of an emissions reduction event, as will be further discussed later. One objective achieved by some of the embodiments is the careful formation of a balance between aggressive thermostat control, which offers a good potential reduction in carbon emissions but may result in more irritation or discomfort and associated real-time setpoint overrides, and less aggressive control, which generally offers more comfort and less irritation and less possibility of real-time setpoint overrides but does not offer a sufficient potential reduction in carbon emissions.

[0063] One way to balance discomfort with a reduction in carbon emissions is to place constraints on the generation, execution, and termination of EDR events. For example, the number of load shift events per day may be limited, or the number of EDR events of a particular type may be limited. Similarly, restricting events to predetermined times during the day, spacing events throughout the day, and / or limiting the aggression of temperature offsets from a predetermined temperature setpoint can reduce the perceived level of discomfort for the user. In more advanced systems, characteristics specific to the user account associated with the thermostat may be used to determine the characteristics of the EDR events. For example, the system may prefer that occupants in a home or building with a thermostat associated with a first account tolerate more frequent events that result in small changes to the setpoint temperature, while occupants in a home or building with a second account prefer more frequent events that result in larger changes to the setpoint temperature. However, it may learn over time that it prefers to tolerate events that result in fewer adjustments. Therefore, by adjusting constraints for each user account or by a thermostat associated with the user account, an increased amount of carbon emission reduction can be achieved while limiting the degree of user discomfort. Further details regarding these and other embodiments are provided in connection with the drawings.

[0064] While the above description focuses on the use of smart thermostats, the embodiments detailed herein can be applied to other smart controllable systems that use significant amounts of electricity whose use can be time-shifted. For example, the consumption of electricity by various appliances such as electric vehicle ("EV") charging stations and smart refrigerators may be shifted from times when energy consumption results in high levels of carbon emissions to times when carbon emissions are lower. As another example, electrical loads from other older or "disconnected" devices may further be shifted using various devices designed to control the amount of electricity flowing to specific devices, such as smart outlets or smart lighting sockets.

[0065] Further details regarding the occurrence and management of EDR events are provided in connection with the drawings. Figure 1 shows an embodiment of a system 100 for managing EDR events. System 100 may include a cloud-based power control server system 110, an emissions data system 120, a network 130, a mobile device 140, a personal computer 150, a smart thermostat 160, an electric vehicle ("EV") charging station 170, and a smart appliance 180. The smart thermostat 160 may be connected to a heating, ventilation, and air conditioning ("HVAC") system 165. The EV charging station 170 may be connected to an electric vehicle 175. In some embodiments, one or more components of system 100 may be communicably connected to other components of system 100 via the network 130.

[0066] The cloud-based power control server system 110 may include one or more processors configured to perform various functions, such as generating and managing EDR events, as will be further described below with reference to Figure 2. The cloud-based power control server system 110 may include one or more physical servers performing one or more processes. The cloud-based power control server system 110 may also include one or more processes distributed across the cloud-based server system. In some embodiments, the cloud-based power control server system 110 is connected to one or all of the other components of the system 100 on the network 130. For example, the cloud-based power control server system 110 may be connected to the emissions data system 120 to receive current and projected emissions data. In some embodiments, the current and projected emissions data is expressed as percentage values ​​representing relative emissions at a given point in time compared to emissions recorded over a period in the past. For example, a value of zero at a particular point in time means that the emissions rate is equal to the minimum emissions rate over the past two weeks, while a value of 100 means that the emissions rate is equal to the maximum emissions rate over the past two weeks. In some embodiments, current and projected emissions data are expressed as MER (e.g., lbs-CO2 / MHh). Projected emissions data may include projected emission rates at regular intervals over a predetermined period into the future. For example, emission rate projections may include projected emission rates at 5-minute intervals over a 24-hour period. The accuracy of projected emission rates or MER data may vary depending on the source and / or how the emission rates are determined. For example, projected emission rates may be generated using a model that accepts multiple inputs having varying degrees of correlation with actual emission rates, such as weather data, publicly available grid demand and / or price data, and historical emission rate data. Alternatively, other projected emission rates may be obtained from utilities and / or grid operators. It may be based directly on the data.

[0067] The data received from the emissions data system 120 can be used by the cloud-based power control server system 110 to generate and manage EDR events. The cloud-based power control server system 110 may be connected to the mobile device 140 and personal computer 150 to send notifications about updates or subsequent EDR events. For example, after generating an EDR event, the cloud-based power control server system 110 may send a notification to the user of the mobile device 140 about a scheduled EDR event for a smart thermostat 160 owned by the user of the mobile device 140. The cloud-based power control server system 110 can also distribute the instructions or details of newly generated EDR events to the smart thermostat 160, the EV charging station 170, and / or the smart appliance 180.

[0068] The emissions data system 120 may be a server system, such as a cloud-based server system, connected via the network 130, and may be capable of performing one or more processes related to collecting and generating emissions rate data. Alternatively, the emissions data system 120 may be a commercial service such as WattTime® or any other similar website or web service having a publicly available application programming interface ("API") that provides such emissions rate data and / or equivalents and / or substitutes, such as a website or web service that provides future estimates of “pollution” per kilowatt-hour, or more generally, future estimates of “undesirable” or “more undesirable” per kilowatt-hour. For example, the emissions data system 120 may issue an API that causes an external system, such as a cloud-based power control server system 110, to connect on the network 130 to send requests for data and receive the requested data in response. The emissions data system 120 may connect to external services to receive data from various sources. For example, the emissions data system 120 may be connected to multiple power companies on the network 130 to receive emissions data corresponding to current and projected emissions generated by power plants owned by power companies that supply electricity to cities or regions. The emissions data system 120 may also be connected to other data sources, such as the National Weather Service, to collect additional data related to generating emission rate forecasts using models or any other appropriate calculations. The emissions data system 120 itself can use all the data it collects, along with historical emission rate data, to generate detailed forecasts of MER projected over a given period into the future.

[0069] Network 130 may include one or more wireless networks, wired networks, public networks, private networks, and / or mesh networks. A home wireless local area network (e.g., a Wi-Fi network) may be part of Network 130. Network 130 may include the Internet. Network 130 may include a mesh network which may include one or more other smart home devices and may be used to enable the smart thermostat 160, EV charging station 170, and smart appliance 180 to communicate with another network, such as a Wi-Fi network. Any of the smart thermostat 160, EV charging station 170, and smart appliance 180 may function as an edge router, which translates communications received from other devices in a relatively low-power mesh network to another form of network, such as a relatively higher-power network, such as a Wi-Fi network.

[0070] The mobile device 140 may be a smartphone, tablet computer, laptop computer, game device, or any other form of computerized device that can communicate with the cloud-based power control server system 110 via the network 130, or directly with any of the thermostat 160, EV charging station 170, and smart appliance 180 (for example, via Bluetooth® or some other device-to-device communication protocol). Similarly, the personal computer 150 may be a laptop computer, desktop computer, or any other computerized device that can communicate with the cloud-based power control server system 110 via the network 130, or directly with any of the smart thermostat 160, EV charging station 170, and smart appliance 180. The user can interact with applications running on the mobile device 140 or personal computer 150 to control or interact with the smart thermostat 160, EV charging station 170, and smart appliance 180. For example, a user of a mobile device 140 or a personal computer 150 can connect to a smart thermostat 160 in the user's home via the network 130, thereby monitoring the status of the smart thermostat 160 or sending heating and cooling commands to the smart thermostat 160, which in turn causes the HVAC system to provide heating or cooling to the user's home. The mobile device 140 may also be connected to a cloud-based power control server system 110 on the network 130. For example, the cloud-based power control server system 110 may send notifications to the user of the mobile device 140 about opportunities to participate in EDR events, or it may send updates about the status of future or ongoing EDR events.Notifications or updates may be in the form of text messages, emails, or notifications via applications.

[0071] The smart thermostat 160 may be a smart thermostat that can be connected to a network 130 and can control an HVAC system 165. The smart thermostat 160 may include one or more processors capable of running special-purpose software stored in the memory of the smart thermostat 160. The smart thermostat 160 may include one or more sensors, such as a temperature sensor or an ambient light sensor. The smart thermostat 160 may also include an electronic display. The electronic display may include a touch sensor that allows a user to interact with the electronic screen. The smart thermostat 160 may be connected to a cloud-based power control server system 110 via the network 130. For example, the smart thermostat 160 may receive commands for EDR events from the cloud-based power control server system 110. The smart thermostat 160 may receive emission rate data from the cloud-based power control server system 110 via the network 130.

[0072] In some embodiments, the smart thermostat 160 may be connected to a mobile device 140 or personal computer 150 via a network 130. For example, the smart thermostat 160 may receive heating or cooling commands from the user's mobile device 140 or personal computer 150. In some embodiments, the smart thermostat 160 collectively corrects ERD events and / or future EDR events. For example, the smart thermostat 160 may receive inputs such as setpoint temperature adjustments in thermostats that are generating ongoing EDR events to be corrected. As another example, the smart thermostat 160 may be connected to a smart thermostat The tatt 160 may receive one or more commands from the mobile device 140, resulting in the tatt 160 no longer being involved in and / or generating future EDR events. As an alternative example, the smart thermostat 160 may receive one or more commands. The smart thermostat 160 may be connected to an HVAC system 165, which may cause the HVAC system 165 to provide heating or cooling until a setpoint temperature measured in the smart thermostat 160 is achieved. The HVAC system 165 may be any type of HVAC, such as an electric water heater connected to a hot water circulating baseboard, an electric baseboard, or a fan unit of a forced air system.

[0073] The EV charging station 170 may be a charging system capable of charging one or more electric vehicles 175. The EV charging station 170 may be connected to a cloud-based power control server system via a network 130. For example, the EV charging station 170 may receive commands for EDR events from the cloud-based power control server system 110. The EV charging station 170 may receive emission rate data from the cloud-based power control server system 110 via the network 130. In some embodiments, the EV charging station 170 may be connected to a mobile device 140 or personal computer 150 via the network 130. For example, the EV charging station 170 may send notifications or updates to the user's mobile device 140 or personal computer 150 regarding the charging status of the user's electric vehicle 175. Similarly, the smart appliance 180 may be any appliance connected to the network 130 that can modify electricity consumption by the smart appliance or a device connected to the smart appliance 180.

[0074] Figure 2 shows an embodiment of system 200 for managing EDR events. System 200 may include a cloud-based power control server system 110, an emissions data system 120, a network 130, a mobile device 140, a smart thermostat 160, and an HVAC system 165. The emissions data system 120 may function as detailed above with respect to Figure 1. The smart thermostat 160 may function as detailed above with respect to Figure 1. The HVAC system 165 may function as detailed above with respect to Figure 1. The network 130 may function as detailed above with respect to Figure 1.

[0075] The cloud-based power control server system 110 may include multiple services such as an API engine 211, a communication interface 212, an event scheduler 213, a constraint engine 214, a history data engine 215, a user management module 216, and a forecast engine 217. The cloud-based power control server system 110 may also include one or more databases, such as an emissions rate database 218. The cloud-based power control server system 110 may also include a processing system 219 that can coordinate the execution of various functions provided by the multiple services and communicate with one or more databases, such as the emissions rate database 218.

[0076] The API engine 211 may implement interfaces issued by one or more external systems. These issued interfaces may cause the cloud-based power control server system 110 to interact with various external systems to request and exchange data. The API engine 211 may also cause the cloud-based power control server system 110 to communicate with various devices connected to the network 130. For example, the API engine 211 may implement an interface for sending text messages, emails, or application notifications to a mobile device 140. The API engine 211 may also cause the cloud-based power control server system 110 to send commands to smart devices connected to the network 130 to execute EDR events. For example, API Engine 211 may implement an interface for the smart thermostat 160.

[0077] The communication interface 212 may be used to communicate with one or more wired networks. In some embodiments, the wired network interface may be present to allow communication with a local area network (LAN), for example. The communication interface 212 may be used to communicate with services distributed across multiple virtual machines via a virtual network. The communication interface 212 may be used by one or more of the other processes to communicate with other processes or external devices and services such as the mobile device 140, the emission data system 120, or the smart thermostat 160.

[0078] The event scheduler 213 may implement business logic for scheduling EDR events. For example, the event scheduler 213 may request and receive data from the constraint engine 214, the historical data engine 215, and the forecast engine 217 to determine when to schedule an EDR event to result in a reduction in carbon emissions. The event scheduler 213 may receive emission rate forecasts for future periods from the emissions data system 120. In some embodiments, the event scheduler 213 may use emission rate forecasts to identify emission rate events. Future emission rate events may be any future period when emission rates are expected to be at increased or decreased levels, as will be further described later herein. In some embodiments, the event scheduler 213 may use emission rate forecasts to calculate one or more emission differential values. Emission differential values ​​may be understood as the rate of change in carbon emissions at any given point in time. For example, using emission rate forecasts, the event scheduler 213 may calculate emission differential values ​​for each of several point in time during a future period covered by the forecast. In some embodiments, the event scheduler 213 determines event scores for EDR events ending at each of the multiple point in time. Based on the emission differential values ​​and event scores, the event scheduler 213 may generate and schedule EDR events to be sent to the smart thermostat 160 or any other smart appliance. The event scheduler 213 may modify or cancel previously generated and scheduled EDR events based on updated emission rate forecasts. In some embodiments, constraints imposed by the constraint engine 214 may limit the generation of EDR events by the event scheduler 213.

[0079] The constraint engine 214 may generate and maintain one or more constraints intended to ensure that EDR events scheduled by the event scheduler 213 produce the least amount of user discomfort and irritation. For example, the constraint engine 214 may limit the number of events scheduled for a day. In some embodiments, the constraint engine 214 may limit the number of events of a particular type per day. The constraint engine 214 may limit the generation of events during constrained times of the day. For example, the constraint engine 214 may limit the generation of EDR events when the user is likely to be sleeping or relaxing. In some embodiments, the constraint engine 214 defines a minimum score required for any EDR event scheduled by the event scheduler 213. The constraint engine 214 may define a minimum amount of time between scheduled EDR events. For example, the constraint engine 214 may require a minimum amount of time between the end of one event and the start of the next event of the same or different type. In some embodiments, the constraint engine 214 requests user account identification data from the user management module 216 to define user account specific constraints. For example, the user management module 216 requests user account identification data from the user management module 216 to define user account specific constraints. It may be indicated that the user account always cancels EDR events of a predetermined size, in which case the constraint engine 214 may define constraints for a particular user account that restrict the event scheduler 213 from scheduling events for that user account that are larger than the user account has indicated it is willing to allow.

[0080] The historical data engine 215 may include processes for analyzing historical data and metrics. For example, the historical data engine 215 may periodically or occasionally analyze historical emission rates to help predict when emission rates will rise or fall again in the future. The historical data engine 215 may analyze historical data collected from various user devices. For example, the historical data engine 215 may record and store the effectiveness of HVAC systems associated with user accounts. The effectiveness itself may be used by the event scheduler 213 to identify the optimal EDR event for the user account based on the effectiveness of the HVAC systems. In some embodiments, the data analyzed by the historical data engine 215 is stored in one or more databases of the cloud-based power control server system 110, such as an emission rate database.

[0081] The user management module 216 may include one or more processes for managing user accounts. For example, the user management module 216 may access, modify, and store account details for a particular user account, such as information for one or more devices owned and operated by the user associated with the account, various settings and their extent for programs the user account may be involved in, payment methods, setting temperature preferences, or user account habits. The user management module 216 may provide user account-specific information to the constraint engine 214 to generate user account-specific constraints and limits. The user management module 216 may provide user account-specific information to the event scheduler 213 to help determine which events to schedule and when, based on preferences associated with the user account. In some embodiments, the user management module 216 may send communications to the user associated with the user account, such as notifications or updates, or communications to applications on the mobile device 140 associated with the user account. For example, the user management module 216 may send emails, texts, or application invitations to a particular user account to participate in future EDR program events.

[0082] The forecasting engine 217 may include one or more processes for analyzing, modifying, or generating emission rate forecasts. The forecasting engine 217 may receive emission rate forecasts from the emission data system 120 or the emission rate database 218. In some embodiments, the forecasting engine 217 modifies the received emission rate forecasts using data generated by the historical data engine 215 or other historical data from one or more databases, such as the emission rate database 218. For example, after receiving emission rate forecasts from the emission data system 120, the forecasting engine 217 may modify the forecasts based on a combination of weather forecasts and historical emission rates for similar weather conditions. The forecasting engine 217 may use the combination of historical emission rates to generate independent emission rate forecasts. In some embodiments, the forecasting engine 217 analyzes the emission rate forecasts and determines emission differential values ​​that the event scheduler 213 can use to generate EDR events.

[0083] One or more databases, such as the emission rate database 218, may store data in the cloud-based power control server system 110 or otherwise make the data accessible to the cloud-based power control server system 110. The emission rate database 218 may include data related to historical and predicted emission rates. Historical emission rate data includes recorded emission rates measured by a power company or third-party service for a city or region. This may include both recorded and older forecasts covering the recorded period. For example, if the emission rate database 218 stores recorded and older forecasts, the historical data engine 215 may analyze these sets of data to determine the accuracy of future forecasts. The predicted emission rates may be one or more emission rate forecasts covering the same or overlapping periods. By maintaining multiple emission rate forecasts covering the same or overlapping periods, the historical data engine 215 or any other analysis process may compare the forecasts and determine trends in the forecasts as they approach in real time. For example, a first forecast may predict a high emission rate in the 24 hours into the forecast. A later forecast (e.g., 12 hours later) may correct the forecast, showing that the emission rate at the same point in time (e.g., now 12 hours into the forecast) will not be as high. Once this trend is identified across a sufficient number of emission rate forecasts, the forecast engine 217 may revise future forecasts to more accurately predict future emission rates. The cloud-based power control server system 110 may include other databases for various purposes. For example, there may be a user database that stores specific information for individual user accounts, such as account details, program involvement settings, HVAC system characteristics, and setpoint temperature preferences. One or more databases, including the emissions rate database 218, may be implemented by one or more suitable database structures, such as relational databases (e.g., SQL) or non-SQL databases (e.g., MongoDB).

[0084] The processing system 219 may include one or more processors. The processing system 219 may include one or more special-purpose or general-purpose processors. Such a special-purpose processor may include a processor specifically designed to perform the functions detailed herein. Such a special-purpose processor may be an ASIC or FPGA, which is a general-purpose component physically and electrically configured to perform the functions detailed herein. Such a general-purpose processor may run special-purpose software stored using one or more non-temporary processor-readable media, such as random-access memory (RAM), flash memory, hard disk drives (HDDs), or solid-state drives (SSDs) of the cloud-based power control server system 110.

[0085] Figure 3 shows an embodiment of a smart thermostat system 300 for managing EDR events. The smart thermostat system 300 may include a smart thermostat 160, a network 130, a cloud-based server system 110, and a backplate 360. The cloud-based server system 110 may function as described above in relation to Figures 1 and 2. The network 130 may function as described above in relation to Figure 1. The emissions data system 120 may be connected to the cloud-based server system 110 and may function as described above in relation to Figure 1. The smart thermostat 160 may include an electronic display 311, a touch sensor 312, a network interface 313, an event scheduler 314, a constraint engine 315, an ambient light sensor 316, a temperature sensor 317, an HVAC interface 318, a housing 321, and a cover 322.

[0086] The electronic display 311 may be visible through the cover 322. In some embodiments, the electronic display 311 is only visible when it is illuminated. In some embodiments, the electronic display 311 is not a touchscreen. The touch sensor 312 may be capable of detecting one or more gestures, including tap and swipe gestures. The touch sensor 312 may be a capacitive sensor comprising multiple electrodes. In some embodiments, the touch sensor 312 is a touch strip comprising five or more electrodes.

[0087] The network interface 313 may be used to communicate with one or more wired or wireless networks. The network interface 313 may communicate with a wireless local area network such as a Wi-Fi network. Additional or alternative network interfaces may also be present. For example, the smart thermostat 160 may communicate directly with user devices by using Bluetooth® or the like. The smart thermostat 160 may communicate with various other home automation devices via a mesh network. The mesh network may use relatively less power compared to wireless local area network-based communication such as Wi-Fi. In some embodiments, the smart thermostat 160 can act as an edge router that translates communication between the mesh network and wireless networks such as Wi-Fi networks. In some embodiments, a wired network interface may be present, for example, to enable communication with a local area network (LAN). One or more direct wireless communication interfaces may also be present, for example, to enable direct communication with remote temperature sensors installed in separate different housings outside of the housing 321. The evolution of wireless communication to fifth-generation (5G) and sixth-generation (6G) standards and technologies provides greater throughput with lower latency, which improves mobile broadband services. 5G and 6G technologies also offer a new class of services on control and data channels for vehicle-to-vehicle (V2X), fixed wireless broadband, and the Internet of Things (IoT). The smart thermostat 160 may include one or more wireless interfaces that can communicate using 5G and / or 6G networks.

[0088] The event scheduler 314 may implement business logic for executing EDR events. For example, the event scheduler 314 may receive information related to EDR events generated by the cloud-based server system 110 for the smart thermostat 160. The event scheduler 314 may then translate the information into instructions to be executed at the appropriate time for the EDR events. In some embodiments, the event scheduler 314 generates and schedules EDR events from emission rate forecast data. For example, the event scheduler 314 may request and receive emission rate forecasts from the cloud-based server system 110 to determine when to schedule EDR events to result in a reduction in carbon emissions. Using the emission rate forecasts, the event scheduler 314 may identify future emission rate events. Future emission rate events may be any future period when emission rates are predicted to be at increased or decreased levels, as further described below herein. In some embodiments, the event scheduler 213 uses emission rate forecasts to calculate emission differential values ​​for each of several point in time during a future period covered by the forecasts. In some embodiments, the event scheduler 314 determines event scores for EDR events ending at each of the several point in time. Based on the emission differential values ​​and event scores, the event scheduler 314 may generate and schedule subsequent EDR events. The event scheduler 314 may modify or cancel previously generated and scheduled EDR events based on updated emission rate forecasts. In some embodiments, constraints imposed by the constraint engine 315 limit the generation of EDR events by the event scheduler 314.

[0089] The constraint engine 315 may generate and maintain one or more constraints intended to ensure that EDR events scheduled by the event scheduler 314 produce the least amount of user discomfort and frustration. For example, the constraint engine 315 may limit the number of events scheduled per day. In some embodiments... The constraint engine 315 also limits the number of events of a particular type per day. The constraint engine 315 may limit the generation of events during limited hours of the day. For example, the constraint engine 315 may limit the generation of EDR events when the user is generally sleeping or relaxing. In some embodiments, the constraint engine 315 defines a minimum score required for any EDR event scheduled by the event scheduler 314. The constraint engine 315 may also define a minimum amount of time between scheduled EDR events, or more specifically, between EDR events of a certain type. For example, the constraint engine 315 may require a minimum amount of time between the end of one event and the start of the next event of the same or different type. In some embodiments, the constraint engine 315 defines specific constraints on the user account of the smart thermostat 160. For example, the smart thermostat 160 may record details of an overridden EDR event each time a person overrides an EDR event. The constraint engine 315 may then use this information to define specific constraints that limit the generation of future EDR events that match the details of previously overridden EDR events.

[0090] The ambient light sensor 316 may sense the amount of light present in the environment of the smart thermostat 160. The measurement taken by the ambient light sensor 316 may be used to adjust the brightness of the electronic display 311. In some embodiments, the ambient light sensor 316 senses the amount of ambient light through the cover 322. Therefore, the reflectivity of the cover 322 may be compensated so that the ambient light level is accurately determined via the ambient light sensor 316. In a particular area of ​​the cover 322, a light pipe may be present between the ambient light sensor 316 and the cover 322 so that light transmitted through the cover 322 is directed towards the ambient light sensor 316, which may be mounted on a printed circuit board (PCB), such as a PCB, to which the processing system 319 is attached.

[0091] One or more temperature sensors, such as temperature sensor 317, may be present within the smart thermostat 160. Temperature sensor 317 may be used to measure ambient temperature in the environment of the smart thermostat 160. One or more additional temperature sensors, such as remote temperature sensors 320, located away from the smart thermostat 160, may be used additionally or alternatively to measure ambient temperature. For example, one or more remote temperature sensors 320, located throughout a home or building, may be connected to the smart thermostat 160 to provide a more accurate representation of ambient temperature throughout the home or building.

[0092] The cover 322 may have sufficient transmittance so that the illuminated portion of the electronic display 311 can be seen by the user from outside the smart thermostat 160 through the cover 322. The cover 322 may have sufficient reflectivity so that the portion of the cover 322 that is not illuminated from behind appears to have a mirror-like effect to a user looking at the front of the thermostat 310.

[0093] The HVAC interface 318 may include one or more interfaces that control whether a circuit is complete with various HVAC control wires connected directly to the thermostat 310 or to the backplate 360. Heating systems (e.g., furnaces, heat pumps), cooling systems (e.g., air conditioners), and / or fans may be controlled via HVAC wires by opening and closing circuits containing HVAC control wires. The HVAC interface 318 may also be in the form of a wireless interface that controls separate electronic units communicating with the HVAC system via HVAC wires. In some embodiments, the HVAC interface 318 implements one or more communication protocols. For example, HVAC interface 31 8 may use the Owner Serial Communication Protocol over the wire as specified by the HVAC system manufacturer. As another example, the HVAC interface 318 may wirelessly communicate with an HVAC system that supports Thread®, Zigbee®, CHIP / Matter®, or any other suitable wireless communication protocol.

[0094] The processing system 319 may include one or more processors. The processing system 319 may include one or more special-purpose or general-purpose processors. Such a special-purpose processor may include a processor specifically designed to perform the functions detailed herein. Such a special-purpose processor may be an ASIC or FPGA, which is a general-purpose component physically and electrically configured to perform the functions detailed herein. Such a general-purpose processor may run special-purpose software stored using one or more non-temporary processor-readable media, such as random-access memory (RAM), flash memory, hard disk drives (HDDs), or the solid-state drives (SSDs) of the smart thermostat 160.

[0095] The processing system 319 may output information for display on the electronic display 311. The processing system 319 can receive information from the touch sensor 312, the ambient light sensor 316, and the temperature sensor 317. The processing system 319 can communicate bidirectionally with the network interface 313. The processing system 319 can control the HVAC system via the HVAC interface 318. In some embodiments, the processing system 319 executes one or more software applications or services stored in or otherwise accessible by the smart thermostat 160. For example, one or more components of the smart thermostat 160, such as the event scheduler 314 and the constraint engine 315, may include one or more software applications or software services that may be executed by the processing system 319.

[0096] The cloud-based server system 110 can maintain user accounts mapped to the smart thermostat 160. The smart thermostat 160 may communicate periodically or intermittently with the cloud-based server system 110 to determine when an EDR event is scheduled or when to adjust the settings in accordance with an EDR event. A person may communicate with the cloud-based server system 110 via a mobile device, smartphone, tablet computer, laptop computer, desktop computer, or network 130, or communicate directly with the thermostat 310, or interact with the thermostat 310 via a computerized device 350, which may be another form of computerized device (e.g., via Bluetooth® or other device-to-device communication protocol). A person may interact with applications running on the computerized device 350 to control or interact with the thermostat 310.

[0097] Figure 4 shows graph 400 of predicted emission data and thermostat setpoint temperature over time. Graph 400 shows the predicted emission rate 416 over time. The left vertical axis 402 shows the emission rate in lbs-CO2 / MWh. However, any similar unit of measurement for the emission rate may be used. The horizontal axis 404 shows time in hours, but any unit of time may be used to provide the desired level of granularity. Graph 400 also shows the thermostat setpoint temperature 420 over time. The right vertical axis 408 shows the measured temperature The temperatures are shown in Fahrenheit, but any similar unit of temperature measurement may be used. As shown in Graph 400, the projected emission rate 416 changes over time, including periods of sustained low-carbon emissions and other periods of sustained high-carbon emissions.

[0098] In some embodiments, the normal operation of the thermostat involves adjusting the setpoint temperature at various points throughout the day according to a pre-programmed and / or pre-defined schedule. For example, referring to Graph 400, the thermostat may include a defined schedule during the hotter periods of the year, in which case the setpoint temperature is automatically adjusted to 68 degrees at night when the occupant may be sleeping and raised to 72 degrees during the day when the occupant may be out before gradually lowering the setpoint temperature again when the occupant may be returning home. In some embodiments, an EDR event represents a deviation from the pre-defined schedule and is executed as a load shift event. Graph 400 shows a potential load shift event when a deviation from the setpoint temperature schedule for a time interval can achieve a net reduction in overall carbon emissions.

[0099] These potential load shift events are represented, for example, as deviations or adjustments from a setpoint temperature of 420 when the HVAC system is in cooling mode (e.g., controlling an air conditioner). In another example, when the HVAC system is in heating mode (e.g., controlling a heating unit), the deviation or adjustment from the setpoint temperature of 420 may be in the opposite direction. There may be two types of load shift or EDR events, namely preemptive events and deferred events. Each type of event may reduce overall carbon emissions by shifting electricity use from times when electricity consumption results in relatively high levels of carbon emissions to times when carbon emissions are relatively low. Preemptive events may reduce carbon emissions by increasing the electrical load during low-carbon emission times, thereby decreasing the electrical load during times when electricity consumption results in high levels of carbon emissions. Deferred events may achieve a reduction in carbon emissions by reducing the electrical load during high-carbon emission times until carbon emissions are significantly lower.

[0100] During the time the HVAC system is in cooling mode (e.g., controlling the air conditioner), load shift events may be described as preemptive cooling events and deferred cooling events. During a preemptive cooling event, the temperature setpoint may be lowered to increase the likelihood that air conditioning will occur during the event rather than after it has ended. If an increase in emissions is expected, a preemptive cooling event may be scheduled for a period prior to the increase in emissions to shift the electrical load from a time of increased emissions to a time of lower emissions. For example, as shown in Graph 400, the expected emissions rate 416 is expected to be relatively low in the period prior to 9:00 before it increases relatively high at 9:00. Therefore, a preemptive cooling event 424 may be scheduled during the period prior to 9:00 and set to end when the expected emissions rate 416 increases at 9:00. By lowering the setpoint temperature during the preemptive cooling event 424, the HVAC system may lower the ambient temperature in the controlled environment to a level lower than the original setpoint temperature 420. After the preemptive cooling event 424 has ended, the HVAC system may require less electricity than when the temperature in the controlled environment slowly rises to match the setpoint temperature 420. In this way, the HVAC system can consume cleaner electricity during periods of low carbon emissions and less polluting electricity during periods of high carbon emissions.

[0101] On the other hand, during a deferred cooling event, the temperature setpoint is raised, increasing the likelihood that air conditioning will occur after the event has ended rather than before. If a decrease in emissions is expected, a deferred cooling event is scheduled against the period before the decrease in emissions in order to shift the electrical load from the time of increased emissions to the time of lower emissions. For example, as shown in Graph 400, the predicted emission rate 416 is expected to be relatively high in the period starting at 11:00 before decreasing in the period before 12:00. Therefore, the deferred cooling event 428 may be scheduled for the period starting at 11:00 and set to end when the predicted emission rate 416 decreases as 12:00 approaches. By raising the setpoint temperature during the deferred cooling event 428, the HVAC system can use less electricity because the temperature in the controlled environment rises slowly to match the adjusted setpoint temperature. After the deferred cooling event 428 has ended, the HVAC system may then use additional electricity to restore the ambient temperature in the controlled environment to the original setpoint temperature 420. In this way, the HVAC system can consume less electricity during periods of higher carbon emissions and more electricity during periods of lower carbon emissions.

[0102] Similarly, load shift events during the time the HVAC system is in heating mode (e.g., controlling heating units) may be described as preemptive heating events and deferred heating events. As will be readily apparent to those skilled in the art, this teaching relating to preemptive and deferred heating events, as applied in relation to emissions demand response, is applicable to structures where the underlying power source for heating is electrical (e.g., resistance heating, heat pump, electric radiant heating, etc.) rather than non-electric (e.g., natural gas, oil, etc.). A preemptive heating event may involve raising the setpoint temperature 420 to increase the likelihood that the heater will operate before the end of the event rather than after. If an increase in emissions rates is expected, a preemptive heating event may be scheduled in the period prior to the increase in emissions rates to shift the electrical load from a time of increased emissions to a time of lower emissions. For example, referring to Graph 400, instead of lowering the setpoint temperature 420 for a preemptive cooling event 424, the setpoint temperature is raised for a preemptive heating event. Similarly, a deferred heating event may involve lowering the setpoint temperature 420, increasing the likelihood that the heater will operate after the event has ended rather than before. If a decrease in emissions is expected, a deferred heating event may be scheduled in the period prior to the decrease in emissions to shift the electrical load from periods of increased emissions to periods of lower emissions. For example, referring to Graph 400, instead of increasing the setpoint temperature 420 for a deferred cooling event 428, the setpoint temperature is decreased for a deferred heating event.

[0103] In some embodiments, there is a preconditioning period before a load shift event. The preconditioning period may be the period before the start of a load shift event when the setpoint temperature is adjusted in the opposite direction to the setpoint schedule as a future load shift event. For example, in the case of a preemptive cooling event in which the setpoint temperature is lowered relative to the setpoint schedule, the preconditioning period may involve raising the setpoint temperature relative to the setpoint schedule during the period before the start of the preemptive cooling event. In some embodiments, the preconditioning period is triggered immediately before the load shift event. In other embodiments, there is an interval of 5 minutes, 10 minutes, 15 minutes, or similarly appropriate amounts of time between the preconditioning period and the load shift event. By raising the setpoint temperature relative to the setpoint schedule before lowering the setpoint temperature relative to the setpoint schedule, the HVAC system may be less likely to operate before the event, thereby shifting an additional electrical load from before the event to the period between the preemptive cooling event and the event.

[0104] In some embodiments, there is a post-conditioning period following a load shift event. The post-conditioning period may be the period after the end of a load shift event in which the setpoint temperature is adjusted in the opposite direction to the setpoint schedule, which was the load shift event that just ended. For example, the setpoint temperature rises relative to the setpoint schedule. In the case of a preemptive heating event, the postconditioning period may involve lowering the setpoint temperature relative to the setpoint schedule during the period following the end of the preemptive heating event. In some embodiments, the postconditioning period is triggered immediately after a load shift event. In other embodiments, there is an interval between the load shift event and the postconditioning period, such as 5 minutes, 10 minutes, 15 minutes, or a similarly appropriate amount of time. By raising the setpoint temperature and then lowering it, the HVAC system may be less likely to operate after the event, thereby shifting the additional electrical load from the period after the event towards the period during the preemptive heating event. In some embodiments, in addition to having a postconditioning period after a load shift event, there is a preconditioning period before the load shift event.

[0105] In some embodiments, preconditioning and / or postconditioning periods are achieved by scheduling pre-emptive and deferred events in close proximity. For example, by scheduling a pre-emptive cooling event to end simultaneously with the start of a deferred cooling event, the pre-emptive cooling event may perform the function of preconditioning the pre-emptive cooling event. As another example, by scheduling a pre-emptive heating event to end simultaneously with the start of a deferred heating event, the deferred heating event may perform the function of a postconditioning event.

[0106] As shown in Graph 400, the predicted emission rate 416 can rise or fall sharply at several points over time. Various metrics may be used to quantify emission savings potential at any given time to optimize the scheduling and occurrence of load shift events. In some embodiments, the emission differential value may be used to quantify emission savings potential. The emission differential value may be understood as the rate of change of carbon emissions at any given point in time. The larger the emission differential value at a given point in time (e.g., more positive), the more emissions can be avoided by shifting the load from after that point in time to before that point in time. This can be achieved, for example, by scheduling a preemptive heating or cooling event that ends at that point in time. Similarly, the smaller the emission differential value at a given point in time (e.g., more negative), the more emissions can be avoided by shifting the load from before that point in time to after that point in time. This can be achieved, for example, by scheduling a delayed heating or cooling event that ends at that point in time.

[0107] One method for calculating the emissions differential value at a given point in time may be to evaluate the predicted emission rate 416 over the course of the emissions differential range surrounding that point in time. For example, to calculate the emissions differential value at time t, the emission rate over a one-hour emission range including 30 minutes before and after time t may be analyzed. The emissions differential at time t may be calculated by subtracting the average emission rate over the 30 minutes ending at time t from the average emission rate over the 30 minutes starting at time t. Although a one-hour emissions differential range is used as an example, it should be understood that any amount of time before and after time t may be analyzed to determine the emissions differential value at time t. The calculation of emissions differential values ​​and their application in generating EDR events will be explained in more detail with respect to Figures 5 to 9.

[0108] Figure 5 shows graph 500, which represents the positive emission differential value. Graph 500 shows the predicted emission rate 512 over time. Graph 500 represents the same x-axis 504 and y-axis 502 as graph 400 described above with respect to Figure 4. The emission differential value at time t is calculated from the average emission rate over the period starting at time t to the period ending at time t. It may be calculated by subtracting the average emission rate. Graph 500 shows the positive emission differential value 516 at 11:00. The emission differential value 516 may be calculated by evaluating the emission rate over the emission differential range. In this example, the emission differential range spans two hours, starting at 10:00 and ending at 12:00. In this example, the average starting emission rate 514 from 10:00 to 11:00 is 200, because the predicted emission rate 512 from 10:00 to 10:30 is 0, and the predicted emission rate 512 from 10:30 to 11:00 is 400. In this example, the average ending emission rate 518 from 11:00 to 12:00 is 1000. This is because the predicted emission rate 512 from 11:00 to 11:30 is 800, and the predicted emission rate 512 from 11:30 to 12:00 is 1200. Therefore, in this example, the emission differential value 516 may be calculated by subtracting the average starting emission rate 514 from the average ending emission rate 518 so as to reach a positive emission differential value of 800 516.

[0109] Figure 6 shows graph 600, which represents a negative emissions differential value. Graph 600 shows the predicted emissions rate 612 with respect to time. Graph 600 represents the same x-axis 604 and y-axis 602 as graph 400 described above with respect to Figure 4. Graph 600 shows a negative emissions differential value 616 at 11:00. Similar calculations may be performed to determine the emissions differential value 616, as described above with respect to Figure 5. In this example, the emissions differential range is two hours, starting at 10:00 and ending at 12:00. The average starting emissions rate 614 from 10:00 to 11:00 in this example is 1000, because the predicted emissions rate 612 from 10:00 to 10:30 is 1200, and the predicted emissions rate 612 from 10:30 to 11:00 is 800. In this example, the average end emission rate 618 from 11:00 to 12:00 is 200. This is because the predicted emission rate 612 from 11:00 to 11:30 is 400, and the predicted emission rate 612 from 11:30 to 12:00 is 0. Therefore, in this example, the emission differential value 616 may be calculated by subtracting the average start emission rate 614 from the average end emission rate 618 so that it reaches a negative emission differential value of -800, 616.

[0110] Figure 7 shows a graph 700 of multiple emission differential values. Graph 700 represents the same x-axis 704 and y-axis 702 as graph 400 described above with respect to Figure 4. Graph 700 shows that multiple emission differential values ​​736, 740, and 744 may be calculated using shorter emission differential ranges. For example, the emission differential value 736 may be calculated using a one-hour range by subtracting the average starting emission rate 712 from the average ending emission rate 720 over the emission differential range from 10:00 to 11:00.

[0111] While various time lengths are used for emission differential ranges in this specification, it should be understood that any appropriate amount of time may be used to evaluate the emission differential value at a given point in time. For example, by using an emission differential range that gradually shortens, the emission differential value may more accurately reflect the predicted rate of change in carbon emissions at a given point in time. On the other hand, by using an emission differential range that gradually lengthens, the emission differential value may more accurately reflect the rate of change in carbon emissions over a longer period of time.

[0112] Generally speaking, when a progressively shorter differential range is used, the emission differential value responds more quickly to changes in emission rate, but it may become more susceptible to noise in the emission rate, potentially leading to oversensitivity, overcontrol, and / or an excessively large number of EDR events. A progressively longer differential range When used, the emission differential value becomes less responsive to changes in emission rates, but less susceptible to noise in emission rates, which can lead to undersensitivity, undercontrollability, and / or an insufficiently small number of EDR events.

[0113] In some embodiments, the length of the emission differential range may be based on the ideal length of the proposed EDR event. For example, if an EDR event is generally scheduled to last 30 minutes, the emission differential range may be one hour. This correlation allows the system to better assess the expected average emission rate over the entire EDR event. In some embodiments, multiple emission differential ranges of varying lengths may be used to assess emission differential values ​​for the same time point, thereby resulting in multiple emission differential values ​​for that time point. These in themselves may be used to determine the optimal length of the EDR event ending at that time. For example, if the forecast predicts a decrease in emission rate that lasts only 30 minutes and ends at time t, an emission differential range of one hour in length might identify these 30 minutes of low emissions as the optimal time for the EDR event, whereas an emission differential range of two hours in length might not.

[0114] Figure 8 shows another graph 800 of the predicted emission data with respect to the emission differential value. Graph 800 represents the same x-axis 804 and y-axis 802 as graph 400 described above with respect to Figure 4. Graph 800 shows the predicted emission rate 816 over a given period. Graph 800 also shows the calculation of emission differential values ​​836, 840, and 844 at various points in time. For example, the emission differential value 836 is determined by subtracting the average starting emission rate 812 from the average ending emission rate 814. Graph 800 shows that the emission differential value can be positive or negative at various points in time. For example, the emission differential value 836 is positive, but the emission differential value 844 is negative because the average starting emission rate 828 is greater than the average ending emission rate 832. Graph 800 also shows that for a given period, the emission differential value may be greater or less than other emission differential values. For example, an emissions differential of 836 is greater than an emissions differential of 840. This is because the difference between average emission rates of 812 and 814 is greater than the difference between average emission rates of 820 and 824.

[0115] As illustrated in Figures 5-8, emission differential values ​​may be calculated for any point in time using emission differential ranges of any length. This can result in a large set of emission differential values ​​for a number of time points over the expected period of emission data. On the other hand, this can create many possibilities for scheduling EDR events. For example, preemptive or deferred events can be scheduled to terminate each time an emission differential value is calculated.

[0116] In some embodiments, the system may assign an event score to each potential preemptive and deferred event based on the emissions differential value at the end of the event. The event scores may then be used to rank each of the potential events and select the best event to produce the maximum amount of carbon emission savings. For preemptive events, the event score may be equal to the emissions differential value at the end of the event. Similarly, for deferred events, the event score may be equal to the negative emissions differential value at the end of the event. Assigning and determining event scores is discussed further herein with reference to Figure 9.

[0117] Figure 9 shows another graph 900 of the expected emissions data with potential EDR events. Graph 900 shows the same expected emission rates 916, average emission rates 912, 914, 920, 924, 928, and 932 as described above with respect to Figure 8, as well as the same emission differential. The graph shows values ​​936, 940, and 944. Graph 900 also shows the thermostat setpoint temperature 948 in cooling mode with potential load shift events 952, 956, 960, 964, 968, and 972, indicated by deviations from the setpoint temperature 948. In this example, the system may identify three potential times for load shift events at approximately 9:00, 10:00, and 11:30.

[0118] After identifying potential load shift events, the system may use emission differential values ​​936, 940, and 944 to calculate a score for each potential event. For example, using an initial mean emission rate 912 and an end mean emission rate 914, the system may determine that the emission differential value 936 at 9:00 is approximately 600. The emission differential value 936 may then be used to assign event scores to preemptive events 956 and deferred events 952. For example, the event score for preemptive event 956 may be equal to the emission differential value 936 (e.g., 600), and the event score for deferred event 952 may be equal to a negative emission differential value 936 (e.g., -600). This result is consistent with the concept that preemptive events achieve a greater reduction in carbon emissions during times when the emission rate 916 is lower.

[0119] The emissions differential value 944 may be used in a similar form to assign event scores to preemptive events 972 and deferred events 968. For example, if the system determines that the emissions differential value 944 is -600, then the event score for preemptive event 972 is equal to the emissions differential value 944 (e.g., -600), and the event score for deferred event 968 is equal to the negative emissions differential value 944 (e.g., 600). This result is consistent with the concept that deferred events achieve a greater reduction in carbon emissions when scheduled during a higher time period. Similarly, the emissions differential value 940 may be used to assign scores to preemptive events 964 and deferred events 960. In this example, the event score for preemptive event 964 may be equal to 200, while the event score for deferred event 960 may be equal to -200.

[0120] In some embodiments, after assigning an event score to each potential load shift event, the system selects the optimal event for reducing carbon emissions based on the event with the best score. Determining the event with the best score may be done in any number of ways without departing from the scope of this teaching, such as by ranking each of the potential load shift events or by using another suitable algorithm or method. For example, the system may determine that the preemptive events 956, 964 and the deferred event 968 represent the best potential load shift events during the period being evaluated, because their associated scores are each higher than the associated scores for the deferred events 952, 960 and the preemptive event 972. The system may restrict the generation of load shift events with scores lower than the minimum score.

[0121] In addition to the minimum score-based constraint, one or more other constraints may be imposed on event generation. These constraints may be used in addition to the emission differential value to reduce overall carbon emissions while minimizing user discomfort and frustration. The system may use any number or type of constraints to minimize user discomfort and frustration. Some of the various types of constraints and how they can affect EDR event generation will be discussed further with reference to Figures 10 and 11.

[0122] Figure 10 shows another graph 1000 of predicted emission data with various time constraints. Graph 1000 represents the same x-axis 1004 and y-axis 1002 and 1008 as graph 400 described above with respect to Figure 4. Graph 1000 also shows the predicted emission rate 1016 over a given period. Graph 1000 also shows the thermostat setpoint temperature 1020. As shown in graph 1000 by deviations from the setpoint temperature 1020, the system may have already generated deferred cooling events 1036 and preemptive cooling events 1048, and may be expected to generate potential preemptive cooling events 1032 and potential deferred cooling events 1040.

[0123] In some embodiments, the generation of load shift events is restricted during predetermined times of the day. For example, the system may restrict the generation of load shift events during the night or early morning. These times may correspond to when the user is asleep, more sensitive to changes in ambient temperature, and therefore more likely to experience discomfort or irritation caused by changes in the setpoint temperature. This type of restriction is shown in Graph 1000 as a first restricted time 1024 and a second restricted time 1028. For example, after identifying a potential preemptive cooling event 1032, the system may determine that it coincides with a first restricted time 1024 and cancel the potential preemptive cooling event 1032. In some embodiments, the system may first determine that there are restricted times during which load shift events do not need to be scheduled, and during these restricted times, the predicted emission rate 1016 for potential events is not evaluated at all.

[0124] To avoid additional user discomfort and frustration, the system may limit the generation of load shift events within a minimum amount of time between other load shift events. For example, the system may limit the generation of any event within one hour of the start or end of another event. The system may limit the generation of two preemptive events or two deferred events within a close time frame. The system may execute a minimum time between the end of a preemptive event and the start of a deferred event. A potential deferred cooling event 1040 exhibits possible conflicts with various of these limitations. In some embodiments, after generating a deferred cooling event 1036 and a preemptive cooling event 1048, the system may evaluate whether to generate a potential deferred cooling event 1040. The system may determine that there is sufficient time 1044 between the deferred cooling event 1036 and the potential deferred cooling event 1040. However, the system may then determine that the end of the potential deferred cooling event 1040 is too close to the start of the preemptive cooling event 1048.

[0125] Figure 11 shows another graph 1100 of predicted emission data with respect to previously generated EDR events. Graph 1100 represents the same x-axis 1104 and y-axis 1102 and 1108 as graph 400 described above with respect to Figure 4. Graph 1100 shows the predicted emission rate 1116 over a given period. Graph 1100 also shows the thermostat setpoint temperature 1120. As shown in graph 1100 by deviations from the setpoint temperature 1120, the system may have already generated deferred cooling events 1136 and 1140, and may be considering generating a potential deferred cooling event 1144.

[0126] To avoid additional user discomfort and frustration, the system may be limited to generating more than a predetermined number of events over a given period. For example, the system may be limited to generating more than four load shift events in any given day. In some embodiments, the system may be limited to a maximum number of any single type of load shift event. For example, the system may be limited to generating more than three deferred events in a given day. This type of limitation is shown in Graph 1100, as indicated by potential deferred cooling event 1144. The system may not have reached the maximum total number of events in a day with deferred cooling events 1136 and 1140. However, the maximum number of deferred events for the day has already been reached. The system may therefore be limited in generating potential deferred cooling events 1144.

[0127] Figure 12 shows graph 1200 of EDR events of various magnitudes and times. Graph 1200 represents the same x-axis 1204 and y-axis 1202 and 1208 as graph 400 described above with respect to Figure 4. Graph 1200 shows the predicted emission rate 1216 over a given period. Graph 1200 also shows the thermostat setpoint temperature 1220. As shown in graph 1200 by deviations from the setpoint temperature 1220, the system may have already generated deferred cooling events 1238 and 1240.

[0128] In addition to the constraints discussed above with respect to Figures 10 and 11, the system may use other factors to reduce the amount of discomfort and frustration experienced by the user when generating EDR events, and / or to increase the carbon reduction impact of the generated EDR events. In some embodiments, the system varies the magnitude of the adjustment to the setpoint temperature relative to the setpoint schedule. For example, based on patterns of user behavior, the system may determine that deferred cooling events that adjust the setpoint temperature by more than 2 degrees relative to the setpoint schedule, such as deferred cooling event 1238, are generally overridden by the user via real-time adjustment of the thermostat's setpoint temperature. Based on this input, the system may determine that a 2-degree deviation is too uncomfortable for the user in relation to the thermostat, and instead generate only deferred cooling events that adjust the setpoint temperature by only 1 degree, as indicated by deferred cooling event 1236. In some embodiments, varying the magnitude of the adjustment to the setpoint temperature is accompanied by a variation in the duration of the EDR event. For example, an EDR event may be generated with a smaller adjustment, while its duration may be extended, as demonstrated by the deferred cooling event 1242. In some embodiments, the magnitude of the adjustment varies based on a number of factors, such as whether the thermostat is in cooling mode or heating mode and / or whether the EDR event is a preemptive or deferred EDR event. For example, some people may not be comfortable with a two-degree adjustment (e.g., making the ambient temperature higher) during a deferred cooling event, but they may still be comfortable with a two-degree adjustment (making the ambient temperature lower) during a preemptive cooling event.

[0129] In some embodiments, the system varies the length of EDR events to reduce user discomfort and frustration. For example, the system may decide that deferred cooling events lasting longer than two hours, such as deferred cooling event 1240, are generally overridden by the user via real-time adjustment of the thermostat setpoint temperature at or near two hours into the event. Based on this input, the system may decide that events lasting longer than two hours produce an unacceptable amount of discomfort and instead generate only deferred cooling events lasting less than two hours, as indicated by deferred cooling event 1238. In some embodiments, the acceptable duration may vary based on a number of factors, such as whether the thermostat is in cooling mode or heating mode and / or whether the EDR event is a preemptive EDR event or a deferred EDR event.

[0130] In some embodiments, the system varies both the duration and magnitude of adjustments to the set temperature to reduce user discomfort while still reducing carbon emissions. For example, the system may determine that one or more users may not tolerate a 2-degree adjustment for longer than two hours, while they may tolerate a 1-degree adjustment for up to three hours. As another example, the system may determine that one or more users may not tolerate a 3-hour event with an adjustment greater than 2 degrees, while they may tolerate a 1-hour event with an adjustment of up to 4 degrees. It is acceptable to conclude that there is a match.

[0131] In some embodiments, multiple EDR events with varying characteristics are distributed across the community for the same emission rate event based on the diverse preferences of people within the community. For example, in a community of 100 households, if 50 households participate in the EDR program, 25 households may receive a longer-duration EDR event with smaller adjustments, while the other 25 households receive a shorter-duration EDR event with larger adjustments. In this way, the system may accommodate the preferences of each specific participant while still achieving a net reduction in carbon emissions.

[0132] As detailed above with respect to Figures 4 to 12, various methods may be used to implement EDR events using the systems detailed in Figures 1 to 3. Figure 13 shows an embodiment of method 1300 for executing EDR events. In some embodiments, method 1300 may be executed by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. For example, the processing system 219 of the cloud-based power control server system 110 may execute software from one or more modules, such as an event scheduler 213, a constraint engine 214, and / or a prediction engine 217. In some embodiments, method 1300 may be executed by a smart device, such as a smart thermostat 160 described above with respect to Figure 3. For example, the processing system 319 of the smart thermostat 160 may execute software from one or more modules, such as an event scheduler 314 and a constraint engine 315. In some embodiments, some steps of Method 1300 are performed by a cloud-based power control server system, such as a cloud-based power control server system 110, while other steps are performed by a smart device, such as a smart thermostat 160.

[0133] Method 1300 may include, in block 1310, receiving emission rate forecasts for a predetermined future period. The emission rate forecasts may include predicted rates of carbon emissions over a predetermined period into the future. Carbon emission rates may be measured in lbs-CO2 / MWh or any similar unit of measurement. The predetermined period into the future may be any number of hours, including 24 hours into the future. Emission rate forecasts may be received from a commercial service that collects and analyzes emission rate data from various sources, such as a power company that provides electricity to a city or region. In some embodiments, emission rate forecasts are received by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. Emission rate forecasts may also be received by a smart thermostat. In some embodiments, a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, may receive emission rate forecasts from the cloud-based power control server system 110.

[0134] In block 1312, emission differential values ​​may be determined for each of several points in time during a predetermined future period. The emission differential value may represent the rate of change of the predicted emission rate at each point in time. The emission differential value may be determined using received emission rate forecasts. For example, the emission differential value may be determined from the difference between a first average emission rate ending at that point in time and a second average emission rate starting at that point in time. Each average emission rate may be an average emission rate for a period of time of varying lengths. For example, the first average emission rate may be the average emission rate over a 30-minute period up to that point in time, while the second average emission rate may be the average emission rate starting at that point in time for a 30-minute period after that point in time. The combination of time up to that point and time after that point in time may be defined as an emission differential range. In some embodiments, the emission differential The differential value is determined by a cloud-based power control server system, such as the cloud-based power control server system 110, as described above with respect to Figure 2. In some embodiments, the emission differential value is determined by a smart thermostat, such as the smart thermostat 160, as described above with respect to Figure 3.

[0135] In block 1314, an EDR event may be generated during a predetermined future period. The EDR event may be generated based on a set of determined emission differential values. For example, the EDR event may be generated at an end time corresponding to the time of an emission differential value among a set of emission differential values ​​representing the maximum rate of change of the predicted emission rate. The type of the EDR event may be based on the emission differential value at the end of the EDR event. For example, if the emission differential value is negative, the EDR event may be a deferred event, and if the emission differential value is positive, the EDR event may be a preemptive event. The EDR event may be generated by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. In some embodiments, the EDR event may be generated by a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3.

[0136] In some embodiments, EDR events are generated based on a predetermined maximum number of EDR events. For example, if the predetermined maximum number of EDR events is 3, the generation of additional EDR events may be limited after the generation of the third event. In some embodiments, the predetermined maximum number of EDR events is set by the system based on how many EDR events an average user is willing to tolerate. In some embodiments, the predetermined maximum number of EDR events is set or modified by user input. For example, a user may set a predetermined maximum number through various settings available to the user. As another example, the system may determine, based on historical data for an account associated with the user, that a user will not tolerate more than a predetermined number of EDR events in a day. In some embodiments, the system considers a predetermined maximum number of EDR events when generating events. For example, the system may consider generating only up to the maximum number of events. In some embodiments, the system generates more events than the maximum number and then applies constraints to reduce that number. For example, the system may generate a large number of events and then apply a constraint algorithm to reach a reduced number of events set for execution.

[0137] The generation of EDR events may be restricted to a specific time of day. For example, the generation of events at night may be restricted. The generation of EDR events may also be restricted by the time relative to previously generated EDR events. For example, the time between the end of a previously generated EDR event may restrict the generation of EDR events with start times that are too close to those of a previously generated EDR event.

[0138] In block 1316, the thermostat may be made to control the HVAC system according to the generated EDR events. The generated EDR events may be preemptive or deferred events. A preemptive EDR event may cause the thermostat to adjust the setpoint temperature to increase the use of the HVAC system for a predetermined time before the end of the preemptive EDR event. While the HVAC system is in cooling mode, a preemptive EDR event may cause the thermostat to decrease the setpoint temperature. While the HVAC system is in heating mode, a preemptive EDR event may cause the thermostat to increase the setpoint temperature. A deferred EDR event may cause the thermostat to adjust the setpoint temperature to decrease the use of the HVAC system for a predetermined time before the end of the deferred EDR event. While the HVAC system is in cooling mode, a deferred EDR event may cause the thermostat to... The set temperature may be increased. During the time the HVAC system is in heating mode, a deferred EDR event may cause the thermostat to decrease the set temperature. In some embodiments, a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2, causes a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, to control the HVAC system.

[0139] Figure 14 shows an embodiment of Method 1400 for executing EDR events based on event score rankings. In some embodiments, Method 1400 is performed by any or all of the same components as described above with respect to Method 1300 with respect to Figure 13. Method 1400 may include receiving emission rate forecasts for a predetermined future period in block 1410. In some embodiments, the system generates the emission rate forecasts internally. For example, the system may collect and analyze emission rate data from a utility company to generate emission rate forecasts. The emission rate forecasts may include predicted carbon emissions over a predetermined future period. Carbon emissions may be measured in lbs-CO2 / MWh or any similar unit of measurement. The predetermined future period may be any number of hours, including the next 24 hours. Emission rate forecasts may be received from a commercial service that collects and analyzes emission rate data from various sources, such as a utility company that provides electricity to a city or region.

[0140] In some embodiments, emission rate forecasts are received by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. The emission rate forecasts may also be received from the cloud-based power control server system 110 by a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3. In some embodiments, emission rate forecasts are generated by the cloud-based power control server system. For example, emission rate forecasts may be generated by a forecasting engine, such as the forecasting engine 217 described above with respect to Figure 2, using emission data collected from a power company. In some embodiments, the generation of emission rate forecasts is based on historical emission data, current emission data, and / or weather data.

[0141] In block 1412, the emission differential value may be determined for each of several points in time during a predetermined future period. The emission differential value may represent the rate of change of the predicted emission rate at each point in time. The emission differential value may be determined using emission rate forecasts. For example, the emission differential value may be determined from the difference between a first average emission rate ending at that point in time and a second average emission rate starting at that point in time. Each average emission rate may be an average emission rate for a period of time of varying lengths. For example, the first average emission rate may be the average emission rate over a 30-minute period up to that point in time, while the second average emission rate may be the average emission rate starting at that point in time for a 30-minute period after that point in time. The combination of time up to that point and time after that point in time may be defined as the emission differential range. In some embodiments, the emission differential value is determined by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. In some embodiments, the emission differential value is determined by a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3.

[0142] In block 1414, preemptive and deferred event scores may be determined for preemptive and deferred events that end at each of multiple time points based on the emissions differential value. The preemptive event score for preemptive events that end at a time related to the emissions differential value may be equal to the emissions differential value. A higher preemptive event score may correspond to a faster increase in the emissions rate at that point in time, while a lower preemptive event score may correspond to a slower increase in the emissions rate at that point in time. A deferred event score for a deferred event ending in a time related to the emissions differential value may be equal to a negative emissions differential value. A higher deferred event score may correspond to a faster decrease in the emissions rate at that point in time, while a lower deferred event score may correspond to a slower decrease in the emissions rate at that point in time. In some embodiments, the preemptive and deferred event scores are determined by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. In some embodiments, the preemptive and deferred event scores are determined by a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3. In block 1416, the ranking of each preemptive and deferred event may be generated based on the associated preemptive and deferred event scores. In some embodiments, after assigning an event score to each potential load shift event, the system selects the optimal event for reducing carbon emissions based on the event with the best score. Determining the event with the best score may be done in any number of ways, such as by ranking each of the potential load shift events or by using any other suitable algorithm or method.

[0143] In block 1418, EDR events may be generated during a predetermined future period based on the ranking of events. For example, EDR events may be generated based on the highest-ranking event having the highest event score. In some embodiments, EDR events may be based on a predetermined maximum number of EDR events. For example, if the predetermined maximum number of EDR events is 3, the generation of additional EDR events may be restricted after the third EDR event has been generated. EDR events may be generated by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. In some embodiments, EDR events are generated by a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3.

[0144] In block 1420, the thermostat may be configured to control the HVAC system according to the generated EDR events. The generated EDR events may be preemptive or deferred events. A preemptive EDR event may cause the thermostat to adjust the setpoint temperature to increase the use of the HVAC system for a predetermined period before the end of the preemptive EDR event. While the HVAC system is in cooling mode, a preemptive EDR event may cause the thermostat to decrease the setpoint temperature. While the HVAC system is in heating mode, a preemptive EDR event may cause the thermostat to increase the setpoint temperature. A deferred EDR event may cause the thermostat to adjust the setpoint temperature to decrease the use of the HVAC system for a period before the end of the deferred EDR event. While the HVAC system is in cooling mode, a deferred EDR event may cause the thermostat to increase the setpoint temperature. While the HVAC system is in heating mode, a deferred EDR event may cause the thermostat to decrease the setpoint temperature. In some embodiments, a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2, causes a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, to control the HVAC system.

[0145] Figure 15 shows an embodiment of Method 1500 for executing EDR events based on a limited number of allowed events. In some embodiments, Method 1500 is performed by any or all of the same components as described above with respect to Method 1300 with respect to Figure 13. In block 1510, Method 1500 is performed by a predetermined This may include receiving emission rate forecasts for future periods. Emission rate forecasts may include predicted carbon emission rates over a predetermined period in the future. Carbon emission rates may be measured in lbs-CO2 / MWh or any similar unit of measurement. Future periods may be any number of hours, including the next 24 hours. Emission rate forecasts may be received from a commercial service that collects and analyzes emission rate data from various sources, such as a power company that provides electricity to a city or region. In some embodiments, emission rate forecasts are received by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. Emission rate forecasts may also be received by a smart thermostat. In some embodiments, a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, receives emission rate forecasts from the cloud-based power control server system 110.

[0146] In block 1512, the emission differential value may be determined for each of several points in time during a predetermined future period. The emission differential value may represent the rate of change of the predicted emission rate at each point in time. The emission differential value may be determined using emission rate forecasts. For example, the emission differential value may be determined from the difference between a first average emission rate ending at that point in time and a second average emission rate starting at that point in time. Each average emission rate may be an average emission rate for a period of time of varying lengths. For example, the first average emission rate may be the average emission rate over a 30-minute period up to that point in time, while the second average emission rate may be the average emission rate starting at that point in time for a 30-minute period after that point in time. The combination of time up to that point and time after that point in time may be defined as the emission differential range. In some embodiments, the emission differential value is determined by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. In some embodiments, the emission differential value is determined by a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3.

[0147] In block 1514, the number of preemptive EDR events generated in advance for a predetermined future period may be determined. Determining the number of preemptive EDR events may involve accessing memory or a database of previously generated EDR events. For example, a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2, may access a local or remote database containing some or all of the previously generated EDR events by the cloud-based power control server system. In some embodiments, a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, accesses memory containing previously generated EDR events for a predetermined future period.

[0148] In block 1516, it may be determined that the number of previously generated preemptive EDR events is equal to the maximum number of preemptive events. The maximum number of preemptive events may be any number that reduces the discomfort or frustration experienced by the user. For example, the maximum number may be three preemptive EDR events per day. However, it should be noted that any appropriate number may be used to limit the amount of discomfort or frustration experienced by the user. In some embodiments, there may be a maximum number of deferred events. Determining that the number of previously generated preemptive EDR events is equal to the maximum number of preemptive events may be done by a simple comparison of each number. In block 1518, the generation of additional preemptive EDR events may be restricted until after a predetermined future period. Restricting the generation of additional preemptive EDR events may instead include generating deferred EDR events during a predetermined future period. In some embodiments, the restriction includes not evaluating emission differential values ​​that have a negative value.

[0149] As illustrated in Figures 4-15, EDR events may be generated based on emission rate forecasts covering one or more hours in the future. However, since conditions change, the predicted emission rates may change. For example, changes in weather or electricity generation can affect the generation of carbon emissions. Since emission rates are not constant, the accuracy of the forecast may decrease as the forecast covers further into the future. Similarly, an EDR event scheduled far into the future may not be effective enough to reduce carbon emissions if the actual emission rate does not match the predicted emission rate on which the EDR event was based.

[0150] In some embodiments, the system may receive or generate updated forecasts at periodic time intervals. For example, the system may receive a stream of forecasts every 5 minutes, every 10 minutes, every 15 minutes, or at other time intervals with new forecasts for new periods into the future. Once all forecasts have been received, the system may evaluate the forecast emission rates using the same or similar methods described above with respect to Figures 4 to 15. In some embodiments, the optimal scheduling of EDR events may be recalculated periodically or from time to time using the most recent available forecasts. In some embodiments, existing EDR events that were based on previous forecasts may be updated by the system based on each new forecast. For example, a modified EDR event with a modified end time may be generated based on subsequent forecasts and sent to the thermostat after the thermostat has begun controlling the HVAC system according to the initial event, but before the initial end time and / or modified end time. By periodically or from time to time recalculating and / or updating existing EDR events, the system may improve the accuracy and effectiveness of each EDR event for reducing carbon emissions.

[0151] In some embodiments, EDR events are transmitted to the thermostat after they are generated, and modified EDR events based on subsequent updated predictions are transmitted before and / or after the thermostat begins controlling the HVAC system according to the initial EDR events. Thus, a favorable combination of practicality and immediacy can be provided. Practicality may arise from the fact that the start and end times of the EDR events are communicated in advance and stored locally in the thermostat, in order to provide predictable forwarded EDR events that can be executed from start to finish. Immediacy may arise from the fact that the modified EDR events are communicated and executed in just-in-time format, if feasible. However, the practicality of the initial EDR events may remain, even if the better (i.e., modified) EDR events are not communicated in time, since the second-best option (i.e., the initial EDR events) is still performed. How and when to update existing EDR events will be discussed further herein with respect to Figures 16–25.

[0152] Figures 16A and 16B show graph 1600 of the updated emissions forecast and EDR events sent based on the updated emissions forecast. Graph 1600 represents the same x-axis 1604 and y-axis 1602 and 1608 as graph 400 described above with respect to Figure 4. Graph 1600 also shows the thermostat set temperature 1620 with respect to time. Graph 1600 also shows the time 1630 (e.g., 6:00) when the forecast including the predicted emissions rate 1616 is received and the time 1632 (e.g., 12:00) when the updated forecast including the predicted emissions rate 1618 is received. Graph 1600 also includes the EDR event 1640.

[0153] In some embodiments, EDR events are not executed until immediately before their start time. For example, referring to Figure 16A, the system may generate EDR event 1640 after receiving the predicted emission rate 1616 at time 1630. However, as shown by the dotted line in Figure 16A, EDR event 1640 has not yet been executed or sent to the thermostat for execution. Waiting for the EDR event to be executed By doing so, the system may improve the chances that each EDR event is based on the best available forecast and may consider potential changes in the forecast before executing the event. In some embodiments, the system simply executes the EDR event if it is too late to execute the event after waiting for the next available forecast. For example, as shown in Figure 16B, the system may decide that a new forecast, including the forecast emission rate 1618, is received at time 1632 before the scheduled start time of event 1640. In some embodiments, the system determines when the next forecast is available based on the time since the last forecast was received or generated, and the intervals at which forecasts are received or generated.

[0154] In some embodiments, after receiving an updated forecast, the system may determine that there is no change or sufficient change in the predicted emissions rate between the most recent forecast and the previous forecast to ensure a change in the EDR event. For example, as shown by Figures 16A and 16B, the predicted emissions rate did not change between time 1630 and time 1632. In some embodiments, if the amount of change in the updated forecast is less than a threshold amount, the previously generated EDR event is maintained. After determining that the predicted emissions rate did not change, or that no change exceeding a threshold occurred, the system may determine that the updated forecast is the last available forecast before the scheduled start time of the EDR event, and may proceed to execute the EDR event. For example, the system may determine that the next available forecast is 18:00, later than the scheduled start time of EDR event 1640. As shown by the solid line in Figure 16B, the system may execute EDR event 1640 and / or send EDR event 1640 to the thermostat for execution at an appropriate time.

[0155] Figures 17A and 17B show Graph 1700 of the updated emissions forecast with EDR events sent early based on the updated emissions forecast changes. Graph 1700 represents the same x-axis 1704 and y-axis 1702 and 1708 as Graph 400 described above with respect to Figure 4. Graph 1700 also shows the thermostat setpoint temperature 1720 with respect to time. Graph 1700 also shows the time 1730 (e.g., 6:00) when the forecast including the predicted emissions rate 1716 is received and the time 1732 (e.g., 12:00) when the updated forecast including the predicted emissions rate 1718 is received. Graph 1700 also includes EDR events 1740.

[0156] As shown in Figure 17A, the system may generate an EDR event 1740 after receiving a predicted emission rate 1716 at time 1730. Since the forecast received at time 1730 predicted a decrease in the emission rate at 18:00, the EDR event 1740 may be scheduled to end at approximately 18:00. In some embodiments, after receiving an updated forecast, the system proceeds to determine whether the updated forecast predicts that a particular change in the emission rate will occur at an earlier time than predicted in the previous forecast. For example, as shown in Figure 17A, the predicted emission rate 1716 received at time 1730 indicated a decrease in the emission rate at approximately 18:00. However, as shown in Figure 17B, the predicted emission rate 1718 received at time 1732 indicates that the decrease in the emission rate now occurs at approximately 15:00.

[0157] In some embodiments, after determining that an emission rate change will occur at an earlier time, the system updates the EDR event to match the updated time for the emission rate change. For example, as shown in Figure 17B, EDR event 1740 is now scheduled to end at approximately 15:00 to match the predicted decrease in emissions. After updating the EDR event, the system may determine that the updated forecast is the last available forecast before the scheduled start time of the EDR event, and the EDR event The system proceeds to execute the vent. For example, the system may determine that the next available forecast is 18:00, which is later than the scheduled start time of EDR event 1740. As shown by the solid line in Figure 17B, the system may execute EDR event 1740 and / or send EDR event 1740 to the thermostat for execution at an appropriate time.

[0158] In some embodiments, the system updates the EDR event only if the change between forecasts is greater than a threshold amount. For example, if the subsequent emission rate forecast indicates that the predicted increase or decrease in the predicted emission rate is expected to occur more than 5 minutes earlier than originally predicted, the EDR event is updated based on the subsequent emission rate forecast. In other embodiments, the threshold may be 10 minutes, 15 minutes, 30 minutes, or other appropriate time quantities. Additional thresholds may be used to limit the amount of updates to the EDR event, such as threshold changes in duration and / or threshold changes in emissions.

[0159] Figures 18A and 18B show graph 1800 of updated emissions forecasts with delayed EDR events based on updated emissions changes. Graph 1800 represents the same x-axis 1804 and y-axis 1802 and 1808 as graph 400 described above with respect to Figure 4. Graph 1800 also shows the thermostat setpoint temperature 1820 with respect to time. Graph 1800 also shows the time 1830 (e.g., 6:00) when the forecast including predicted emissions rate 1816 is received and the time 1832 (e.g., 12:00) when the updated forecast including predicted emissions rate 1818 is received. Graph 1800 also includes the EDR event 1840.

[0160] As shown in Figure 18A, the system may generate an EDR event 1840 after receiving a predicted emission rate 1816 at time 1830. Since the forecast received at time 1830 predicted a decrease in the emission rate at 15:00, the EDR event 1840 may be scheduled to end at approximately 15:00. In some embodiments, after receiving an updated forecast, the system proceeds to determine whether the updated forecast predicts that a particular change in the emission rate will occur at a later time than predicted in the previous forecast. For example, as shown in Figure 18A, the predicted emission rate 1816 received at time 1830 indicated a decrease in the emission rate at approximately 15:00. However, as shown in Figure 18B, the predicted emission rate 1818 received at time 1832 indicates that the decrease in the emission rate will now occur at approximately 18:00.

[0161] In some embodiments, after determining that an emission rate change occurs at a later time, the system may delay the EDR event to coincide with the updated time for the emission rate change. For example, as shown in Figure 18B, EDR event 1840 is now scheduled to end at approximately 18:00 to coincide with the predicted decrease in emissions. After updating the EDR event, the system may determine that the updated forecast is not the last available forecast before the scheduled start time of the EDR event, and may wait until the next available forecast is received before executing the EDR event. For example, the system may determine that the next available forecast is 14:00, earlier than the scheduled start time of EDR event 1840. As shown by the dotted line in Figure 18B, the system may wait until an appropriate time after approximately 14:00 to execute and / or send EDR event 1840 to the thermostat for execution. In some embodiments, the system may determine that the updated forecast is the last available forecast before the scheduled start time of the EDR event and may proceed to execute the EDR event.

[0162] In some embodiments, the system delays the EDR event only if the change between forecasts is greater than a threshold amount. For example, if the subsequent emission rate forecast is greater than the predicted emission rate If an increase or decrease is predicted to occur more than 5 minutes later than originally predicted, the EDR event is updated based on the subsequent emission rate forecast. In other embodiments, the threshold may be 10 minutes, 15 minutes, 30 minutes, or other appropriate time units. Additional thresholds may be used to limit the amount of updates to the EDR event, such as threshold changes in duration and / or threshold changes in emissions.

[0163] Figures 19A and 19B show Graph 1900 of updated emissions forecasts with restrictions on sending EDR events earlier based on previously sent EDR events. Graph 1900 represents the same x-axis 1904 and y-axis 1902 and 1908 as Graph 400 described above with respect to Figure 4. Graph 1900 also shows the thermostat setpoint temperature 1920 with respect to time. Graph 1900 also shows the time 1930 (e.g., 6:00) when the forecast including predicted emissions rate 1916 is received and the time 1932 (e.g., 12:00) when the updated forecast including predicted emissions rate 1918 is received. Graph 1900 also includes EDR events 1936 and 1940.

[0164] As shown in Figure 19A, the system may generate EDR events 1936 and 1940 after receiving a forecast including a predicted emission rate 1916 at time 1930. As shown by the solid line in Figure 19B, EDR event 1936 may have already been executed by the system before time 1932 when the updated forecast is received. In some embodiments, after receiving the updated forecast, the system decides that the updated forecast predicts a particular emission rate change will occur at an earlier time than predicted in the previous forecast. For example, as shown in Figure 19A, the predicted emission rate 1916 received at time 1930 indicated an emission rate decrease at approximately 18:00. However, as shown in Figure 19B, the predicted emission rate 1918 received at time 1932 now indicates that the emission rate decrease will occur at approximately 15:00.

[0165] In some embodiments, after determining that an emission rate change occurs at an earlier time, the system determines whether there are any constraints that limit the system to updating the EDR event based on the updated forecast. In some embodiments, the system is restricted from scheduling the EDR event within a minimum time interval between other EDR events. For example, as shown by Figure 19B, the system may be restricted from updating EDR event 1940 to match the updated time for the emission rate change, because the time span 1944 between EDR event 1936 and EDR event 1940 is smaller than a predetermined minimum time interval between EDR events. Additional constraints are described further above with respect to Figures 10 and 11. In some embodiments, after determining that a constraint limits modification of an event, the system cancels the EDR event and generates a new EDR event at a later time interval that matches a different emission rate change. In some embodiments, after determining that a constraint limits a predetermined modification, the system identifies alternative modifications, such as reducing the event duration.

[0166] Figures 20A and 20B show Graph 2000 of the updated emissions forecast with limitations on delaying EDR events based on a limited time of day. Graph 2000 represents the same x-axis 2004 and y-axis 2002 and 2008 as Graph 400 described above with respect to Figure 4. Graph 2000 also shows the thermostat setpoint temperature 2020 with respect to time. Graph 2000 also shows the time 2030 (e.g., 6:00) when the forecast including the predicted emissions rate 2016 is received and the time 2032 (e.g., 12:00) when the updated forecast including the predicted emissions rate 2018 is received. Graph 2000 also includes EDR event 2040.

[0167] As shown in Figure 20A, the system predicts an emission rate of 201 at time 2030. An EDR event 2040 may be generated after receiving 6. The EDR event 2040 may be scheduled to end at approximately 15:00 because the forecast received at time 2030 predicted a decrease in the emission rate at 15:00. In some embodiments, after receiving the updated forecast, the system proceeds to determine whether the updated forecast predicts that a particular change in the emission rate will occur at a later time than predicted in the previous forecast. For example, as shown in Figure 20A, the forecast emission rate 2016 received at time 2030 indicated a decrease in the emission rate at approximately 15:00. However, as shown in Figure 20B, the forecast emission rate 2018 received at time 2032 indicates that the decrease in the emission rate now occurs at approximately 18:00.

[0168] In some embodiments, after determining that an emissions change occurs at a later time, the system determines whether there are any constraints that restrict the system from updating EDR events based on the updated forecast. In some embodiments, the system is restricted from scheduling EDR events for a predetermined period of time in the day. For example, as shown by Figure 20B, the system may be restricted from updating EDR event 2040 to coincide with an updated time for an emissions change, because EDR event 2040 ends after the restricted time 2028 has begun. As another example, the system may be restricted from updating EDR events to occur earlier when they would conflict with a restricted time in the day. Additional constraints are described further above with respect to Figures 10 and 11. In some embodiments, after determining that constraints restrict modification of an event, the system cancels the EDR event and generates a new EDR event at a later time that coincides with a different emissions change.

[0169] Figures 21A and 21B show graph 2100 of the updated emissions forecast and the extended end time of the dispatched EDR event based on the updated emissions forecast changes. Graph 2100 represents the same x-axis 2104 and y-axis 2102 and 2108 as graph 400 described above with respect to Figure 4. Graph 2100 also shows the thermostat setpoint temperature 2120 with respect to time. Graph 2100 also shows the time 2130 (e.g., 6:00) when the forecast including the predicted emissions rate 2116 is received and the time 2132 (e.g., 12:00) when the updated forecast including the predicted emissions rate 2118 is received. Graph 2100 also includes the EDR event 2140.

[0170] As shown in Figure 21A, the system may generate an EDR event 2140 after receiving a forecast at time 2130 (e.g., 12:00) which includes the predicted emission rate 2116. In some embodiments, after receiving a forecast, the system proceeds to determine whether the forecast is the last available forecast before the scheduled start time of the EDR event, and to execute the EDR event or to have the thermostat execute the EDR event at an appropriate time. For example, as shown in Figures 21A and 21B, the system may determine that the next available forecast is at a later time 2132 (e.g., 14:00) than the scheduled start time of the EDR event 2140 (e.g., 13:00). In some embodiments, the system determines when the next forecast will be available based on the time since the last forecast became available and the interval between forecasts.

[0171] In some embodiments, while an EDR event is currently running, the system receives an updated forecast and determines that the updated forecast predicts a particular emission rate change will occur later than predicted in the previous forecast. For example, as shown in Figure 21A, the forecast emission rate 2116 received at time 2130 indicated an emission rate decrease at approximately 15:00. However, as shown in Figure 21B, the forecast emission rate 2118 received at time 2132 indicates that the emission rate decrease will now occur at approximately 17:00. To indicate that.

[0172] In some embodiments, after determining that an emissions rate change will occur at a later time, the system may extend the event based on the updated forecast. For example, as shown in Figure 21B, the system may extend the end of EDR event 2140 to match the expected decrease in the predicted emissions rate 2118 at approximately 17:00. In some embodiments, adjusting the end time involves generating and transmitting a modified EDR event with a modified end time that is later than the end time of the compared, in this case the initial EDR event. In some embodiments, while an EDR event is in progress, the system may periodically or occasionally adjust the end time based on the most recent available forecast and terminate the EDR event only if the next available forecast is received later than the currently scheduled end time for the EDR event. For example, as shown in Figure 21B, the system may determine that the next available forecast is 15:00, earlier than the scheduled end time 2142 for EDR event 2140. As shown by the dotted line in Figure 21B, the system may wait to terminate EDR event 2140 and / or to have the thermostat terminate EDR event 2140 until after it has received the next available updated forecast.

[0173] In some embodiments, the system is restricted from extending EDR events beyond a predetermined length of time. For example, to minimize user discomfort and frustration caused by longer EDR events, the system may include a constraint that defines a maximum allowable event duration, and the system may restrict extending the EDR event once the event duration reaches the maximum allowable duration. In some embodiments, the system is restricted from extending EDR events beyond a predetermined time by other constraints. For example, as described above with respect to Figures 10 and 11, the system may be restricted by a predetermined time of day or by additional EDR events scheduled in advance.

[0174] In some embodiments, the system extends an EDR event only if the change between forecasts is greater than a threshold amount. For example, if a subsequent emission rate forecast indicates that an increase or decrease in the predicted emission rate is expected to occur more than 5 minutes later than originally predicted, the EDR event is updated based on the subsequent emission rate forecast. In other embodiments, the threshold may be 10 minutes, 15 minutes, 30 minutes, or other appropriate time quantities. Additional thresholds may be used to limit the amount of updates to the EDR event, such as threshold changes in duration and / or threshold changes in emissions.

[0175] Figures 22A and 22B show Graph 2200 of the updated emissions forecast with EDR events that end earlier based on the changes in the updated emissions forecast. Graph 2200 represents the same x-axis 2204 and y-axis 2202 and 2208 as Graph 400 described above with respect to Figure 4. Graph 2200 also shows the thermostat setpoint temperature 2220 with respect to time. Graph 2200 also shows the time 2230 (e.g., 6:00) when the forecast including the predicted emissions rate 2216 is received and the time 2232 (e.g., 12:00) when the updated forecast including the predicted emissions rate 2218 is received. Graph 2200 also includes EDR events 2240.

[0176] As shown in Figure 22A, the system may generate an EDR event 2140 after receiving a forecast at time 2230 (e.g., 12:00) which includes the predicted emission rate 2216. In some embodiments, after receiving the forecast, the system determines whether the forecast is the last available forecast before the scheduled start time of the EDR event and proceeds to execute the EDR event at the appropriate time or to have the thermostat execute the EDR event. For example, as shown in Figures 22A and 22B, the system M may determine that the next available forecast is a later time 2232 (e.g., 14:00) than the scheduled start time of EDR event 2240 (e.g., 13:00).

[0177] In some embodiments, while an EDR event is currently running, the system receives an updated forecast and determines that the updated forecast predicts a particular emission rate change will occur earlier than predicted in the previous forecast. For example, as shown in Figure 22A, the forecast emission rate 2216 received at time 2230 indicated an emission rate decrease at approximately 17:00. However, as shown in Figure 22B, the forecast emission rate 2218 received at time 2232 indicates that the emission rate decrease will now occur at approximately 15:00.

[0178] In some embodiments, after determining that an emission rate change occurs at an earlier time, the system shortens the event based on the updated forecast. For example, as shown in Figure 22B, the system may update the end of EDR event 2240 to match the expected decrease in the predicted emission rate 2218 at approximately 15:00. After updating the EDR event, the system may determine that the updated forecast is the last available forecast before the scheduled end time of the EDR event and proceed to terminate the EDR event at an appropriate time or to have the thermostat terminate the EDR event. For example, the system may determine that the next available forecast is 18:00, later than the scheduled end time of EDR event 2240. In some embodiments, adjusting the end time involves generating and transmitting a modified EDR event that has a modified end time earlier than the end time of the original EDR event being compared. In some embodiments, an EDR event is shortened only when the change between predictions is greater than a threshold amount, such as 5 minutes, 10 minutes, 15 minutes, or any other appropriate unit of time, earlier than the originally predicted end time.

[0179] Various methods may be implemented using the systems detailed above in Figures 1 to 3 to implement EDR events as detailed above with respect to Figures 16A to 22B. Figure 23 shows an embodiment of method 2300 for managing EDR events based on updated emissions. In some embodiments, method 2300 is implemented by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. For example, the processing system 219 of the cloud-based power control server system 110 may run software from one or more modules, such as an event scheduler 213, a constraint engine 214, and / or a forecast engine 217. In some embodiments, method 2300 is implemented by a smart device, such as the smart thermostat 160 described above with respect to Figure 3. For example, the processing system 319 of the smart thermostat 160 may run software from one or more modules, such as an event scheduler 314 and a constraint engine 315. In some embodiments, some steps of Method 2300 are performed by a cloud-based power control server system, such as a cloud-based power control server system 110, while other steps are performed by a smart device, such as a smart thermostat 160.

[0180] Method 2300 may include obtaining multiple emission rate forecasts at different times in block 2310. The multiple emission rate forecasts may be received from a commercial service that collects and analyzes emission rate data from various sources, such as a power company that provides electricity to a city or region. In some embodiments, the multiple emission rate forecasts are generated by a cloud-based power control server system using data collected from one or more sources, such as a power company and a weather forecasting agency. The multiple emission rate forecasts may be obtained every 5 minutes, every 15 minutes, or The data may be acquired at regular intervals, such as every 30 minutes. For example, a cloud-based power control server system may send requests to an external service at regular intervals and receive new emission rate forecasts in response. In some embodiments, multiple emission rate forecasts are received by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. Multiple emission rate forecasts may also be received by a smart thermostat. In some embodiments, a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, receives multiple emission rate forecasts from the cloud-based power control server system 110. Each of the multiple emission rate forecasts may include a predicted rate of carbon emissions over a predetermined future period, as described above with respect to Figure 4.

[0181] In block 2312, the EDR event may be generated based on a first emission rate forecast among several emission rate forecasts. The EDR event may be generated according to any of the methods described above with respect to Figures 13 to 15. For example, the EDR event may be generated at an end time corresponding to the time of the emission differential value calculated from the first emission rate forecast. The first emission rate forecast may be any emission rate forecast received at any time. In some embodiments, the EDR event is generated by the event scheduler 213 of the cloud-based power control server system 110 as described above with respect to Figure 2. In some embodiments, after the EDR event is generated, it is sent to the thermostat and stored by the thermostat until the start time of the EDR event, when the thermostat may begin controlling the HVAC system according to the EDR event. In some embodiments, the EDR event is generated by the event scheduler 314 of the smart thermostat 160 as described above with respect to Figure 3. The EDR event may be a preemptive EDR event or a deferred EDR event.

[0182] In block 2314, subsequent emission rate forecasts may be obtained from multiple emission rate forecasts. In some embodiments, the subsequent forecast is obtained after an EDR event has been generated. The subsequent emission rate forecast may be the next available emission rate forecast received after the first emission rate forecast. In some embodiments, the subsequent emission rate forecast may be any subsequent forecast after an EDR event has been generated that shows a change in the predicted emission rate over a specific time period of interest. For example, the first forecast may predict an increase in the emission rate for 10 hours from the time the first forecast was received. After 5 hours and several similar forecasts, a new forecast may now predict that the same increase occurs in 4 hours instead of 5 hours, as originally predicted by the first emission rate forecast.

[0183] In block 2316, the generated EDR event may be modified based on subsequent emission rate forecasts. In some embodiments, the generated EDR event is modified based on the difference in predicted emission rates between a first emission rate forecast and a subsequent emission rate forecast. For example, if the EDR event was generated based on the emission rate increase predicted by the first forecast, the EDR event may be updated to occur earlier based on a subsequent forecast that predicts the increase will occur sooner than originally predicted. In some embodiments, the generated EDR event is modified based on multiple subsequent emission rate forecasts. For example, if the EDR event was generated based on the emission rate increase predicted by the first emission rate forecast, the EDR event may be delayed if a second forecast indicates an increase at a later time. Furthermore, the EDR event may be delayed again after a third forecast indicates an increase at an even later time than the second forecast. In some embodiments, the EDR event is modified using one of the same methods used to initially generate the events as described above with respect to Figures 13 to 15.

[0184] In some embodiments, the same constraints described above with respect to Figures 10 and 11 are also present. However, this also applies to modifying EDR events. For example, there may be constraints on delaying an event when it overlaps with a limited time of day. Another example is the constraint on modifying an event to occur earlier if it is too close to an event that has already occurred. In some embodiments, an EDR event is modified only if the difference in subsequent emission rate forecasts is greater than a threshold change. For example, if the emission rate reduction is expected to occur less than 5 minutes later than originally predicted, the EDR event does not need to be modified. In other embodiments, the threshold may be 10 minutes, 15 minutes, 30 minutes, or other appropriate time units.

[0185] In block 2318, the thermostat may be configured to control the HVAC system according to a modified EDR event. The thermostat may be configured to control the HVAC system in any of the ways described above with respect to Figures 13 to 15. For example, at the start time of an EDR event, the EDR event may cause the thermostat to raise or lower its set temperature to increase or decrease the use of the HVAC system depending on whether the HVAC system is in heating mode or cooling mode. In some embodiments, a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2, causes a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, to control the HVAC system.

[0186] Figure 24 shows an embodiment of Method 2400 for sending the last EDR event based on updated emissions forecasts. In some embodiments, Method 2400 is performed by any or all of the same components as described above with respect to Method 2300 with respect to Figure 23. Method 2400 may include, in block 2410, obtaining multiple emissions forecasts at different times. Multiple emissions forecasts may be received from a commercial service that collects and analyzes emissions data from various sources, such as a power company that provides electricity to a city or region. In some embodiments, multiple emissions forecasts may be generated by a cloud-based power control server system using data collected from one or more sources, such as a power company and a weather forecasting agency. Multiple emissions forecasts may be obtained at regular intervals, such as every 5 minutes, every 15 minutes, or every 30 minutes. For example, the cloud-based power control server system may send requests to an external service at regular intervals and receive new emissions forecasts in response. In some embodiments, multiple emission rate forecasts are received by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. Multiple emission rate forecasts may also be received by a smart thermostat. In some embodiments, a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, receives multiple emission rate forecasts from the cloud-based power control server system 110. Each of the multiple emission rate forecasts may include a predicted carbon emission rate over a predetermined future period, as described above with respect to Figure 4.

[0187] In block 2412, the EDR event may be generated based on a first emission rate forecast among several emission rate forecasts. The EDR event may be generated according to any of the methods described above with respect to Figures 13 to 15. For example, the EDR event may be generated at an end time corresponding to the time of the emission differential value calculated from the first emission rate forecast. The first emission rate forecast may be any emission rate forecast received at any time. In some embodiments, the EDR event is generated by the event scheduler 213 of the cloud-based power control server system 110 as described above with respect to Figure 2. In some embodiments, the EDR event is generated by the event scheduler 314 of the smart thermostat 160 as described above with respect to Figure 3. The EDR event may be a preemptive EDR event or a deferred EDR event. .

[0188] In block 2414, the next available emission rate forecast may be determined to be later than the scheduled start time of the generated emission demand response event. For example, if the generated emission demand response event is scheduled to start at 15 minutes, the next available emission rate forecast may not be available for the next 30 minutes. In block 2416, after determining that the next available forecast will be received after the scheduled start time, the start time may be set to begin at the scheduled start time, which is before the next available forecast is received. In some embodiments, generating an EDR event may generate only future EDR events that may be subject to modification by methods such as method 2300 described above with respect to Figure 23. For example, when subsequent emission rate forecasts are received, the start and end times may be modified based on the new forecast for the emission rate. In some embodiments, when the last available emission rate forecast before the future start time of the EDR event is received, the final start time of the EDR event is set, and the EDR event is then executed by the thermostat.

[0189] In block 2418, the thermostat may be configured to control the HVAC system according to a modified EDR event. The thermostat may be configured to control the HVAC system according to any of the methods described above with respect to Figures 13 to 15. For example, at the start time of an EDR event, the EDR event may cause the thermostat to raise or lower its set temperature to increase or decrease the use of the HVAC system depending on whether the HVAC system is in heating mode or cooling mode. In some embodiments, a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2, causes a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, to control the HVAC system.

[0190] Figure 25 shows an embodiment of Method 2500 for correcting EDR events based on updated emission forecasts. In some embodiments, Method 2500 is performed by any or all of the same components as described above with respect to Method 2300 with respect to Figure 23. Method 2500 may include, in block 2510, obtaining multiple emission forecasts at different times. Multiple emission forecasts may be received from a commercial service that collects and analyzes emission data from various sources, such as a power company that provides electricity to a city or region. In some embodiments, multiple emission forecasts are generated by a cloud-based power control server system using data collected from one or more sources, such as a power company and a weather forecasting service. Multiple emission forecasts may be obtained at regular intervals, such as every 5 minutes, every 15 minutes, or every 30 minutes. For example, the cloud-based power control server system may send requests to an external service at regular intervals and receive new emission forecasts in response. In some embodiments, multiple emission rate forecasts are received by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. Multiple emission rate forecasts may also be received by a smart thermostat. In some embodiments, a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, receives multiple emission rate forecasts from the cloud-based power control server system 110. Each of the multiple emission rate forecasts may include a predicted carbon emission rate over a predetermined future period, as described above with respect to Figure 4.

[0191] In block 2512, the EDR event may be generated based on a first emission rate forecast among several emission rate forecasts. The EDR event may be generated according to any of the methods described above with respect to Figures 13 to 15. For example, the EDR event may be based on a first emission rate forecast. The EDR event may be generated at an end time corresponding to the time of the emission differential value calculated from the rate forecast. The first emission rate forecast may be any emission rate forecast received at any time. In some embodiments, the EDR event is generated by the event scheduler 213 of the cloud-based power control server system 110 as described above with respect to Figure 2. In some embodiments, the EDR event is generated by the event scheduler 314 of the smart thermostat 160 as described above with respect to Figure 3. The EDR event may be a preemptive EDR event or a deferred EDR event.

[0192] In block 2514, the thermostat may be configured to control the HVAC system according to the generated EDR event. The thermostat may be configured to control the HVAC system according to any of the methods described above with respect to Figures 13 to 15. For example, at the start time of the EDR event, the EDR event may cause the thermostat to raise or lower the thermostat's setpoint temperature to increase or decrease the use of the HVAC system depending on whether the HVAC system is in heating mode or cooling mode. In some embodiments, a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2, causes a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, to control the HVAC system.

[0193] In block 2516, subsequent emission rate forecasts may be obtained from multiple emission rate forecasts. In some embodiments, the subsequent forecast is obtained after the thermostat has been instructed to control the HVAC system in accordance with the generated EDR event. In some embodiments, additional emission rate forecasts are received after the thermostat has been instructed to control the HVAC system in accordance with the start of the generated EDR event. In some embodiments, the subsequent forecast may include new predictions for the emission rate over the time scheduled for the EDR event to be running. For example, a previous forecast received before the start of the EDR event may predict that an increase in the emission rate will exist at the same time the EDR event is scheduled to end. However, the subsequent forecast may now predict that the increase will occur earlier or later than previously predicted.

[0194] In block 2518, the end time of an EDR event may be modified based on subsequent emission rate forecasts. In some embodiments, the modified EDR event is generated and sent to a thermostat, which may begin controlling the HVAC system according to the modified end time of the modified EDR event. In some embodiments, the end time of an ongoing EDR event is set to an earlier time based on subsequent forecasts. For example, if subsequent forecasts predict that the emission rate will rise faster than previously predicted, the end time of the EDR event may be set to coincide with the newly predicted time of the emission rate increase. In some embodiments, an ongoing EDR event may not be terminated until forecasts indicate that it would be too late to terminate the EDR event after waiting for the next available emission rate forecast. For example, if recent forecasts predict that the emission rate will increase over 5 minutes and the next available forecast will be received in 30 minutes, the system may terminate the EDR event to coincide with the predicted time of the emission rate increase. As another example, if a recent forecast predicts an increase in emission rates over 30 minutes and the next available forecast is received in 15 minutes, the system may wait to terminate the EDR event until after the next available forecast is received. In some embodiments, an ongoing EDR event is extended indefinitely until the maximum event duration is reached. For example, if each subsequent forecast predicts a later time for the forecast rate increase, the system may continue to extend the duration of the event until the maximum event duration limit is reached, at which point the system may terminate the EDR event when the maximum duration limit is reached.

[0195] In block 2520, the thermostat may be configured to control the HVAC system according to a modified EDR event. The thermostat may be configured to control the HVAC system according to any of the methods described above with respect to Figures 13 to 15. For example, at the start time of an EDR event, the EDR event may cause the thermostat to raise or lower its set temperature to increase or decrease the use of the HVAC system depending on whether the HVAC system is in heating mode or cooling mode. In some embodiments, a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2, causes a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, to control the HVAC system.

[0196] As illustrated above in Figures 4-25, EDR events may be generated and modified based on emission rate forecasts covering one or more hours into the future. Additional constraints may be used during generation and modification to help minimize the amount of user discomfort or irritation experienced by the user. However, some users may express more or less pleasure than others in tolerating the temperature control fluctuations resulting from their participation in EDR events. For example, some users may be keen to reduce their carbon footprint as much as possible by participating in all possible EDR events to the greatest possible degree. Conversely, other users may be willing to sacrifice a little comfort to reduce carbon emissions.

[0197] In some embodiments, these differences among users may be taken into account by providing user account participation levels. For example, users associated with a user account may select different participation levels, with lower participation levels corresponding to fewer and / or shorter duration EDR events between EDR events, and higher participation levels corresponding to more and / or longer duration EDR events. In some embodiments, the participation level may be set to unlimited for a period of time or less until the user associated with the user account corrects it. For example, the system may identify a time span such as several days or a week and provide the user with an opportunity to increase the user account's participation level for that span of time, after which the participation level reverts to the previous level. In some embodiments, the participation level may be determined by other actions taken by the user associated with the user account. For example, when adjustments to the setpoint temperature of a thermostat mapped to a user account are made between multiple EDR events, the system may identify the trend between each adjustment and correct the user account's participation level for future events. Determining the level of participation and generating events based on that level of participation will be discussed further in this specification with respect to Figures 26 to 30.

[0198] Figure 26 shows a graph 2600 of the weather forecast against historical emission rates for the same time of year. Graph 2600 represents the same x-axis 2604 and y-axis 2602 as graph 400 described above with respect to Figure 4. The right-hand vertical axis 2608 shows the expected average temperature in Fahrenheit. Graph 2600 shows historical emission rates 2612 over a given period. Graph 2600 also shows the current date 2616 and the average temperature forecast 2620.

[0199] In some embodiments, the system obtains actual emission rates for one or more cities or regions. For example, the historical data engine 215 of the cloud-based power control server system 110 may collect actual emission rates from a power company or third-party database. In some embodiments, the system may collect and store actual emission rates for each day of a calendar year for any number of past years. In some embodiments, The emission rates at any given time are analyzed to determine trends and average emission rates over time. For example, the system may determine the historical emission rate for each calendar day of a year based on the average emission rate for each of those days over the past few years. In some embodiments, the historical emission rate for a day is also obtained from various sources that collect and store actual emission rates.

[0200] In some embodiments, actual emission rates are analyzed to identify historical periods of higher emissions. Higher emissions may be defined as periods where emissions are, on average, 10% higher than the long-term average over a longer duration. For example, a given day may be defined as having higher emissions if it is expected to produce emissions at least 10% higher than for the monthly average. In other embodiments, the percentage may vary, such as 5%, 15%, 20%, or other larger or smaller values. Alternatively, higher emissions may be defined as periods where emissions are expected to be 10% higher than for the same period in the past. For example, a given week may be defined as having higher emissions if it is expected to produce emissions at least 10% higher than for the same week of the year in the previous year. In other embodiments, the percentage may vary, such as 5%, 15%, 20%, or other larger or smaller values.

[0201] In some embodiments, historical periods of higher emissions are analyzed to predict future periods of higher emissions. Future periods of higher emissions may be determined from repeated periods of higher emissions for the same period in previous years. For example, if emissions were generally higher at the end of July compared to the beginning of July, as shown by historical emission rate 2612, the system may determine that emissions are likely to be higher at the end of July in the future. In some embodiments, weather forecasts are used to improve the accuracy of the predicted future periods of higher emissions. For example, if a 10-day average high temperature forecast indicates a heatwave at the end of July, as shown by average temperature forecast 2620, the system may determine that predicted emissions are even more likely to be higher at the end of July.

[0202] In some embodiments, additional data, such as historical temperature, is used to improve accuracy. For example, if the predicted temperature is similar to the historical temperature, the system may determine that the emission rate for that period will also be similar to the historical emission rate. As another example, if the predicted temperature is higher or lower than the historical temperature, the system may determine that the actual emission rates for that period will be higher or lower than the historical rates, respectively. In some embodiments, these predictions may be made by various components of the cloud-based power control server system 110, such as the prediction engine 217.

[0203] In some embodiments, the prediction that higher emissions will occur during the extended period may be used as an opportunity to increase the quantity and magnitude of EDR events generated for that period. For example, the system may identify High Emissions Week 2624 as an opportunity to further reduce carbon emissions by generating more EDR events during High Emissions Week 2624, which typically occur during other periods of the year. High Emissions Week 2624 is approximately one week in this example, but any appropriate period such as 5 days, one week, two weeks, and / or one month may be used.

[0204] In some embodiments, users associated with a user account can increase their level of participation in EDR events during periods when higher emissions are expected. For example, a user management module 216 of a cloud-based power control server system 110 may send a notification to a user account, and a linked smart thermostat may notify the user that there is a period of expected high emissions and instruct the user associated with the account to take action during that period. This provides an opportunity to increase the number or magnitude of EDR events in between.

[0205] In some embodiments, a user account with an increased level of participation in EDR events results in a thermostat linked to a user account that receives more EDR events per day. For example, instead of a thermostat that receives a maximum of three EDR events per day, the thermostat may receive up to six EDR events per day after the level of participation in EDR events associated with the user account has increased. In some embodiments, a user account with an increased level of participation in EDR events results in a thermostat linked to a user account that receives larger EDR events. For example, instead of receiving an EDR event with a maximum duration of one hour or a maximum setpoint deviation of two degrees, a thermostat linked to a user account with a higher level of EDR participation may receive an EDR event with a setpoint deviation greater than one hour and / or greater than two degrees.

[0206] In some embodiments, a user associated with a user account selects from two available participation levels. In other embodiments, there are three, four, five or more participation levels to select from. In some embodiments, a user associated with a user account can define the level of participation for their user account by individually increasing or decreasing specific settings, such as the maximum number of events per day, the maximum event duration, and / or the maximum setpoint temperature adjustment.

[0207] Figures 27A and 27B show a graph 2700 of the modified event participation levels based on canceled EDR events. Graph 2700 represents the same x-axis 2704 and y-axis 2702 and 2708 as graph 400 described above with respect to Figure 4. Graph 2700 also shows the thermostat setpoint temperature 2720 and the predicted emission rate 2716 with respect to time. Graph 2700 also shows the time 2730 (e.g., 6:00) before any scheduled EDR event is executed and the time 2732 (e.g., 12:00) after EDR event 2740 is executed. Multiple EDR events, such as EDR events 2740, 2744, and 2748, may be scheduled for a 24-hour period, as shown in Figure 27A. In some embodiments, the number of EDR events generated per day may be based on the participation level of a particular user account. For example, as shown in Figure 27A, a user account configured to an increased participation level may receive three EDR events per day instead of two per day. More generally, a user account configured to a higher level of participation may receive at least one additional EDR event within a given period, such as a day or a week, compared to a user account configured to a lower level of participation.

[0208] In some embodiments, an EDR event may be canceled by a person who overrides the thermostat's setpoint temperature while the event is in progress. For example, as shown in Figure 27B, after event 2740 has already started and the setpoint temperature 2720 has been increased, the person may adjust the setpoint temperature 2720 back to the previous setting. Thus, the person may cancel the EDR event at any point while the EDR event is running. For example, EDR event 2740 may be scheduled to end after two hours. However, the person may only begin to feel uncomfortable due to the temperature change after EDR event 2740 has progressed for one hour. There can be any number of reasons why a user might want to cancel an EDR event earlier. For example, some people may take longer or shorter to notice that the temperature has risen or fallen, in which case the event may be initially scheduled longer than is acceptable for that particular user account than for other user accounts.

[0209] In some embodiments, adjustments to the thermostat during an ongoing EDR event do not result in any change to the thermostat's or associated user account's participation in future EDR events. For example, an adjustment to the setpoint temperature 2720 during EDR event 2740 may only cancel the ongoing event, while all future events still occur as originally scheduled. As another example, if the setpoint temperature is adjusted in the same direction as the adjustment for an EDR event, the system may interpret this as a new setpoint temperature moving forward and make a deviation of the same magnitude from the new setpoint temperature for future events. However, an adjustment in the opposite direction (e.g., away from the EDR event deviation) may indicate that a person associated with the thermostat's user account will perform a similar adjustment during future EDR events, thereby reducing the system's ability to optimize carbon emission reductions.

[0210] In some embodiments, the EDR participation level associated with a user account is reduced based on adjustments during EDR events. For example, if a user associated with a user account chooses to participate in an increased number of EDR events over a given period, one or more adjustments to the setpoint temperature of the thermostat associated with the user account during an EDR event may be interpreted as an indication that the person associated with the user account no longer wishes to participate in an increased number of EDR events. For example, a thermostat associated with a user account with a higher participation level may begin receiving three EDR events per day, such as EDR events 2740, 2744, and 2748, as shown in Figure 27A. However, as shown in Figure 27B, after the person cancels EDR event 2740 before the scheduled end time, the system may reduce the user account's participation level by reducing the number of events per day to correspond to a lower participation level, such as two events per day, and may cancel any excess events (e.g., EDR event 2748).

[0211] In some embodiments, EDR events with longer durations are generated for user accounts set to a higher participation level. For example, the system may generate only EDR events with a maximum duration of 1 hour for user accounts set to a lower participation level, while EDR events generated for user accounts set to an increased participation level may have a maximum duration of 3 hours. In some embodiments, the duration of an EDR event may be based on adjustment to a setpoint temperature. For example, as shown in Figure 27A, a user account set to an increased participation level for longer-duration EDR events may receive EDR events 2740 and 2744 scheduled with a duration of at least 2 hours. However, as shown in Figure 27B, someone may override EDR event 2740 after only 1 hour of execution by adjusting the thermostat's setpoint temperature. Based on the adjustment to the setpoint temperature, the system may determine that future events should not have a longer duration than the elapsed time of EDR event 2740 before it was overridden. As shown in Figure 27B, the system may shorten the duration of EDR event 2744 to the same or similar duration as EDR event 2740 before it was overridden. In some embodiments, future events are similarly adjusted and / or EDR events are generated only with the new, forward-moving duration.

[0212] Figures 28A and 28B show graph 2800 of the modified event participation level based on user input during emission demand response events. Graph 2800 represents the same x-axis 2804 and y-axis 2802 and 2808 as graph 400 described above with respect to Figure 4. Graph 2800 also shows the thermostat setpoint temperature 2820 and the predicted emission rate 2816 with respect to time. Graph 2800 shows all scheduled EDR events The time before the event is executed (e.g., 6:00) and the time after EDR event 2840 is executed (e.g., 12:00) are also shown. Multiple EDR events, such as EDR events 2840 and 2844, may be scheduled for a 24-hour period, as shown in Figure 28A.

[0213] In some embodiments, adjusting the thermostat to a setpoint temperature while an EDR event is in progress adjusts the magnitude of the EDR event for the remaining EDR events. For example, as shown in Figure 28B, after event 2840 has already started with an initial adjustment to the setpoint temperature 2820 (e.g., a 3-degree offset), the person may adjust the setpoint temperature 2820 by only less than the magnitude of EDR event 2840 (e.g., less than 3 degrees). In some embodiments, the adjustment is interpreted as canceling the EDR event and setting a new setpoint temperature. For example, if the thermostat's setpoint temperature is adjusted to be lowered by 2 degrees, the setpoint may remain at that temperature after the EDR event has been scheduled to end. In other embodiments, the adjustment is interpreted as subtracting the offset associated with the currently ongoing EDR event. For example, the setpoint temperature may remain at the new temperature only until the scheduled event ends, and then return to the original setpoint after the EDR event has ended.

[0214] In some embodiments, the setpoint temperature offset of a generated EDR event is based on the participation level of a particular user account. For example, as shown in Figure 28A, EDR events 2840 and 2844 may be generated with a larger adjustment (e.g., 3 degrees) to the setpoint temperature 2820 for a user account set to a higher participation level compared to a user account set to a lower participation level. In some embodiments, the participation level of a user account in an EDR event is modified based on adjustments to the setpoint temperature made during the EDR event. For example, as shown by Figure 28B, the system may reduce the user's participation level based on adjustments made to EDR event 2840 after the event started. In some embodiments, a reduction in the user account's participation level to a future EDR event results in a reduction of the setpoint temperature offset for that future event. For example, as shown in Figures 28A and 28B, the setpoint temperature offset of EDR event 2844 may be reduced after the system has determined that adjustments were made during EDR event 2840. In some embodiments, both the setpoint temperature offset and duration of future events are reduced based on adjustments to the setpoint temperature.

[0215] To implement the EDR events detailed above with respect to Figures 26 to 28B, various methods may be used with respect to the systems detailed above in Figures 1 to 3. Figure 29 shows an embodiment of method 2900 for generating emission demand response events based on user account participation levels. In some embodiments, method 2900 may be performed by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. For example, the processing system 219 of the cloud-based power control server system 110 may execute software from one or more modules, such as an event scheduler 213, a constraint engine 214, a history data engine 215, a user management module 216, and / or a forecast engine 217. In some embodiments, various steps of method 2900 may be performed by smart devices, such as a smart thermostat 160 described above with respect to Figure 3. For example, the processing system 319 of the smart thermostat 160 may execute software from one or more modules such as an event scheduler 314 and a constraint engine 315. In some embodiments, some steps of method 2900 may be performed by a cloud-based power control server system such as a cloud-based power control server system 110, while other steps may be performed by a smart server This is performed by smart devices such as the Mosta 160.

[0216] Method 2900 may include obtaining emission rate history in block 2910. In some embodiments, a cloud-based power control server system may obtain emission rate history. For example, the history data engine 215 of the cloud-based power control server system 110 may obtain emission rate history. In some embodiments, emission rate history may be obtained from one or more third-party sources. For example, the cloud-based power control server system 110 may obtain emission rate history from the emission data system 120 or any number of power companies that provide electricity to a city or region. In some embodiments, emission rate history may be obtained from recorded emission rates over a predetermined period. For example, the history data engine 215 may record actual emission rates when they occur and store them in a database or similar data store. In some embodiments, emission rate history may span recorded emission rates over a year or more. In some embodiments, historical emission rates may be expressed as average historical emission rates per day, per week, or per month over a year. For example, the average historical emission rate for a given day in a given year may be determined based on the recorded emission rates for that day over the past three, five, or ten years or more.

[0217] In block 2912, future periods of predicted higher emissions may be identified based on historical emission rates. Higher emissions may be defined as a period in which emissions are, on average, 10% higher than the long-term average over a longer duration. For example, a given day may be defined as having higher emissions if it is expected to produce emissions at least 10% higher than the monthly average. In other embodiments, the percentage may be varied, such as 5%, 15%, 20%, or other larger or smaller values. In some embodiments, the system uses historical emission rates to identify future periods of predicted higher emissions. For example, the historical data engine 215 of the cloud-based power control server system 110 may analyze historical emission rates and identify trends in historical emission rates that are likely to repeat in the future. In some embodiments, future periods of predicted high emissions may be based on a week of the year in which higher emission rates have been observed in the past. For example, if the last week of July has historically had higher emission rates than the peripheral period of the year, the system may identify that same period in the future as having a high probability of higher emissions.

[0218] In some embodiments, identifying future periods of predicted high emissions may be based on additional factors such as weather. For example, the last week of July may historically be the hottest period of the year and therefore be associated with a historical increase in emissions rates during that period of the year. Similarly, the beginning of January may historically be the coldest period of the year and therefore be associated with a historical increase in emissions rates due to increased heater use. In some embodiments, weather forecasts may be used to improve the accuracy of identifying future periods of predicted high emissions. For example, when historical temperature and emissions rates are associated with higher-than-average emissions rates during a certain period of the year, and the weather forecast indicates that temperatures will be higher for that future period, the system may determine that there is a higher probability that actual emissions during that period will be the same as or higher than the historical emissions rate for that period. Similarly, if the weather forecast indicates that temperatures will be lower than the historical average, the system may determine that there is a lower probability that actual emissions during that period will be as high as historical emissions rates.

[0219] In block 2914, the user account participation level may be determined for a future period of projected high emissions. In some embodiments, there are one or more available participation levels to reduce carbon emissions through EDR events. For example There may be a basic entry level of participation and more advanced or rigorous levels of participation. While two levels of participation are described here as examples, it should be understood that there may be additional levels and gradients between levels that apply to each individual user account. For example, the participation level may be defined by increasing or decreasing individual settings of the user account, such as the maximum number of EDR events per day, the maximum EDR event duration, and / or the maximum set temperature offset per EDR event. In some embodiments, the user sets the participation level of the user account through an application installed on a computerized device such as a smartphone or tablet computer. In some embodiments, the user account is managed by a user management module 216 of a cloud-based power control server system 110, as described above with respect to Figure 2.

[0220] In some embodiments, participation levels may be applied to a user account indefinitely. For example, when a user account is created, a desired participation level is selected and remains active until the user associated with the account modifies the participation level. In some embodiments, a given participation level expires after a predetermined period. For example, an increased participation level may be applied only to periods of predicted higher emissions, such as those identified and described above with respect to block 2912. After the period of predicted higher emissions, the user account's participation level reverts to the previous or original setting. In some embodiments, after identifying future periods of predicted higher emissions, a user account may receive a request or invitation to increase its participation level for generated EDR events. For example, the user management module 216 may send a notification to a mobile device 140 associated with a user account, such as those described above with respect to Figure 2. In some embodiments, input received in response to a request to increase the participation level is stored as a preference or setting associated with the user account. In some embodiments, the user account setting is used to determine the user account's participation level for future periods of predicted higher emissions. For example, the user management module 216 may search for settings from user accounts related to the account's participation level.

[0221] In block 2916, EDR events may be generated based on the user account participation level. In some embodiments, the user account participation level affects the generation of EDR events for devices associated with the user account. For example, constraints on generating events, as described above with respect to Figures 10 and 11, may differ based on the participation level of a particular user account. In some embodiments, an increased or higher participation level is associated with a higher maximum number of EDR events per day. For example, if the baseline constraint limits the number of EDR events generated per day to 3 or fewer, the constraint for a higher participation level may allow the generation of up to 6 EDR events per day. In some embodiments, an increased or higher participation level is associated with generating EDR events of a larger magnitude. For example, if the baseline constraint limits the setpoint temperature adjustment associated with generated EDR events to 2 degrees or less, an increased participation level may allow events with a setpoint temperature adjustment of up to 4 degrees. In some embodiments, an increased or higher participation level is associated with generating EDR events of a larger duration. For example, if a baseline constraint limits the generation of EDR events with a duration greater than 2 hours, a constraint associated with a higher participation level may limit only events with a duration greater than 4 hours. In some embodiments, an increased or higher participation level is associated with an increase in any combination of the above factors. For example, a user account set to a higher participation level may receive more EDR events that are longer-lasting and have larger setpoint temperature adjustments.

[0222] In some embodiments, EDR events may be generated according to any of the methods described above with respect to Figures 13 to 15. For example, an EDR event may be generated at an end time corresponding to the time of the emission differential value calculated from a first emission rate forecast. The first emission rate forecast may be any emission rate forecast received at any time. In some embodiments, an EDR event may be generated by an event scheduler 213 of a cloud-based power control server system 110, as described above with respect to Figure 2. In some embodiments, an EDR event may be generated by an event scheduler 314 of a smart thermostat 160, as described above with respect to Figure 3. An EDR event may be a preemptive EDR event or a deferred EDR event.

[0223] In block 2918, a thermostat associated with a user account may be configured to control the HVAC system according to a modified EDR event. The thermostat may be configured to control the HVAC system according to any of the methods described above with respect to Figures 13 to 15. For example, the EDR event may cause the thermostat to raise or lower its set temperature in order to increase or decrease the use of the HVAC system depending on whether the HVAC system is in heating mode or cooling mode at the start time of the EDR event. In some embodiments, a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2, may cause a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, to control the HVAC system.

[0224] Figure 30 shows an embodiment of Method 3000 for correcting user account participation levels based on adjustments to a setpoint temperature during an EDR event. In some embodiments, Method 3000 may be performed by any or all of the same components as described above with respect to Method 2900 with respect to Figure 29. Method 3000 may include obtaining emission rate history in block 3010. In some embodiments, a cloud-based power control server system may obtain emission rate history. For example, the history data engine 215 of the cloud-based power control server system 110 may obtain emission rate history. In some embodiments, emission rate history may be obtained from one or more third-party sources. For example, the cloud-based power control server system 110 may obtain emission rate history from emission data system 120 or any number of power companies that provide electricity to a city or region. In some embodiments, emission rate history may be obtained from recorded emission rates over a predetermined period. For example, the history data engine 215 may record actual emission rates as they occur and store them in a database or similar data store. In some embodiments, the emission rate history may span a year or several years of recorded emission rates. In some embodiments, the historical emission rate may be expressed as an average historical emission rate per day, per week, or per month of the year. For example, the average historical emission rate for a given day of the year may be determined based on the recorded emission rate for that day of the year over the past three, five, or ten years or more.

[0225] In block 3012, future periods of predicted higher emissions may be identified based on historical emission rates. Higher emissions may be defined as periods in which emissions are, on average, 10% higher than the long-term average over a longer duration. For example, a given day may be defined as having higher emissions if emissions are expected to be at least 10% higher than for the monthly average. In other embodiments, the percentage may be varied, such as 5%, 15%, 20%, or other larger, intermediate, or smaller values. In some embodiments, the system uses historical emission rates to identify future periods of predicted higher emissions. For example, the historical data engine 215 of the cloud-based power control server system 110 analyzes historical emission rates and identifies future periods of predicted higher emissions. Trends in historical emission rates that are likely to repeat may be identified. In some embodiments, future periods of predicted high emissions may be based on weeks of the year in the past where higher-than-usual emission rates were observed. For example, if the last week of July has historically had higher emission rates than the surrounding period of the year, the system may identify the same period in the future as having a high probability of higher emissions.

[0226] In some embodiments, identifying future periods of predicted high emissions may be based on additional factors such as weather. For example, the last week of July may historically be the hottest period of the year and therefore related to an increase in historical emissions rates for that period of the year. Similarly, the beginning of January may historically be the coldest period of the year and therefore related to an increase in historical emissions rates due to increased heater use. In some embodiments, weather forecasts may be used to improve the accuracy of identifying future periods of predicted high emissions. For example, if historical temperature and emission rates indicate that a certain period of the year is associated with higher-than-average emissions rates, and the weather forecast indicates that temperatures will be higher for that future period, the system may determine that actual emissions during that period are likely to be the same as or higher than the historical emissions rates for that period. Similarly, if the weather forecast indicates that temperatures will be lower than the historical average, the system may determine that actual emissions during that period are less likely to be as high as the historical emissions rates.

[0227] In block 3014, the participation level of a user account may be determined for a future period of projected high emissions. In some embodiments, there may be one or more available participation levels to reduce carbon emissions through EDR events. For example, there may be a basic entry participation level and more advanced or stricter participation levels. While two participation levels are described here as examples, it should be understood that there may be additional levels and gradients between levels that apply to each individual user. For example, participation levels may be defined by increasing or decreasing individual settings of a user account, such as the maximum number of EDR events per day, the maximum duration of an EDR event, and / or the maximum setpoint temperature offset per EDR event. In some embodiments, the user sets the participation level of the user account through an application installed on a computerized device such as a smartphone or tablet computer. In some embodiments, the user account is managed by a user management module 216 of a cloud-based power control server system 110, as described above with respect to Figure 2.

[0228] In some embodiments, participation levels may be applied to user accounts indefinitely. For example, when a user account is created, a desired participation level is selected and remains active until the user associated with the account modifies the participation level. In some embodiments, a given participation level expires after a predetermined period. For example, an increased participation level may be applied only for periods of predicted higher emissions, such as those identified and described above in relation to block 3012. After periods of predicted higher emissions, the user account's participation level reverts to the previous or original setting. In some embodiments, after identifying future times of predicted higher emissions, a user account may receive a request or invitation to increase its participation level for generated EDR events. For example, the user management module 216 may send a notification to the mobile device 140 associated with the user account as described above with respect to Figure 2. In some embodiments, input received in response to a request to increase the participation level is stored as a preference or setting associated with the user account. In some embodiments, the user account setting is used to determine the user account's participation level for predicted higher future periods. For example, the user management module Rule 216 may search for settings from user accounts related to the account's participation level.

[0229] In block 3016, EDR events may be generated based on the user account participation level. In some embodiments, the user account participation level affects the generation of EDR events for devices associated with the user account. For example, as described above with respect to Figures 10 and 11, the constraints on generating events may differ based on the participation level of a particular user account. In some embodiments, an increased or higher participation level is associated with a higher maximum number of EDR events per day. For example, if the baseline constraint limits the number of EDR events generated per day to 3 or fewer, the constraint for a higher participation level may allow the generation of up to 6 EDR events per day. In some embodiments, an increased or higher participation level is associated with generating EDR events of a larger magnitude. For example, if the baseline constraint limits the setpoint temperature adjustment associated with a generated EDR event to 2 degrees or less, an increased participation level may allow events with a setpoint temperature adjustment of up to 4 degrees. In some embodiments, an increased or higher participation level is associated with generating EDR events of a larger duration. For example, if a baseline constraint limits the generation of EDR events with a duration greater than 2 hours, a constraint associated with a higher participation level may limit only events with a duration greater than 4 hours. In some embodiments, an increased or higher participation level is associated with an increase in any combination of the above factors. For example, a user account set to a higher participation level may receive more EDR events that are longer-lasting and have larger setpoint temperature adjustments.

[0230] In some embodiments, EDR events may be generated according to any of the methods described above with respect to Figures 13 to 15. For example, an EDR event may be generated at an end time corresponding to the time of the emission differential value calculated from a first emission rate forecast. The first emission rate forecast may be any emission rate forecast received at any time. In some embodiments, an EDR event may be generated by an event scheduler 213 of a cloud-based power control server system 110, as described above with respect to Figure 2. In some embodiments, an EDR event may be generated by an event scheduler 314 of a smart thermostat 160, as described above with respect to Figure 3. An EDR event may be a preemptive EDR event or a deferred EDR event.

[0231] In block 3018, a thermostat associated with a user account may be configured to control the HVAC system according to a modified EDR event. The thermostat may be configured to control the HVAC system according to any of the methods described above with respect to Figures 13 to 15. For example, at the start time of an EDR event, the EDR event may cause the thermostat to raise or lower its set temperature to increase or decrease the use of the HVAC system depending on whether the HVAC system is in heating mode or cooling mode. In some embodiments, a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2, may cause a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, to control the HVAC system.

[0232] In block 3020, adjustment to the setpoint temperature may be received during the execution of an EDR event. In some embodiments, the setpoint temperature is adjusted after the thermostat has raised or lowered the setpoint temperature in accordance with the EDR event, and before the thermostat restores the setpoint temperature to its original setting. For example, if an EDR event has raised the setpoint temperature by 2 degrees over a period of time, the thermostat may further raise the setpoint temperature. The set temperature may be adjusted by raising or lowering the set temperature. In some embodiments, the set temperature is adjusted manually in the thermostat or via remote communication with the thermostat. For example, a person may adjust a knob or dial on the surface of a thermostat, such as the smart thermostat 160 described above with respect to Figure 3. As another example, a user associated with a user account linked to the thermostat may adjust the set temperature through an application on a mobile device, such as the mobile device 140 described above with respect to Figure 1.

[0233] In some embodiments, adjusting the setpoint temperature during an EDR event cancels the execution of the EDR event. For example, if an EDR event was scheduled to raise the setpoint temperature by 2 degrees over two hours, the EDR event may be canceled by lowering the setpoint temperature by 2 degrees before the end of the two hours. In some embodiments, adjustments during an EDR event only modify the remainder of the EDR event. Using the same example, if the setpoint temperature is lowered by only 1 degree, the setpoint temperature may remain at that temperature until the end of the scheduled event, at which point the setpoint temperature may return to its original setpoint temperature.

[0234] In block 3022, the participation level of a user account may be modified based on adjustments to the setpoint temperature. In some embodiments, one or more adjustments that cancel or modify ongoing EDR events are used as a basis for modifying the participation level of a user account. For example, after several EDR events have been canceled in succession, the system may reduce the participation level of a user account. In some embodiments, the participation level of a user account is reduced gradually and / or based on a predetermined trend identified in several adjustments to the setpoint temperature while the EDR events are progressing. For example, if a user account is set to an increased participation level resulting in more EDR events with longer durations (e.g., 2 hours), and several consecutive events are canceled after a shorter period (e.g., 1 hour), the system may continue to generate the same number of events, but with shorter durations (e.g., 1 hour). In some embodiments, one or more adjustments are used as a basis for reducing the participation level of a user account to a previous or original participation level. For example, if a user account is configured to participate in increased EDR event activity for a week, the system may identify one or more canceled events and configure the user account to no longer participate in increased EDR event activity for that week. In some embodiments, a notification may be sent to the user account before reducing the participation level. For example, a user management module 216 of a cloud-based power control server system 110 may send a notification to a mobile device 140 associated with the user account requesting verification that the user account should or should not remain at the same participation level.

[0235] In some embodiments, additional factors and data are used to generate and execute EDR events, such as confidence values ​​and / or expected variability. These and other features in some embodiments are further discussed herein with respect to Figures 31-37. Figure 31 shows a graph 3100 of emission demand response events based on the magnitude of future emission rate events. Graph 3100 represents the same x-axis 3104 and y-axis 3102 and 3108 as graph 400 described above with respect to Figure 4. Graph 3100 shows the predicted emission rates 3116 over a given period. Graph 3100 also shows the thermostat setpoint temperature 3120. The system may have already generated EDR events 3140 and 3142, as shown in graph 3100 by deviations from the setpoint temperature 3120.

[0236] In some embodiments, EDR events are generated based on future emission rate events. A future emission rate event may be any future period in which an increase or decrease in emission rates is expected. The increase or decrease may be based on any appropriate measure, such as a deviation from the ongoing average emission rate for a previous period. For example, if the average emission rate over the last one, two, or three weeks or more was a predetermined amount, a 10% deviation in the emission rate may be considered an increase or decrease in emissions. In other embodiments, the deviation may be a deviation of 10%, 20%, 30%, or other percentages from the average emission rate. A future emission rate event may also be defined as a period in which the rate of change in emission levels, or the emission differential, is higher or lower than a threshold. The ongoing average emission rate may be more or less specific, such as the average emission rate for a given time of day based on the average emission rate for the same time of day in the past. A future emission rate event may also be a time in which an expected increase or decrease in emission rates exists, based on an expected emission differential or other estimate of the rate of change in emission rates. In some embodiments, after identifying future emission rate events, EDR events are generated to match the future emission rate events as further described above with respect to Figures 4 to 15.

[0237] In some embodiments, EDR events are generated based on the shape or magnitude of a future emission rate event. The shape or magnitude of a future emission rate event may be the amount of time the predicted emission rate is expected to be at an increased or decreased level and / or the amount of deviation from a threshold emission rate value. For example, an increased level of emissions lasting two hours may be considered to be of a greater magnitude than the same increased level of emissions lasting only one hour. As another example, an increase in emission rate of 600 lbs-CO2 / MWh lasting one hour may be considered to be of a greater magnitude than an increase of 200 lbs-CO2 / MWh lasting one hour.

[0238] In some embodiments, EDR events are generated with different shapes or sizes. The shape or size of an EDR event may be the size of the adjustment of the thermostat to the setpoint temperature and / or the amount of time over which the setpoint temperature is adjusted. For example, an EDR event that adjusts the setpoint temperature by only 3 degrees over 2 hours may be considered to have a larger size than an EDR event that adjusts the setpoint temperature by only 1 degree over 1 hour.

[0239] In some embodiments, the shape or magnitude of an EDR event is based on the shape or magnitude of a future emission rate event. More generally, future emission rate events having a magnitude greater than a threshold magnitude may result in an increase in the duration, setpoint adjustment, or both of the EDR event. For example, as shown in Figure 31, EDR event 3140 may be generated with a certain magnitude (e.g., an offset of 1 degree for 1 hour) to correspond to a smaller future emission rate event. Similarly, EDR event 3142 may be generated with a larger magnitude than EDR event 3140 (e.g., an offset of 3 degrees for 3 hours) to correspond to a larger future emission rate event.

[0240] Figure 32 shows a graph 3200 of predicted discharge data against decreasing confidence values. Graph 3200 represents the same x-axis 3204 and y-axis 3202 as graph 400 described above with respect to FIG. 4. Graph 3200 shows a predicted discharge rate 3216 over a given period. Graph 3200 also shows a confidence value 3228 as a measure of the certainty in the predicted discharge rate 3216 as it is predicted to occur over time. The right vertical axis 3208 indicates percentage confidence.

[0241] In some embodiments, a confidence value is obtained for a predicted discharge rate in a prediction. The confidence value may measure the certainty of an actual discharge rate that matches the predicted discharge rate when it is predicted to occur. The confidence value may also measure the certainty of the actual rate of change of the discharge rate as quantified by a discharge differential that matches the predicted rate of change. The confidence value may be in any form of measurement, such as the percentage likelihood of a discharge rate occurring at the same rate as predicted. For example, a 90% confidence value may indicate a high likelihood that the actual discharge rate will occur as predicted, whereas a 30% confidence value may indicate a low likelihood that the actual discharge rate will occur as predicted. In some embodiments, the confidence value is obtained from a third-party source such as the discharge data system 120 as further described above with respect to FIG. 1. 測定してよい。信頼値は、予測変化率と一致する排出ディファレンシャルによって定量化されたような排出率の実際の変化率の確実性をも測定し得る。信頼値は、予測されたのと同じ率で生じる排出率のパーセンテージ可能性など、測定のあらゆる形式であってよい。例えば、90%の信頼値は、実際の排出率が予測されたとおりに生じる高い可能性を示してよいのに対し、30%の信頼値は、実際の排出率が予測されたとおりに生じる可能性が低いことを示してよい。幾つかの実施形態において、信頼値は、図1に関して上記でさらに説明したように排出データシステム120などの第三者ソースから取得される。

[0242] In some embodiments, the confidence value is based on a time decay applied to the predicted discharge rate when the predicted discharge rate is received or generated. The time decay may be a measure of the rate of decline of the confidence value over time, such that the confidence value decreases at a certain rate over time. The decay rate may be any suitable rate, such as 5, 10, 15 percent or more per hour. For example, as shown by Figure 32, the confidence value 3228 may start at 90% at the time 3224 (e.g., 6:00) when the predicted discharge rate 3216 is received and may decrease to about 20% by the expected end (e.g., 00:00). Although a linear decay rate is shown in Figure 32, any other suitable decay rate, such as parabolic or exponential, may be applied to the predicted discharge rate in the prediction. In some embodiments, one or more modules in the cloud-based power control server system 110 may determine a confidence value for the predicted discharge rate, such as the historical data engine 215 and / or the prediction engine 217 as described above with respect to Figure 2.

[0243] In some embodiments, the confidence value is determined for a future discharge rate event. The confidence value for a future discharge rate event may be the average confidence value over the duration of the future discharge rate event. For example, if the confidence value at the start of a 1-hour future discharge rate event is 90% and the confidence value decays at a rate of 10% per hour (i.e., the confidence value at the end of the future discharge rate event is 80%), the confidence value for the future discharge rate event may be 85% (i.e., the average of 90% and 80%). In some embodiments, the confidence value for a future discharge rate event is the confidence value at the start or end of the future discharge rate event.

[0244] Figure 33 shows graph 3300 of emission demand response events generated based on confidence values. Graph 3300 represents the same x-axis 3304 and y-axis 3302 and 3308 as graph 400 described above with respect to Figure 4. Graph 3300 shows the time 3324 when the predicted emission rate 3316 was received. Graph 3300 also shows the thermostat setpoint temperature 3320. The system may have already generated EDR events 3340 and 3342 as shown in graph 3300 by deviation from the setpoint temperature 3320. Graph 3300 also shows confidence values ​​3328 as a measure of certainty that the predicted emission rate 3316 will occur as predicted over time.

[0245] In some embodiments, the shape or magnitude of an EDR event is based on a confidence value associated with a future emission rate event. For example, if the confidence value for a future emission rate event is greater than a threshold confidence value, the magnitude of the EDR event may be increased. Similarly, if the confidence value for a future emission rate event is lower than a threshold confidence value, the magnitude of the EDR event may be decreased. As described above with respect to Figure 31, increasing or decreasing the magnitude may include increasing or decreasing the duration of the EDR event and / or increasing or decreasing the size of the thermostat adjustment to the setpoint temperature. For example, as shown in Figure 33, EDR event 3340 has a larger setpoint adjustment (e.g., 3 degrees instead of 2 degrees) because the confidence value associated with the future emission rate event used to generate EDR event 3340 was greater than a threshold confidence value. Similarly, EDR event 3342 has a smaller setpoint adjustment (e.g., 1 degree instead of 2 degrees) because the future emission used to generate EDR event 3342 This is because the confidence value associated with the rate event was smaller than the threshold confidence value. In some embodiments, confidence values ​​are used to adjust the event score, as described above with respect to Figure 9. For example, a potential event may get a higher score if the confidence value is higher, which increases the likelihood that it is one of the actual scheduled events.

[0246] In some embodiments, there may be one or more thresholds related to various EDR event magnitudes. For example, if the confidence level is higher than 75%, an EDR event may be generated with a 3-degree setpoint adjustment, while a confidence level lower than 75% but higher than 50% may be generated with a 2-degree adjustment, and a confidence level lower than 50% may be generated with only a 1-degree adjustment to the setpoint temperature.

[0247] Figure 34 shows graph 3400 of the end times of multiple emission demand response events based on confidence values. Graph 3400 represents the same x-axis 3404 and y-axis 3402 and 3408 as graph 400 described above with respect to Figure 4. Graph 3400 shows the time 3424 when the predicted emission rate 3416 was received. Graph 3400 also shows the thermostat setpoint temperature 3420. Graph 3400 shows the confidence value 3428 as a measure of the certainty that the predicted emission rate 3416 occurs as predicted over time. Graph 3400 also shows the end times of potential EDR events 3438, 3440, and 3442.

[0248] In some embodiments, multiple different EDR events are generated for future emission rate events. After identifying future emission rate events, the system may generate a first EDR event for one or more thermostats and a second EDR event for one or more other thermostats, having different characteristics from the first. The different characteristics may include the size of the adjustment of the thermostat to the setpoint temperature and / or the duration of the EDR event. In some embodiments, the magnitude of the EDR events differs due to different constraints within the user account, as described above with respect to Figures 4 to 15. In some embodiments, different EDR events are generated by different user account participation levels, as described above with respect to Figures 26 to 30.

[0249] In some embodiments, multiple EDR events are generated with different start and / or end times for a future emissions event based on confidence levels associated with that event. This may be due to the uncertainty involved in predicting when emissions increases / decreases will occur. When the confidence level is lower, there may be a greater chance that the emissions event will end earlier or later than currently predicted. For example, a future emissions event with a predicted end time of 15:00 and a 50% confidence level may end 5, 10, 15 minutes or more earlier or later than 15:00. When the confidence level is lower than the threshold confidence level, one or more additional EDR events may be generated with different end times. For example, as shown in Figure 34, multiple EDR events with event end times 3438, 3440, and 3442 may be generated around the same time because the confidence level is lower than the threshold confidence level (e.g., less than 50%).

[0250] In some embodiments, the number of different EDR events is based on a confidence level for a future emission rate event. If the confidence level for a future emission rate event is lower than a threshold confidence level, the number of generated EDR events may increase by at least one. For example, a confidence level of over 50% for a future emission rate event may result in the generation of one EDR event, such as an EDR event with event end time 3440, while a confidence level lower than 50% may result in the generation of additional EDR events with different end times, such as event end times 3438 and 3442.

[0251] In some embodiments, multiple different EDR events for future emission rate events are distributed to available thermostats or similar devices. The distribution of different EDR events may be the percentage of devices that receive each different EDR event. For example, if there are 100 available devices for three different EDR events, an even distribution may occur when the number of available devices receiving one of the EDR events is equal to the number of devices receiving each of the other EDR events. On the other hand, a smaller distribution may mean that more devices receive one of the EDR events than the other EDR events. In some embodiments, the distribution of different EDR events is based on confidence values ​​for the future emission rate events. In some embodiments, if the confidence value of a future emission rate event is smaller than a threshold confidence value, the distribution increases toward an even distribution.

[0252] Figure 35 shows graph 3500 of emission demand response events with stepwise adjustment to the setpoint temperature. Graph 3500 represents the same x-axis 3504 and y-axis 3502 and 3508 as graph 400 described above with respect to Figure 4. Graph 3500 shows the predicted emission rate 3516 over a given period. Graph 3500 also shows the thermostat setpoint temperature 3520. The system may have already generated an EDR event 3540 due to a deviation from the setpoint temperature 3520, as shown in graph 3500.

[0253] In some embodiments, an EDR event causes the thermostat to adjust the setpoint temperature once or multiple times during the EDR event. For example, as shown in Figure 35, an EDR event 3540 includes a first setpoint adjustment of about 3 degrees for the first part of the event (e.g., from 20 to 23) before reducing the adjustment by about half (e.g., from 23 to 21.5). In some embodiments, the different adjustments during an EDR event are based on different predicted emission rates. As described above with respect to Figure 31, a larger increase or decrease in the emission rate may correspond to a larger adjustment of the thermostat to the setpoint temperature.

[0254] In some embodiments, the initial adjustment to the setpoint temperature is greater than the adjustment for the remainder of the EDR event to trigger a change in the state of the HVAC system. This may be due to the thermostat's hysteresis setpoint temperature. The hysteresis setpoint temperature may be a boundary temperature around a desired setpoint temperature that triggers the HVAC to change from an operating state to an idle state and from an idle state to an operating state. For example, if the desired setpoint temperature is 60 degrees, the thermostat in cooling mode may raise the ambient temperature to 61 degrees before turning on the HVAC system and may lower the ambient temperature to 59 degrees before turning off the HVAC system again.

[0255] When an HVAC system is already operating, a larger adjustment may be used to turn the HVAC system off earlier. For example, using the same setpoint temperature as above, if the HVAC system is operating in cooling mode and the ambient temperature is 60.9 degrees, a 1-degree increase in the setpoint temperature may not turn the HVAC system off because the new lower hysteresis setpoint temperature is 60 degrees (i.e., lower than the ambient temperature). However, a 2-degree adjustment will turn the HVAC system off because the new lower hysteresis setpoint temperature is 61 degrees (i.e., higher than the ambient temperature). Similarly, when an HVAC system is idle, a larger adjustment may be used to turn the HVAC system on earlier. To continue the example above, if the ambient temperature is 59.1 degrees, a 1-degree decrease in the setpoint temperature may not turn the HVAC system on because the new higher hysteresis setpoint temperature is 60 degrees (i.e., higher than the ambient temperature). However, a 2-degree adjustment will turn the HVAC system on. This is because the new, higher hysteresis setpoint temperature starts at 59 degrees (for example, lower than ambient temperature).

[0256] In some embodiments, EDR events adjust higher and / or lower hysteresis setpoint temperatures. For example, instead of adjusting a desired setpoint temperature of a thermostat, an EDR event may raise or lower higher and lower hysteresis setpoint temperatures by the same adjustment that would otherwise bring them to the desired setpoint temperature. In some embodiments, higher and lower hysteresis setpoint temperatures receive different adjustments based on the type of EDR event. For example, a deferred heating event or a preemptive cooling event may lower a lower hysteresis setpoint temperature by a first amount, while lowering a higher hysteresis setpoint temperature by a first amount. Similarly, a deferred cooling event or a preemptive heating event may raise a higher hysteresis setpoint temperature by a first amount, while lowering a lower hysteresis setpoint temperature by a first amount.

[0257] Figures 36A and 36B show graphs 3600 and 3601 of emission demand response events generated based on predicted variability. Graphs 3600 and 3601 represent the same x-axis 3604 and y-axis 3602 and 3608 as graph 400 described above with respect to Figure 4. Graphs 3600 and 3601 also show the thermostat setpoint temperature 3620 with respect to time. As shown by Figures 36A and 36B, the predicted emission rates 3616 and 3618 may have different amounts of emission rate variability.

[0258] In some embodiments, the emission rate variability value is determined based on the predicted emission rate forecast. The emission rate variability value may measure the relative variability of the predicted emission rate over a predetermined period, such as a forecast period. The emission rate variability value may be expressed as a percentage value or any other appropriate unit of measurement. In some embodiments, the emission rate variability value is a measure of the relative variability in the predicted emission rate forecast compared to the historical emission rate variability for a certain region. In some embodiments, the emission rate variability value is a measure of the relative variability of the predicted emission rate forecast compared to the variability for a certain region.

[0259] In some embodiments, a predetermined maximum number of EDR events per day is modified based on an emission variability value. More generally, the predetermined maximum number of EDR events per day may be increased by at least one event per day when the emission variability value is greater than a threshold variability value. For example, as shown in Figures 36A and 36B, four EDR events 3644, 3648, 3650, and 3652 may be generated based on a relatively high emission variability value associated with a predicted emission rate 3618, compared to only two EDR events 3640 and 3642, which were generated based on a relatively low emission variability value associated with a predicted emission rate 3616.

[0260] In some embodiments, the setpoint adjustment for EDR events is modified based on emission rate variability values. More generally, the temperature offset caused by an EDR event may be increased by at least 1 degree when the emission rate variability value is greater than the threshold variability value. For example, as shown in Figures 36A and 36B, EDR events 3644, 3648, 3650, and 3652 may be generated with a 3-degree offset from the setpoint temperature based on a relatively high emission rate variability value associated with the predicted emission rate 3618, compared to only 2 degrees for EDR events 3640 and 3642 which were generated based on a relatively low emission rate variability value associated with the predicted emission rate 3616.

[0261] In some embodiments, a predetermined maximum EDR event duration is modified based on emission rate variability. More generally, the predetermined maximum EDR event duration may be increased by at least 5 minutes, 30 minutes, or 60 minutes per event when the emission rate variability is less than a threshold variability. For example, as shown in Figures 36A and 36B. Therefore, EDR events 3640 and 3642 may be generated with a duration greater than 2 hours based on a relatively low emission variability value associated with the predicted emission rate 3616, compared to only 1 hour for EDR events 3644, 3648, 3650, and 3652 based on a relatively high emission variability value associated with the predicted emission rate 3618. In some embodiments, a predetermined maximum number of EDR events per day and a predetermined maximum EDR event duration are inversely correlated based on the emission variability value. For example, when the emission variability value is greater than a threshold variability value, a predetermined maximum number of EDR events per day is increased, while a predetermined maximum EDR event duration is decreased.

[0262] Various methods may be used to implement EDR events, as detailed above with respect to Figures 31 to 36B, using the systems detailed above in Figures 1 to 3. Figure 37 shows an embodiment of method 3700 for shaping emission demand response events based on expected emission rate confidence values. In some embodiments, method 3700 may be performed by a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2. For example, the processing system 219 of the cloud-based power control server system 110 may execute software from one or more modules, such as an event scheduler 213, a constraint engine 214, a history data engine 215, a user management module 216, and / or an expectation engine 217. In some embodiments, various steps of method 3700 may be performed by smart devices, such as a smart thermostat 160 described above with respect to Figure 3. For example, the processing system 319 of the smart thermostat 160 may execute software from one or more modules, such as an event scheduler 314 and a constraint engine 315. In some embodiments, some steps of method 3700 may be performed by a cloud-based power control server system, such as a cloud-based power control server system 110, while other steps are performed by a smart device, such as the smart thermostat 160.

[0263] Method 3700 may include, at block 3710, obtaining an emission rate forecast for a predetermined future period. The emission rate forecast may include a predicted carbon emission rate over a predetermined future period. The carbon emission rate may be measured in lbs-CO2 / MWh or any similar unit of measurement. The predetermined future period may be any number of hours including the next 24 hours. The emission rate forecast may be received from a commercial service that collects and analyzes emission rate data from various sources such as a power company that provides electricity to a city or region. In some embodiments, the emission rate forecast may be generated by a cloud-based power control server system using data collected from one or more sources such as a power company and a weather forecasting agency. In some embodiments, the emission rate forecast may be received by a cloud-based power control server system such as cloud-based power control server system 110 as described above with respect to FIG. 2. The emission rate forecast may also be received by a smart thermostat. In some embodiments, a smart thermostat such as smart thermostat 160 as described above with respect to FIG. 3 may receive the emission rate forecast from cloud-based power control server system 110.

[0264] At block 3712, future emission rate events may be identified based on the emission rate forecast. A future emission rate event may be any period in the future when the emission rate is expected to be at an increased or decreased level. The increased or decreased level may be based on any suitable measure such as a deviation from the ongoing average emission rate for the previous period. For example, an increased or decreased level of emissions may be identified when there is a 10% deviation from the average emission rate over the last one, two, three or more weeks. In other embodiments, the deviation may be a 10%, 20%, 30%, or other percentage deviation from the average emission rate. The ongoing average emission rate is based on the average emission rate at the same time of day in the past. It may be more or less specific, such as the average emission rate for a particular time of day.

[0265] In some embodiments, future emission rate events are identified by a cloud-based power control server system. For example, the forecasting engine 217 of the cloud-based power control server system 110 may analyze emission rate forecasts to identify future emission rate events. In some embodiments, the cloud-based power control server system determines the shape or magnitude of a future emission rate event. The shape or magnitude of a future emission rate event may be the amount of time during which the predicted emission rate is expected to be at an increased or decreased level, and / or the amount of deviation from a threshold emission rate value, such as the average emission rate over time. For example, an increased level of emissions lasting for two hours may be considered to have a greater magnitude than the same increased level of emissions lasting for only one hour.

[0266] In block 3714, confidence values ​​may be determined for future emission rate events. Confidence values ​​may measure the certainty that actual emission rates will match predicted emission rates over the course of future emission rate events. Confidence values ​​may be any form of measurement, such as the percentage probability of emission rates occurring at the same rate as predicted. For example, a 90% confidence value may indicate a high probability that actual emission rates will occur as predicted, while a 30% confidence value may indicate a low probability that actual emission rates will occur as predicted. In some embodiments, confidence values ​​are obtained from a third-party source, such as the emission data system 120, as further described above with respect to Figure 1. In some embodiments, confidence values ​​are determined by a cloud-based power control server system, as described above with respect to Figure 32.

[0267] In block 3716, EDR events may be generated based on future emission rate events and confidence values. In some embodiments, the shape or magnitude of the generated EDR event is based on future emission rate events. The shape or magnitude of the EDR event may be the size of the thermostat adjustment to the setpoint temperature and / or the amount of time the setpoint temperature is adjusted. In some embodiments, the shape or magnitude of the EDR event is based on the shape or magnitude of future emission rate events, as described above with respect to Figure 31.

[0268] In some embodiments, the shape or magnitude of an EDR event is based on a confidence value related to a future emission rate event. For example, the magnitude of the EDR event may be increased when the confidence value for a future emission rate event is greater than a threshold confidence value. Similarly, the magnitude of the EDR event may be decreased when the confidence value for a future emission rate event is lower than a threshold confidence value. In some embodiments, there may be one or more thresholds related to the magnitude of various EDR events, as described above with respect to Figure 33.

[0269] In some embodiments, EDR events are also based on emission rate variability values ​​for emission rate forecasting. Emission rate variability values ​​may measure the relative variability in the predicted emission rate over a predetermined period, such as a forecast period. Emission rate variability values ​​may be expressed as a percentage or any other appropriate unit of measurement. Emission rate variability values ​​may measure the relative variability in the predicted emission rate forecast compared to various other sources, as described above with respect to Figures 36A and 36B. In some embodiments, emission rate variability values ​​modify a predetermined maximum number of EDR events per day, resulting in larger or smaller generated EDR events. In some embodiments, emission rate variability values ​​modify setpoint adjustments for EDR events, resulting in larger or smaller adjustments to the thermostat setpoint temperature. In some embodiments, emission rate variability values ​​modify a predetermined maximum EDR event duration, resulting in the generation of EDR events with longer or shorter durations.

[0270] In some embodiments, EDR events may be generated according to any of the methods described above with respect to Figures 13 to 15. For example, an EDR event may be generated with an end time corresponding to the time of an emission differential value calculated from emission rate forecasts. In some embodiments, multiple EDR events are generated with different start and / or end times for future emission rate events based on confidence values ​​associated with future emission rate events, as described above with respect to Figure 34. In some embodiments, EDR events may be generated by an event scheduler 213 of a cloud-based power control server system 110, as described above with respect to Figure 2. In some embodiments, EDR events may be generated by an event scheduler 314 of a smart thermostat 160, as described above with respect to Figure 3. EDR events may be proactive or deferred EDR events.

[0271] In block 3718, the thermostat may be configured to control the HVAC system according to EDR events. The thermostat may be configured to control the HVAC system according to any of the methods described above with respect to Figures 13 to 15. For example, at the start time of an EDR event, the EDR event may cause the thermostat to raise or lower its setpoint temperature to increase or decrease the utilization of the HVAC system depending on whether the HVAC system is in heating mode or cooling mode. In some embodiments, the configuration of the thermostat to control the HVAC system is achieved by adjusting the thermostat's hysteresis setpoint temperature, as described above with respect to Figure 35. In some embodiments, multiple thermostats may be configured to control the HVAC system according to multiple different EDR events, as described above with respect to Figure 34. In some embodiments, a cloud-based power control server system, such as the cloud-based power control server system 110 described above with respect to Figure 2, may cause a smart thermostat, such as the smart thermostat 160 described above with respect to Figure 3, to control the HVAC system.

[0272] Figure 38 illustrates an embodiment of the indication of the impact on carbon emissions generated by a user account. In some embodiments, the system quantifies the impact on carbon emissions generated by a user account. By quantifying the impact generated by a user account in a meaningful way, users associated with the account may be encouraged to pursue cleaner electricity practices and continue to reduce their environmental impacts.

[0273] In some embodiments, the impact generated by a user account is displayed in a graphical user interface. For example, as shown in Figure 38, the impact may be displayed on a web page such as the user account's homepage 3800. In other embodiments, the impact is displayed via an application on a mobile device or personal computer. For example, an application running on a mobile device such as the mobile device 140 described above with respect to Figure 1 may have a page or section of a page that displays the impact achieved by the entire user account and / or individual devices linked to the user account, such as the smart thermostat 160 described above with respect to Figure 3. In some embodiments, the impact generated by a user account is sent periodically or from time to time to the user associated with the user account. For example, a cloud-based power control server system such as the cloud-based power control server system 110 described with respect to Figure 2 may send an email weekly or monthly to the email address mapped to the user account, showing the total amount of carbon emission savings since the user account was created and / or the amount of carbon emission savings generated by the user account since the last notification was sent. The interface shown in Figure 38 is just one of many potential examples of visual displays, and the same or similar information can be displayed in a visual display. It should be understood that it can be displayed in any number of visual formats or layouts.

[0274] In some embodiments, the impact generated by a user account is quantified by the actual amount of cleaner electricity consumed or the actual amount of dirtier electricity avoided by the user account. In other embodiments, emission savings may be quantified by the amount of clean electricity matching achieved by the user account, such as a measurement in kWh or any similar electricity measurement. For example, as shown in Figure 38, homepage 3800 may include a clean electricity match value 3802 indicating the amount of cleaner electricity matched by the user account's participation in an EDR event. In some embodiments, the impact may be quantified by the actual amount of carbon emission reduction, such as a measurement in lbs-CO2 / MWh. In some embodiments, the impact generated by a user account includes multiple periods. For example, the system may display the overall impact and the impact generated in measurements for the last month, week, day, or any other time.

[0275] In some embodiments, the impact generated by a user account is displayed in one or more graphs. For example, homepage 3800 may include a status indicator ring 3804 showing the carbon emissions saved from the total amount of electricity consumed. Other graph displays may be used instead of the status indicator ring 3804. For example, any number of bar graphs, line graphs, pie charts, or similar methods of graphing data may be used. In some embodiments, the impact generated by a user account is quantified in more relevant terms. For example, homepage 3800 may include an icon 3806 showing a recognizable image with a relevant description of the impact generated by avoiding carbon emissions, by comparing the impact to an equivalent impact generated from a certain number of trees or acres in a forest. Alternatively, homepage 3800 may include additional descriptions, such as a statement 3808 indicating that the amount of electricity saved is equivalent to the savings achieved by replacing a certain number of gasoline cars with electric vehicles, or the carbon emissions generated by a single flight from New York to Los Angeles. Any other relevant measurements may be used to quantify the amount of emission savings generated by a user account's participation in an EDR event.

[0276] Figure 39 illustrates an embodiment of indicating the collective impact on community-generated carbon emissions. In some embodiments, the system quantifies the collective impact on community-generated carbon emissions. By quantifying the community-generated impact in a meaningful way, individual users can feel a sense of satisfaction from being part of the larger meaning and greater cause of the community. In some embodiments, the community may include all user accounts participating in EDR events. In other embodiments, the system may quantify the collective impact at other program levels, such as by region, by city and / or by power plant. In some embodiments, the community-generated collective impact is displayed in a graphical user interface. For example, as shown in Figure 39, various figures and data may be displayed on the user account's website or homepage 3900. One or more of the same methods and interfaces described above with respect to Figure 38 may be used to quantify the collective impact on community-generated carbon emissions for a user account.

[0277] In some embodiments, collective carbon emission savings are quantified by the amount of cleaner electricity matching achieved through the community. For example, as shown in Figure 39, Homepage 3900 matches through community participation in EDR events. It may include a clean electricity match value 3902 indicating the amount of cleaner electricity being matched. In some embodiments, collective carbon emission savings are quantified and displayed in a more relevant form. For example, homepage 3900 may include an icon 3904 having a relevant description of the number of households that can be powered by the amount of clean electricity matching generated by the program.

[0278] In some embodiments, information about local or regional clean electricity power plants is displayed. For example, homepage 3900 may include clean electricity output 3906 showing the amount of clean electricity generated by local clean electricity power plants. In some embodiments, details of individual power plants are provided. For example, homepage 3900 may include one or more tiles 3908, 3910, and 3912 for individual clean electricity power plants that supply electricity to local communities. Any other relevant measurements may be used to quantify the impact generated by a group or community of user accounts, such as those described above with respect to Figure 38.

[0279] Figure 40 shows an embodiment of a user interface that displays account settings for managing participation in emission demand response events. In some embodiments, the system generates EDR events for thermostats associated with a user account based on one or more account settings. For example, as described above with respect to Figures 26-30, the settings may describe the duration and magnitude of EDR events and / or the level of participation in EDR event programs. In some embodiments, a user associated with a user account may describe one or more account settings. For example, a user may select a maximum event duration for all future EDR events. As another example, a user may choose to participate in one or more programs provided by the system. In some embodiments, account settings are accessible via one or more user interfaces. For example, as shown in Figure 40, a user may access an application interface 4000 on a personal device. As another example, account settings may be accessible via one or more web pages on the internet. In some embodiments, a settings user interface may be displayed to the user when creating an account. In other embodiments, a user may access account-related settings at any time after creating an account in order to modify or update existing settings.

[0280] In some embodiments, the graphical user interface displays one or more settings related to the generation of future EDR events. For example, as shown in Figure 40, there may be one or more fields 4004 for each setting. In some embodiments, the user interface includes a description of the relevant setting. For example, each field 4004 may have a related description 4008 that describes how each particular setting affects the generation of future EDR events and the participation of the thermostat associated with the user account. In some embodiments, the user interface has one or more input controls that allow a user associated with a user account to describe a desired setting for each available setting. For example, field 4004 may be associated with a toggle button 4012 that allows a user associated with an account to toggle the setting on or off. In other embodiments, the input control may be a dropdown, a slider, a checkbox, a text field, a dialog box, or any other suitable input control. In some embodiments, the user interface includes fields for settings that are not yet available as a preview of a new feature currently under development. For example, field 4016 is filled in gray. As indicated by the toggle button 4020, this may relate to a new setting or program that is not yet available. In some embodiments, the graphical user interface has an option for the user to save any changes made to the user account settings.

[0281] Figures 41A to 41D illustrate embodiments of the smart thermostat user interface. In some embodiments, the smart thermostat may indicate that it is attempting to control or is already controlling the HVAC system in accordance with the generated EDR event. For example, the smart thermostat may emit a sound or change a graphical display, such as the electronic display 311 discussed above with respect to Figure 3. In some embodiments, the smart thermostat may indicate the setpoint temperature and the current temperature in accordance with the EDR event. For example, as shown in Figures 41A and 41B, the smart thermostat display 4100 may indicate the setpoint temperature 4104 and the current temperature 4108 as marks on the dial. In other embodiments, the setpoint temperature and the current temperature may be represented as text, numbers, or any suitable way of indicating temperature. In some embodiments, the smart thermostat display includes text describing the current operation of the thermostat in accordance with the generated EDR event. For example, the smart thermostat display 4100 may include one or more text boxes 4112 and 4116 that indicate the current operation of the thermostat. As shown in Figures 41A and 41B, the text boxes 4112 and 4116 may indicate that the smart thermostat is preconditioning the environment before an EDR event by raising the temperature before the temperature is reduced by the EDR event.

[0282] In some embodiments, the smart thermostat display changes in accordance with the current operation of the smart thermostat in response to an EDR event. For example, as shown in Figure 41A and indicated by text box 4112, the thermostat may be in an idle mode that raises the temperature in the environment without the use of the HVAC system. As another example, as shown in Figure 41B and indicated by text box 4112, the smart thermostat may be actively controlling the HVAC system to raise the temperature in the environment before an EDR event. In some embodiments, the smart thermostat display scrolls or loops to display additional information that would otherwise not fit on the display at the same time. For example, as shown in Figures 41C and 41D, text box 4116 may loop between text indicating the current mode and the time when the mode is expected to change.

[0283] In some embodiments, the smart thermostat display includes additional indication that the thermostat is operating in accordance with an EDR event. For example, as shown in Figures 41A to 41D, the icon 4120 may include a symbol associated with the EDR event. By including a recognizable symbol, the smart thermostat may quickly and easily inform the user operating the smart thermostat that the smart thermostat is currently operating in accordance with an EDR event. In some embodiments, one or more features of the smart thermostat display 4100 can be remotely displayed on a computerized device such as a smartphone. For example, a mobile device associated with a user account linked to the smart thermostat, such as the mobile device 140 described above with respect to Figures 1 to 3, as described below with respect to Figure 42, may display some or all of the same features displayed on the smart thermostat itself.

[0284] Figure 42 shows an embodiment of a personal device interface for managing EDR events. In some embodiments, the system may notify a user associated with a user account that the thermostat associated with that user account is operating according to the generated EDR events. For example, the system may send a notification to a mobile device, such as the mobile device 140 as described above with respect to Figures 1 to 3. In some embodiments, the status of the smart thermostat associated with a user account may be viewed from the mobile device or personal computer associated with the user account. For example, as shown in Figure 42, an application running on the mobile device for 4200 may show the setpoint temperature 4204 and the current temperature 4208 for the environment in which the smart thermostat is controlling the HVAC system. In some embodiments, the mobile device displays the same information accessible from the smart thermostat's display, as described above with respect to Figures 41A to 41D.

[0285] In some embodiments, the system sends a notification to a mobile device associated with the user account indicating that a thermostat linked to the user account is about to control the HVAC system in accordance with an EDR event. For example, as shown in Figure 42, an application running on the mobile device 4200 may receive a notification from the system that an EDR event is about to start and display a banner notification 4212 to the user on the mobile device. In other embodiments, an application running on the mobile device may use a pop-up dialog, a badge, an alert, or any other appropriate notification method to warn the user that a thermostat associated with the user account is about to control the HVAC system in accordance with an generated EDR event.

[0286] It should be noted that the methods, systems, and apparatus discussed above are intended to be merely examples. It must be emphasized that various embodiments may omit, substitute, or add various procedures or components as needed. For example, in alternative embodiments, the method may be performed in a different order than described, and various steps may be added, omitted, or combined. Also, features described in relation to one embodiment may be combined in various other embodiments. Different aspects and elements of the embodiments may be combined in a similar manner. It should also be emphasized that as the technology develops, and as many of the elements described are examples, they should not be interpreted as limiting the scope of the invention.

[0287] Specific details are provided in the description to provide a thorough understanding of the embodiments. However, it will be understood by those skilled in the art that the embodiments may be carried out without these specific details. For example, well-known processes, structures, and techniques are shown without unnecessary details to avoid obscuring the embodiments. This description provides only exemplary embodiments and is not intended to limit the scope, applicability, or configuration of the invention. Rather, the preceding descriptions of embodiments provide those skilled in the art with descriptions that authorize carrying out embodiments of the invention. Various modifications may be made in the function and arrangement of elements without departing from the spirit and scope of the invention.

[0288] Furthermore, it should be noted that embodiments may be described as processes depicted in flowcharts or block diagrams. Each may be described as a sequential process, but many of the operations can be performed in parallel or simultaneously. In addition, the order of operations may be rearranged. The process may have additional steps not shown in the diagram.

[0289] Although several embodiments have been described, it will be recognized by those skilled in the art that various modifications, alternative configurations, and equivalents may be used without departing from the spirit of the invention. For example The elements described above may simply be components of a larger system, and other rules may take precedence over or modify the application of the invention. Furthermore, the number of steps may be performed before, during, or after the consideration of the elements described above. Therefore, the above description should not be construed as limiting the scope of the invention.

Claims

1. A method for executing an emissions demand response event, A cloud-based HVAC control server system obtains a first emission rate forecast, including the change in emission rate during a first time period. The cloud-based HVAC control server system includes the step of generating an EDR event based on the first emission rate forecast, wherein the EDR event includes a start time and an end time. The aforementioned method, Before the aforementioned start time, the cloud-based HVAC control server system transmits the generated EDR event over the data network to a thermostat located in a remote structure from the cloud-based HVAC control server system. The steps include: storing the EDR event in the thermostat's memory using the thermostat; The steps include: at the aforementioned start time, causing the thermostat to initiate control of the HVAC system in accordance with the generated EDR event; The steps include: obtaining a second emission rate forecast, including the change in emission rate in a second time different from the first time, from the cloud-based HVAC control server system, following the aforementioned start time and before the aforementioned end time; The process includes the step of generating a modified EDR event by the cloud-based HVAC control server system, following the acquisition of the second emission rate forecast and prior to the end time, wherein the modified EDR event includes a modified end time based on the difference between the first time and the second time. The aforementioned method, The cloud-based HVAC control server system includes the step of sending the modified EDR event to the thermostat at a time earlier than the earlier of the end time and the modified end time. The aforementioned method, When the modified EDR event is received by the thermostat, The steps include: storing the modified EDR event in the memory of the thermostat using the thermostat; A method for performing an emissions demand response event, comprising the step of causing the thermostat to control the HVAC system in accordance with the modified EDR event until the modified end time is reached.

2. The first emission rate forecast includes a faster change in the emission rate during the third time period. The step of generating the aforementioned EDR event is: The steps include determining after the third time that the cloud-based HVAC control server system obtains the second emission rate forecast, A method for performing an emissions demand response event according to claim 1, further comprising the step of setting the start time of the emissions demand response event to begin before the second emissions rate forecast is received.

3. A method for executing an emission demand response event according to claim 1, wherein the EDR event is generated for a duration set to the maximum allowable event duration.

4. The step of generating the modified EDR event is: The steps include: determining after the second time period that the cloud-based HVAC control server system obtains a third emission rate forecast; A method for performing an emissions demand response event according to claim 1, comprising the step of setting the modified end time of the modified EDR event so that it is before the third emission rate forecast is received.

5. The step of generating the modified EDR event is: The steps include determining that the cloud-based HVAC control server system obtains a third emission rate forecast within a predetermined minimum period prior to the second time, A method for performing an emissions demand response event according to claim 1, comprising the step of setting the modified end time of the EDR event to coincide with the second time, before the third emission rate forecast is obtained.

6. The second time is earlier than the first time, and the step of generating the modified EDR event is, The steps include: determining after the second time period that the cloud-based HVAC control server system obtains a third emission rate forecast; A method for performing an emissions demand response event according to claim 1, comprising the step of setting the modified end time of the modified EDR event so that the third emission rate forecast is obtained.

7. The second time is later than the first time. A method for performing an emissions demand response event according to claim 1, wherein the step of generating the modified EDR event includes setting the modified end time of the modified EDR event to be after the first time.

8. A method for performing an emissions demand response event according to claim 7, wherein the step of setting the modified end time of the modified EDR event is limited by the maximum allowable event duration.

9. The step of generating the aforementioned EDR event is: A method for performing an emissions demand response event according to claim 1, further comprising the steps of using the first emissions forecast by the cloud-based HVAC control server system to determine emissions differential values ​​for each of a plurality of points in time during a future period covered by the first emissions forecast, thereby generating a plurality of emissions differential values, the emissions demand response event being generated based on the determined plurality of emissions differential values.

10. The step of generating the aforementioned EDR event is: A method for performing an emissions demand response event according to claim 1, further comprising the step of restricting the start time of the emissions demand response event to be after a predetermined minimum time after the end time of a previously generated EDR event.

11. The step of generating the modified EDR event is: The emission demand according to claim 1, further comprising the step of limiting the modified end time of the modified EDR event so as not to be later than a predetermined latest time of day A method for executing an event requiring a response.

12. A system for executing emission demand response events, This includes a cloud-based power control server system, and the cloud-based power control server system is One or more processors, The memory includes, which is communicatively coupled to one or more processors and is readable by one or more processors and stores processor-readable instructions, and when a processor-readable instruction is executed by one or more processors, A step of obtaining a first emission rate forecast that includes changes in the emission rate during a first time period, The process involves generating an EDR event based on the first emission rate forecast, wherein the EDR event includes a start time and an end time. Before the aforementioned start time, the generated EDR event is transmitted over the data network from the cloud-based power control server system to a thermostat located in a remote structure. The steps include: storing the EDR event in the thermostat's memory using the thermostat; The steps include: at the aforementioned start time, causing the thermostat to initiate control of the HVAC system in accordance with the generated EDR event; A step of obtaining a second emission rate forecast, which includes the change in emission rate in a second time period different from the first time period, following the start time period and before the end time period, After obtaining the second emission rate forecast and before the end time, the system performs the step of generating a modified EDR event, wherein the modified EDR event includes a modified end time based on the difference between the first time and the second time. The steps include sending the modified EDR event to the thermostat at a time earlier than the earlier of the aforementioned end time and the modified end time, When the thermostat receives the modified EDR event, the thermostat stores the modified EDR event in the thermostat's memory. A system for executing an emissions demand response event, which involves the steps of causing the thermostat to control the HVAC system in accordance with the modified EDR event until the modified end time is reached.

13. A system for performing an emission demand response event according to claim 12, further comprising a plurality of thermostats, wherein the plurality of thermostats comprises the thermostats.

14. A system for performing an emissions demand response event according to claim 12, further comprising an application running on a mobile device, the application configured to control the thermostat via communication with the cloud-based power control server system.

15. The cloud-based power control server system further includes an interface, the interface configured to obtain the plurality of emission rate forecasts from an emission data system that is remotely accessible over a network, for executing the emission demand response event according to claim 12.

16. The first emission rate forecast includes a faster change in the emission rate during the third time period. The step of generating the aforementioned EDR event is: The steps include: determining that the cloud-based power control server system obtains the second emission rate forecast after the third time; A system for performing an emissions demand response event according to claim 12, further comprising the step of setting the start time of the emissions demand response event so that it starts before the second emissions rate forecast is received.

17. A program that, when executed, causes one or more processors of a cloud-based HVAC control server system to perform any of the methods of claims 1 to 11.

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