Normalized heating energy consumption estimation

Abstract

An implementation of the disclosed technology provides a method that includes receiving a setpoint temperature for the building, determining an average outdoor temperature over a first predetermined time period for a location of the building, measuring a total central heating energy consumption for the building over the first predetermined time period, adding the determined average outdoor temperature and measured total central heating energy consumption to a first data set including a plurality of average outdoor temperatures and corresponding total central heating energy consumptions over a plurality of predetermined time periods having durations substantially the same as the first predetermined time period, estimating a weather independent normalized heating energy consumption for maintaining the setpoint temperature in the building based on a predetermined reference outdoor temperature and the first data set, and characterizing the heating energy consumption for heating the building based on the normalized heating energy consumption and a base temperature wherein the base temperature is the outdoor temperature at which heating is required to keep an indoor temperature minimally at the setpoint temperature and wherein the base temperature is selectively obtained by way of optimization and / or linear fit and / or distribution based approach. (1)

Description

[0001] NORMALIZED HEATING ENERGY CONSUMPTION ESTIMATION

[0002] TECHNICAL FIELD

[0003] [1] The disclosed technology relates to models of the energy consumed to heat buildings that are normalized against weather influences.

[0004] BACKGROUND

[0005] [2] Buildings, whether commercial, residential or otherwise in nature, often employ energy management systems to monitor, control and / or otherwise manage the use of energy associated with various utility service and other functions, such as heating, cooling, potable water, electricity, food refrigeration, water heating, etc. More precisely, energy management systems, regardless of their complexity or sophistication, serve to at least monitor the operation of various energy management appliances, such as heating and / or cooling appliances, humidity control appliances, hot water heaters, food storage appliances, cooking appliances, ventilation appliances, etc.

[0006] [3] A relatively simple energy management system may employ various sensors to gather data associated with performing such functions at a building, such as indoor and / or outdoor temperature and / or humidity, amount of precipitation, usage of water and / or various fuels, local generation and / or usage of electricity, etc. Such gathered data may then be organized chronologically and stored with a regular interval of time (e.g., hourly, daily, etc.). However, in such a relatively simple energy management system, it is not uncommon for such data to simply sit in storage with little or no use being made thereof, regardless of its level of detail. It may be that such data is put to use for little more than diagnostics purposes by service personnel during maintenance and / or repairs performed on such an energy management system. Alternatively or additionally, it may be that such data is put to use for little more than guiding consulting personnel in making recommendations concerning improvements to such an energy management system. [4] A somewhat more sophisticated energy management system may, at a recurring interval of time, automatically use such stored data to make some degree of adjustment to the manner in which various energy management appliances are used to improve the efficiency and / or cost effectiveness with which energy is used in a building. For example, such gathered data may be used in conjunction with information concerning weather forecasts and / or utility rates to adjust the times at which such energy management appliances are used (e.g., delaying start and / or stop times) to shift more of the consumption of energy to times when utility rates are lower. It may be that such a more sophisticated energy management system may be provided with various parameters indicative of degrees by which specifications of temperature, humidity, lighting, timings, etc. may be deviated from as part of adjusting times, etc.

[0007] [5] Alternatively or additionally, a somewhat sophisticated energy management system may cooperate with the grid-wide energy management system of a provider of a utility to a building, such as potable water, natural gas, electricity, etc. to adjust the times at which energy management appliances are used. In this way, energy use during peak hours of each day may be reduced by shifting about when various energy management appliances are used. In such a somewhat sophisticated energy management system, such adjusting of times at which energy is used may entail cutting off the provision of energy to one or more energy management appliances to prevent their use during peak hours. In such an energy management system, data gathered locally by sensors of the energy management system may be relayed to the grid-wide energy management system for use, rather than being used locally.

[0008] [6] For example, whether controlled locally or in coordination with a gridwide energy management system, energy "time-shifting" has become increasingly common in regions where electric rates are relatively high during peak demand hours and / or where the electrical grid struggles to meet the demand for electricity during peak demand hours. Energy may be locally stored during hours of lesser demand (e.g., electrical energy storage, thermal energy storage, etc.). Such stored energy may then be used later for at least some of the energy needs at the building during peak demand hours. [7] Also by way of example, installation of a combination of local electricity generation using renewable energy sources (e.g., local solar array, local windmill, etc.) and local electrical energy storage has become increasingly common, both to reduce energy costs and to reduce environmental impacts. However, such installations often additionally include either a connection to an electrical grid or the ability to locally generate electricity using a non-renewable energy source (e.g., natural gas, propane, etc.) at times when the local generation of electricity from renewable energy sources has been insufficient (e.g., where there are multiple days of insufficient sunshine and / or insufficient wind). An energy management system employed at such an installation may be provided with settings that convey priorities thereto (e.g., opportunistically use available renewable energy sources whenever possible, and resort to the use of a grid connection and / or non-renewable energy sources as a last resort). A form of "time-shifting" may be used to adjust the times at which renewable energy sources are consumed to give priority to charging batteries.

[0009] [8] In a somewhat more sophisticated energy management system, such gathered data may be used to preemptively generate multiple predictions of levels of energy consumption that correspond to a pre-selected set of setpoint temperatures. As depicted and described in reference to at least FIGS. 1 and 2 of US9727063, a set of predictions of levels of energy consumption may be preemptively generated for a set of pre-selected candidate setpoint temperatures. Then, at a later time, when a level of energy consumption is needed for a prediction and / or another purpose, degrees of error for levels of energy consumption corresponding to at least the nearest setpoint temperatures may be generated and then used to pick from among one of the preemptively generated levels of energy consumption for one of the pre-selected setpoint temperatures. While this approach may minimize processing and / or data storage resources, this approach inherently introduces an amount of error into predictions and / or other analyses that may be based on one of the preemptively generated levels of energy consumption.

[0010] [9] In a more sophisticated energy management system, such gathered data may be used together with still other information from other sources to identify opportunities to reduce energy consumption. As disclosed in US20150267935, an energy management system may monitor changes made to a setpoint, and may analyze such a change to determine whether it represents an opportunity to reduce energy consumption. If so, then such an energy management system may present a user with a prompt suggesting that the new setpoint be adopted in order to reduce expenses through the identified reduction in energy consumption.

[0011]

[0010] As depicted and described in reference to at least FIG. 2 of US20150267935, such detection of such an opportunity may be through comparing at least the new setpoint to one or more data sets provided by a governmental organization, such as the Environmental Protection Agency in the United States. Unfortunately, this approach requires access to and / or storage of a considerable amount of data, thereby increasing required data storage resources. Alternatively, such detection of such an opportunity may be through determining whether the new setpoint represents a reduction in temperature of sufficient magnitude that is larger than a threshold quantity of degrees. While this approach requires considerably less in the way of data storage resources, it does not include deriving the quantitative change in energy consumption that would arise from adopting the new setpoint.

[0012]

[0011] Also in a more sophisticated energy management system, such gathered data may be used to construct a visual presentation of a recent history of recent energy consumption. As described and depicted in reference to at least FIG. 10 of US9453655, such a visual presentation may include indications of functions and / or particular components of an energy management system that are deemed to be responsible for causing overall increases in energy consumption or causing overall decreases in energy consumption. However, to enable such functionality, separate energy consumption calculations must be performed for each component that consumes energy, and the results of such calculations for each such component must be stored. Thus, both processing and data storage requirements are considerably increased for such an energy management system.

[0013]

[0012] In a still more sophisticated energy management system, such gathered data may be used to generate and maintain a model of energy consumption that occurs at a building, including energy consumption associated with multiple utility service functions. Such a model may necessarily include influences of the building, itself, along with energy management appliances at the building. More specifically, such aspects of a building as the degree to which its walls, windows, etc. are insulated and / or made resistant to drafts may influence the efficiency and / or effectiveness with which various energy management appliances may heat, cool humidify, dehumidify and / or ventilate the building. Alternatively or additionally, such aspects of a building as material used for its exterior surfaces, and / or the color and / or darkness of those exterior surfaces may control the degree to which sunlight levels influence interior temperatures of the building. Also, alternatively or additionally, such aspects of a building as the material it is made from, and / or aspects of the manner in which the building interacts with the ground and / or with precipitation may influence the efficiency and / or effectiveness with which various energy management appliances may control humidity and / or other aspects of air quality within building.

[0014]

[0013] It may be that such a model is used to predict amounts of energy that will be consumed during various ranges of time of a day and / or during various days of a week. Such predictions may be used to automatically adjust times at which various energy management appliances are used, and / or to control the use of time-shifting to store electrical and / or thermal energy.

[0015]

[0014] As disclosed in US1 1268732, such a model for the overall consumption of energy at a building may be normalized against weather influences. This enables such a model to be more readily used to make a prediction concerning overall energy consumption at a building during an upcoming period of time. This also enables making such a prediction while taking into account weather conditions that are forecasted for that upcoming period of time. As described and depicted in reference to at least FIGS. 52-54 of US11268732, such normalization against weather influences may be achieved by generating a regression model of the weather for an earlier period of time, and then using that regression model to counteract weather influences that are included within overall energy consumption data that has been gathered at a recurring interval during that same earlier period of time. The result is a derived time series of overall energy consumption data that is normalized against weather influences. While such an approach to normalization as disclosed in US11268732 may be effective, it is undesirably complex such that considerable processing and / or data storage resources are required.

[0016]

[0015] In still another more sophisticated energy management system, such gathered data may be used together with data concerning the geographic location of a user to cause the automated adjusting of a setpoint based on the distance of the user from a building. As depicted and described in reference to at least FIGS. 3A-D and 4 of US9618227, stored observations of interior temperature changes are used, along with stored observations of travel times are used to determine what changes to make in the setpoint, and when. By way of example, where a user is driving away from the building, the direction in which they are driving and how far they are away from the building may be used to determine how far to allow the interior temperature to deviate from the setpoint temperature. Previously stored observations may reveal that returning to building from one direction takes more time than returning from another direction. Also, previously stored observations may reveal that one energy management appliance is able to return the building interior to the setpoint temperature more quickly than another.

[0017]

[0016] As further depicted and described in reference to at least FIG. 2 of US9618227, following the storage of a sufficiently large set of such observations over time, a set of distances from the building (represented by rings in FIG. 2) may be derived that represent distances at which various configuration changes, including changes to setpoint temperatures may be triggered. Unfortunately, gathering and store such a diverse set of data concerning distances, directions, past temperature change observations, etc. greatly increases data storage requirements for such an energy management system.

[0018]

[0017] US2019 / 0338969A1 is directed to improving energy conservation, in particular to a system and method for forecasting fuel consumption for indoor thermal conditioning using thermal performance forecast approach with the aid of a digital computer and discloses a system for forecasting fuel consumption for indoor thermal conditioning using thermal performance forecast approach with the aid of a digital computer, comprising: Obtaining average daily outdoor temperatures wherein the computer collects data on the average daily outdoor temperatures over a specified period. Obtaining historical daily fuel consumption: wherein the computer also gathers data on how much fuel was used each day during the same period. Converting the data to average daily fuel usage rates: wherein the historical fuel consumption data is converted into average daily fuel usage rates for the specified period. Generating a continuous frequency distribution: The computer then creates a distribution that shows how frequently different average daily outdoor temperatures occur. Creating a plot: The computer plots the average daily fuel usage rates against the average daily outdoor temperatures to visualize the relationship between temperature and fuel consumption. Calculating the fuel consumption: Finally, the system calculates the expected fuel consumption for at least part of the specified period. This calculation is based on the daily fuel usage rates and the frequencies of the corresponding average daily outdoor temperatures.

[0019]

[0018] US2014 / 316584A1 is directed to optimizing the energy consumption of

[0020] HVAC systems and discloses a system for managing and consuming energy efficiently. The system comprises electrical power generators. The generators produce electricity or other energy types (e.g., gas) using various methods, such as hydroelectric systems, nuclear power plants, solar plants, and more. The amount of energy generated at any time is limited by the capacity of the generators. The generators can be managed by either a utility provider or third- party entities contracting with the utility provider. The system further comprises a utility provider computing system. The system communicates with the power generators, an energy management system, and potentially electronic systems in consumer residences. The system manages the distribution of electricity, monitors energy consumption at residences, and collects fees based on consumption. The system further comprises an energy management system. The energy management system manages energy consumption in residences, provides reporting and control mechanisms to the utility provider, and engages in real-time communications with both residences and the utility provider. The energy management system can reduce energy consumption during specific periods to lower the demand on power generators. The system further comprises a communication network. The network enables communication between the energy management system and electronic devices in residences. The network can be a local area network, wide-area network, virtual private network, Internet, intranet, extranet, or other types of networks. The system further comprises energy consumer residences. Energy consumer residences are various structures, such as houses, apartments, schools, commercial properties, etc., equipped with energy consumption devices like heating / cooling systems, microwaves, and dishwashers. Residences may also have control devices like intelligent thermostats that manage energy usage. The system further comprises a power distribution network. This network transfers energy from the power generators to the residences. In an electrical network, it includes power lines and substations. In a gas network, it includes compressor stations and pipes. The system aims to efficiently manage energy supply and demand, ensuring that energy is distributed effectively and consumed responsibly.

[0021]

[0019] US2018 / 039292A1 is directed to optimizing the control of heating and cooling systems by using interval energy consumption data to improve energy efficiency and maintain comfortable indoor temperatures and discloses a method for optimizing heating control with interval data optimization for a heating system wherein the method relies on a linear relationship between energy consumption rates and outdoor temperatures, based on the principle that heat transfer in buildings is largely proportional to the temperature difference between the inside and outside. The method comprises the steps of outdoor temperature input: The current outdoor air temperature is received from an outdoor temperature sensor. An optimized energy consumption rate setpoint is calculated based on the outdoor temperature. The heating system calculates the current energy consumption rate using data from an energy meter. The current energy consumption rate is compared to the optimized setpoint. The heating system adjusts its temperature setpoint based on the comparison to reduce the difference between the current rate and the setpoint. The process iterates until the average energy consumption rate aligns closely with the setpoint. The optimized temperature setpoints and outdoor temperatures are stored in a table for future reference. The heating system's equipment, such as boilers and mixing valves, adjusts its output to maintain the optimized temperature setpoint. This method ensures continuous adjustments and optimizations to maintain efficient energy consumption and comfortable indoor environments.

[0022]

[0020] EP2733563A2 is directed to providing a solution for controlling a heating system in such a way that short-term dynamic prices of energy and changing outside temperature is taken into consideration to provide an effective and dynamic heating control system, and discloses a process for defining daily heating time based on various input information. This method allows for optimized control of a heating system, resulting in improved energy efficiency and cost savings. The process involves collecting and analyzing multiple sources of data to calculate the required daily heating energy consumption and determining the optimal heating time. The process comprises the steps of collecting input information: spot price information is collected, wherein information about the price of heating energy is collected, such as electricity. Further, the average outdoor temperature on the day in question, obtained from a weather server or local temperature sensor. The total energy consumption is obtained during a period without heating, such as a summer month (e.g., August). Further, the total energy consumption over a year is collected, based on historical data, such as electricity invoices. The heating degree days are collected, wherein the heating degree days are a measure of how much heating is needed based on outdoor temperatures, received from a service server. The total water consumption is collected over a period, used to estimate heating energy needed for hot water. Data related to the heating system is collected, such as boiler capacity, heating power, and thermostat status. In a further step the energy consumption is calculated using a formula that accounts for total annual energy consumption (ET), energy consumption during a nonheating period (EAugust_T), and a constant (k) representing increased winter consumption. Further, the consumption per degree day (ES) is determined using the calculated heating energy consumption (EY_H) and total degree days (SY). A degree day is computed as the integral of a function of time that generally varies with temperature. The function is truncated to upper and lower limits that are appropriate for climate control. The function can be estimated or measured by different methods, in each case by reference to a chosen base temperature. Further daily heating energy (EY_D) is calculated based on the degree day of a particular day (SD) and the consumption per degree day (ES). Also the daily hot water energy (EW_D) is calculated based on annual water consumption (VY), hot water rate (f), temperature difference (TW), and heat capacity of water (cm_w). The total Daily Energy Consumption (EHS) is determined which is the sum of daily heating energy (EY_D) and daily hot water energy (EW_D). A control apparatus calculates the total daily energy consumption (EHS) and determines the heating time needed to achieve it, considering spot prices to optimize costs. The control apparatus may adjust the heating power to shorten the heating time if needed for certain spot prices. This method allows for precise control of heating systems, resulting in energy and cost savings while maintaining indoor comfort.

[0023] SUMMARY

[0024]

[0021] Based on the above, it is an object to provide a method of automatically generating and maintaining a model of the consumption of energy used to heat a building wherein the model is normalized against weather influences. In this context, "automatically" refers to the process of generating and maintaining a model of energy consumption without the need for human intervention, thus without manual input. It is also an object to do so in a manner that minimizes complexity so as to minimize the consumption of processing and / or data storage resources. In so doing, the need to use more sophisticated and costly processing and / or data storage components is averted. As a result, the generation and maintenance of such a model of heating -related energy consumption may be performed using the relatively simpler and less expensive processing and / or data storage components of simpler processing devices, such as a thermostat or other controller that may be incorporated into a heating appliance, in addition, it is an object to provide an outdoor temperature independent metric which allows for the comparison of energy usage from period to period, reflects the changes to the heating habits and reflects building isolation changes. In addition, it is an object to provide the end user with personalized, tailored recommendations based on the individual differences in the characteristics of the respective building and optimizing heating control based on user preferences and indoor comfort levels. Thus, it is a further object to improve overall user satisfaction while reducing complexity in generating a model of the consumption and enhancing the accuracy and personalization of the generated recommendations.

[0025]

[0022] The object is achieved by a computer-implemented method for characterizing energy consumption for heating an interior of a building, the method comprising: Receiving, in particular by a data processing device, in particular a controller, a setpoint temperature (Tsetpoint) for the building;

[0026] Determining, in particular by a data processing device, in particular the controller, an average outdoor temperature (Toutdoor) over a first predetermined time period (pdAt) for a location of the building; measuring, in particular based on sensor readings communicated to the data processing device, in particular to the controller, a total central heating energy consumption (EC) for the building over the first predetermined time period (pdAt); adding, in particular via the data processing device, in particular the controller, the determined average outdoor temperature (Toutdoor) and measured total central heating energy consumption (EC) to a first data set comprising a plurality of average outdoor temperatures (Toutdoor) and corresponding total central heating energy consumptions (EC) over a plurality of predetermined time periods (pdAt) having durations substantially the same as the first predetermined time period (PdAt); determining, in particular by the data processing device, in particular the controller, a weather independent normalized heating energy consumption (ENEC) for maintaining the setpoint temperature (Tsetpoint) in the building based on a predetermined reference outdoor temperature (Tret) and the first data set; and characterizing in particular by a data processing device, in particular the controller, the heating energy consumption (EC) for heating the building based on the normalized heating energy consumption (ENEC) and a base temperature (Tbase) wherein the base temperature (Tbase) is the outdoor temperature (Toutdoor) at which heating is required to keep an indoor temperature (Tindoor) minimally at the setpoint temperature (Tsetpoint) and wherein the base temperature (Tbase) is selectively obtained by way of optimization (also referred to as the first method in the application) and / or linear fit (also referred to as second method in the application) and / or distribution based approach (also referred to as the third method in the application).

[0027]

[0023] It is understood within the application that the steps of the computer implemented method according to the invention are performed by way of a data processing device, in particular by a data processing device according to the invention, in particular a controller, in particular a controller according to the invention. This applies to all method steps described herein as the term "computer-implemented" indicates that the steps are carried out by hardware and / or software components, such as data processing devices, in particular controllers, processors, memory, and related devices, which are comprised in such systems and are described herein.

[0028]

[0024] The base temperature (Tbase) is the outdoor temperature below which energy needs to be consumed to heat the building to maintain the building at a particular setpoint temperature selected by a user for the interior of the building. If the outdoor temperature is above the base temperature (Tbase), then no such energy needs to be consumed to heat the building. As an example to visualize the relationship of (the base temperature Tbase), the setpoint temperature (Tsetpoint), and the outdoor temperature (Toutdoor) and the energy consumption (EC): for a particular setpoint temperature (Tsetpoint) that is assumed to be unchanging (at least for a predetermined time period (pdAt)), the base temperature (Tbase) is the outdoor temperature (Toutdoor) at which a flat line of a zero level of heating energy consumption (EC), and a sloped line of a non-zero linearly increasing level of heating energy consumption (EC) meet.

[0029]

[0025] The base temperature (Tbase) is thus defined in such a way that the base temperature (Tbase) can be determined tailored to the specific strengths of each approach by selectively choosing the approach which fits the specific needs and priorities of the respective energy consumption normalization. In other words, tailoring the determination of the base temperature (Tbase) to fit the specific strengths of each approach ensures that the specific approach is selected which suits the particular needs and priorities of the respective energy consumption normalization (ENEC). Optimization, linear fit and / or the distribution approach can be selected based on the need for simplicity, precision and robustness.

[0030]

[0026] Optimization allows prioritizing for precision and customization, making it suitable for accurate and tailored calculations in case of more complex building parameters. Optimization further focuses on achieving the best possible solution by minimizing or maximizing an objective function (e.g., minimizing the error between actual and predicted energy savings). The strengths of optimization comprise precise results making optimization a good combination choice for fine- tuning results. Optimization is excellent for addressing complex problems with multiple variables and constraints. Optimization further adapts well to specific cases where accurate results are critical. Optimization is a good standalone choice when accuracy is paramount and sufficient reliable data is available.

[0031]

[0027] Linear Fit allows prioritizing for simplicity and speed, making it ideal for quick assessments and easy implementation. Uses a straightforward linear regression to establish a relationship between variables (e.g., heating energy consumption (EC) and heating degree days (HDD)). The strengths of linear fit comprise that the linear fit is simple, quick, and computationally efficient. Furthermore, the linear fit is easy to interpret and implement. Linear fit is a good choice for combination with one of the other methods due to the simplicity, versatility, and ability to establish foundational relationships using linear fit. Linear fit further allows providing a baseline trendline for understanding the broader pattern of data before applying more complex methods like optimization or distribution-based analysis. Thus, by using linear fit, it is possible to efficiently reduce the dataset into a manageable model, which can then serve as an input or starting point for more advanced techniques like optimization.

[0032]

[0028] The distribution based approach allows prioritizing for robustness and comprehensive analysis, making it advantageous for handling large datasets and detailed data distribution analysis. The distribution based approach provides a probabilistic determination by analyzing the range and variability of possible outcomes (e.g., uncertainty in Tbase). The distribution based approach captures uncertainty, making it robust for scenarios with incomplete or noisy data. The distribution based approach is effective for generating ranges of possible values rather than fixed outputs. The distribution based approach is a good choice as the standalone method to determine the base temperature (Tbase) as the approach allows for a nuanced analysis, capturing subtle variations that other methods might oversimplify. By analyzing the full range of data distributions, this method identifies potential outliers or rare events (e.g., extreme heating degree day (HDD) years) that could significantly affect heating energy consumption. The distribution based approach further works effectively without assuming pre-defined relationships between variables. The distribution-based approach provides a probabilistic range of possible values, capturing variability and accommodating uncertainty in the data. Combining the distribution-based approach with optimization and / or linear fit can enhance the analysis by balancing uncertainty modeling with either precision and / or simplicity.

[0033]

[0029] Each of the three methods, optimization, linear fit, and distributionbased approach, offers unique advantages that make the methods particularly valuable either as standalone approaches or as part of a combined strategy.

[0034]

[0030] In other words, by selectively choosing from these methods or combining these methods based on the context, it is possible to balance efficiency, accuracy, and robustness. In essence, selective use allows to choose one of the methods for efficiency, accuracy or robustness. Selective combination can maximize the strengths of each method while minimizing the respective weaknesses, leading to solutions that are both practical and effective. In summary, the specific advantage of selectively choosing from the three methods (optimization, linear fit, and distribution-based approach), either individually or in combination, is the ability to tailor the approach to the context, goals, and data quality to fit the building being assessed while each of the approaches as a standalone is suitable for determining the base temperature (Tbase) sufficiently for the purposes of the method according to the invention.

[0035]

[0031] The reference temperature (Tref) allows for the normalization of the energy consumption (EC) by evaluating the periodic energy consumption at the predetermined reference temperature (Tret). The reference temperature (Tret) acts as a baseline to adjust the energy consumption data and adjust for weather- related variability and thus allows for a normalized energy consumption (ENEC) which provides end-users with insights into their weather-independent energy consumption (EC). This allows normalization of the energy consumption (ENEC) of the building wherein the normalization has the following properties:

[0036]

[0032] The outdoor temperature (Toutdoor) does not influence the normalized energy consumption (ENEC). Thermostat setpoint changes affect the normalized energy consumption (ENEC). Thus, a lower temperature setpoint (Tsetpoint) influences, in particular lowers the normalized energy consumption (ENEC) and vice versa. Changes made to the building characteristics are reflected in the normalized energy consumption (ENEC). For example better insulation means a lower normalized energy consumption (ENEC) and vice versa.

[0033] In other words, the resulting normalized energy consumption (ENEC) is the expected energy consumption (ENEC) at the reference temperature (Tref) based on a selectively determined (Tbase). Normalizing energy consumption (EC) around (Tbase) results in performance metrics that are more meaningful and reflective of the building's actual energy efficiency. These metrics can be used to set realistic energy-saving goals and track progress over time.

[0037]

[0034] Additionally, in this way, the generation and maintenance of such a normalized model may be more easily performed locally at a building without the support of remotely located (e.g., cloud-based) resources. As a result, the generation and maintenance of such a model may be enabled at a building in a relatively secluded location where access to the Internet may not be available, or may not be reliable.

[0038]

[0035] Thus, the present method allows to take into account aspects of various relationships among values for temperatures and amounts of energy consumed by one or more energy management appliances (e.g., one or more of the energy management appliances of the energy management system) to heat a building under a variety of weather conditions. This allows for example the elucidation of aspects of relationships among various values that are derived as part of a system manager (e.g., the system manager of the energy management system) generating and maintaining a model of such energy consumption that is normalized against weather influences and / or generating determinations of energy savings for heating energy consumed to heat a building.

[0039]

[0036] The method thus allows to automatically generate and maintain a model of the consumption of energy used to heat a building wherein the model is normalized against weather influences. The method does not require continuous human intervention and, as a computer-implemented method, is thus automatic in the sense that the process of generating and maintaining the model of energy consumption operates independently, without the need for manual input.

[0040]

[0037] The method further allows to do so in a manner that minimizes complexity so as to minimize the consumption of processing and / or data storage resources. In so doing, the need to use more sophisticated and costly processing and / or data storage components is averted. As a result, the generation and maintenance of such a model of heating -related energy consumption can be performed using the relatively simple and less expensive processing and / or data storage components of simple processing devices, such as a thermostat or other controller that may be incorporated into a heating appliance.

[0041]

[0038] The method further allows to provide an outdoor temperature Qindependent metric which allows for the comparison of energy usage from period to period, reflects the changes to the heating habits and reflects building isolation changes.

[0042]

[0039] The method further allows to provide the end user with personalized, tailored recommendations based on the individual differences in the characteristics of the respective building and optimizing heating control based on user preferences and indoor comfort levels. The method thus allows understanding detailed localized factors that influence energy consumption for a specific building. The method in particular allows analyzing specific occupant behavior.

[0043]

[0040] Thus, the method allows improving overall user satisfaction while reducing complexity in generating a model of the consumption and enhancing the accuracy and personalization of the generated recommendations.

[0044]

[0041] In an embodiment the reference outdoor temperature (Tref) is lower than the base temperature (Tbase) in case of heating. In particular, the reference temperature (Tref) should preferably have a low nonlinear error compared to the base temperature (Tbase). The low nonlinear error improves the accuracy of the normalized energy consumption (ENEC). A good scaling can be achieved by choosing the reference temperature (Tret) close to the base temperature (Tbase).

[0045]

[0042] Selecting the reference temperature (Tref) close to the base temperature (Tbase) the influence of temperature fluctuations can be advantageously further reduced, in particular minimized. When the reference temperature (Tref) is close to the base temperature (Tbase), the data used for normalization of the energy consumption (EC) is more relevant to the actual heating or cooling needs of the building. Accurate normalization around (Tbase) improves the predictive modeling of energy consumption. This accuracy additionally helps in forecasting energy needs and planning for future energy use more effectively. In addition, with a reference temperature (Tref) aligned with the base temperature (Tbase), adjustments in energy management strategies can be made more effectively. This alignment ensures that energy-saving measures are implemented at the most impactful temperature points, further optimizing energy efficiency.

[0046]

[0043] In particular in a further embodiment, maintaining a nonlinear error below 4% provides high confidence in the data analysis results. The nonlinear error below 4% ensures consistency in performance evaluation. This consistency helps in identifying true trends and patterns in energy consumption, leading to even more effective energy management strategies. Preferably the reference temperature (Tref) is chosen such that the operating range of this value is quite large (Tbase 5°C) while the nonlinear error remains below 4% within a ±1 °C range.

[0047]

[0044] In a further embodiment the normalized heating energy consumption (ENEC) is determined based on

[0048] Which is the normalized form of the following equation:

[0049]

[0045] The equation thus shows is a particularly straightforward way how the normalized energy consumption (ENEC) is independent of the outdoor temperature (Toutdoor).

[0050]

[0046] This equation is particularly advantageous in case of a daily aggregation of data. The daily sum of the energy consumption (EC) and the daily average of the outdoor temperature (Toutdoor). The other parameters are assumed to be constant. This daily aggregation allows in a straightforward way to filter and / or smoothen out dynamic heating behavior.

[0051]

[0047] In a further embodiment the normalized heating energy consumption (ENEC) is determined based on wherein HDD are heating degree days, wherein the heating degree days are a function of the base temperature Tbase and the outdoor temperature Toutdoor and wherein the heating degree days are an expression of coldness of a day. U is the overall heat transfer coefficient [W / ( [°C-m]2)] and A is the surface area of the building [m2], and wherein Einternai is the average energy production in the building, such as the energy produced by people and / or appliances in the building. The term current means that the respective temperature is a recently determined temperature wherein recently can comprise determination over a predefined period of time. The resulting curve of heating degree days (HDD) versus energy consumption (EC) passes through the origin and is easy to describe mathematically and thus easy to visualize and interpret. This equation is particularly applicable, when solar radiation can be neglected and the building is in a steady state.

[0052]

[0048] The above described relationship among the base temperature (Tbase), outdoor temperatures (Toutdoor) and the energy consumed (EC) to heat a building forms the basis for heating degree days (HDDs) as described by the HDD equation:

[0053] In this equation, the outdoor temperature (Toutdoor, d) is the average outdoor temperature for a day d of a series or sequence of 1 to n days.

[0054]

[0049] With the values for the base temperature (Tbase) and the reference temperature (Tret) known, and with the slope of the sloped line of non-zero levels of heating energy consumption also known, it is possible to derive a value for the heating energy consumption to heat the building that is normalized against the influences of the weather (i.e., ENEC). This derivation of the normalized heat energy consumption ENEC presumes that both the setpoint temperature and thermal characteristics of the building (e.g., level of insulation of the walls, windows, etc.) are unchanging (at least for a predetermined time, for example a day).

[0055]

[0050] In a further embodiment the base temperature (Tbase) is obtained by way of an optimization operation, wherein the optimization operation is based on wherein Pintemai is the total daily power level added to the interior of the building by all other devices within the building in the form of heat. This formula accounts for the heat generated within the building (Pintemai), such as from devices, appliances, or occupants, reducing heating requirements. This ensures that the base temperature (Tbase) reflects the actual heating demand after subtracting internal heat contributions, optimizing energy usage. By incorporating UA (the heat transfer coefficient), the method adapts to the building’s insulation and thermal properties. This provides tailored insights specific to the building’s characteristics, further improving efficiency. The operation allows for enhancing real-time responsiveness and accuracy in energy savings.

[0056]

[0051] Additionally or alternatively: a linear fit, wherein the base temperature is determined by way of zero-crossing of the linear curve defined by a linear equation having the principle formula of: wherein a is -At ■ UA and b is At ■ UA ■ Tsetpo int- Einternal

[0057] The linear equation is derived from:

[0058] This equation can be written in the form of the linear equation ax + b: wherein a is -At ■ UA and b is At ■ UA ■ Tsetpoint- Einternal

[0059] The parameters "a" and "b" explicitly incorporate factors such as UA (heat loss coefficient) and Einternal (internal energy contributions). This enables a detailed understanding of how heat loss and internal energy impact boiler energy consumption. Using the zero-crossing point of the linear curve ensures that the base temperature (Tbase) is accurately derived based on actual energy consumption trends. This minimizes calculation errors, aligning the base temperature (Tbase) with real-world heating behavior. The linear equation (Eboiler = a Toutdoor + b) provides an intuitive way to analyze the relationship between heating energy and outdoor temperature (Toutdoor). This facilitates straightforward calculations and visualization for energy savings analysis.

[0060]

[0052] Additionally or alternatively a distribution based approach, wherein the average outdoor temperature (Toutdoor) and the total central heating energy consumption (EC) are determined for a predetermined time period (pdAt), and wherein the data is stored in the first data set and aggregated for a further predetermined time period (pdAt), the resulting dataset is divided into samples of nonzero energy consumption (nonzeroEC) and zero energy consumption (zeroEC) and visualized as probability density functions (PDF) of the nonzero energy consumption function (nonzeroEC) and zero energy consumption function (nonzeroEC) and the overlap range of the zero probability density function and the nonzero probability density function is determined. This has the advantage that dividing the data into nonzero energy consumption (nonzeroEC) and zero energy consumption (zeroEC) enables visualization of usage patterns. This offers a statistical basis for identifying efficiency improvements or anomalies in energy consumption. By analyzing data over predetermined time periods (pdAt), the approach accounts for seasonal variations and temporal trends in energy consumption. The distribution based approach further helps optimizing the base temperature (Tbase) by identifying temperature thresholds for efficient energy usage.

[0053] Each of the methods to determine the base temperature (Tbase) further supports precision, customization, efficiency, and user-friendly analysis, ultimately maximizing energy savings and system performance. Each method provides distinct advantages, improving accuracy and efficiency in determining energy savings: The optimization operation ensures dynamic, real-time adjustments based on internal heat and building thermal characteristics. The Linear Fit simplifies calculations and visualizations while accurately modeling heating energy dynamics. The Distribution-Based Approach offers statistical insights, robust data aggregation, and visualization of energy consumption patterns.

[0061]

[0054] Each of these three methods is able to be performed with relatively limited processing and / or data storage resources such that a system manager with relatively limited processing and / or data storage resources may be used. Thus, it may be that each of these three methods is able to be performed within a loop controller of an energy management appliance (e.g., the controller of the heating appliance such as a boiler), or within a network communications device (e.g., a network hub, switch, modem, edge device, etc.). In this way, the need to provide a physically separate, more sophisticated, and more expensive computer or other device serving as a system manager can be averted.

[0062]

[0055] Each of these three methods includes deriving the base temperature (Tbase). As will be readily recognized by those skilled in the art, when estimating levels of energy consumed in heating a building (as opposed to cooling a building), the reference temperature (Tret) should be chosen to be lower than the base temperature (Tbase), so as to be within a range of temperatures at which the consumption of heating energy is required. However, the reference temperature (Tref) should not be chosen to be too low. As a result of the efforts of the inventors in the present application, it has been determined that consistently good results in estimating the normalized heating energy consumption (ENEC) are achieved by setting the reference temperature (Tref) to be at the median temperature of the daily average temperatures for the set of daily average outdoor temperatures that are associated with the set of data points for days on which heating energy was consumed for heating. Alternatively, good results in estimating the normalized heating energy consumption (ENEC) have been found to be achieved by setting the reference temperature (Tret) to either of the experimentally derived values of 3°C or 5°C.

[0063]

[0056] In some implementations the first of the three methods may employ a calculated balance of levels of heat energy. The reference temperature Tret may be derived in the manner just described. For a relatively simple building, such as an individual house, it is assumed that a balance of power levels exists as described by the following initial equation:

[0064] ^boiler T ^internal UA (^indoor ^outdoor)

[0065]

[0057] Pboiier is the total daily power introduced into the building, in particular in the house by a boiler (or other form of energy management appliance that is used to heat the house), Pintemai is the total daily power level added to the interior of the building, in particular house, by all other devices within the building, in particular house, in the form of heat, U is the overall heat transfer coefficient between the interior of the building, in particular house, and the environment outside of the building, in particular house, A is the surface area of the building, in particular house, the outdoor temperature Tindoor is the daily average indoor temperature within the building, in particular house, and the outdoor temperature Toutdoor is the daily average temperature outside of the building, in particular house.

[0066]

[0058] In using the above initial equation describing a balance of power levels, it is assumed that the gathered data is stored as data points separated by a daily interval of time, and not a shorter interval of time (e.g., hourly). It should be noted that such storage of the gathered data with a daily interval of time (e.g., daily averages and daily totals), as opposed to an hourly interval of time, serves both to reduce storage requirements, and to filter out or smoothen out dynamic heating behavior that may occur across a day long period of time.

[0067]

[0059] Since, as previously discussed, the base temperature Tbase is the outdoor temperature below which heating is required to keep the indoor temperature Tindoor at the setpoint temperature Tsetpoint, the above initial equation enables the base temperature Tbase to be derived as follows: p

[0068] „ > „ ' internal

[0069] ' base ' setpoint

[0070]

[0060] Since it is assumed that the setpoint temperature Tsetpoint is a steady state temperature (at least for a predetermined time period (pdAt)), the further assumption may be made that Tindoor = Tsetpoint (at least for said time period). Thus, the above initial equation may be rewritten to derive Pboiier as a function of Toutdoor:

[0071]

[0061] The following corresponding equation describes the daily aggregated balance of heat energy:

[0072] Wherein At represents a single day of time. The multiplication product At ■ UA represents the slope of the sloped line for the relationship between the outdoor temperature Toutdoor and total amount of heating energy consumed for the set of data points associated with days on which heating energy was consumed. Thus, the slope may be derived as At ■ UA.

[0073]

[0062] From the above equation for calculating Eboiler, the following equation is able to be derived for estimating the normalized energy consumption (ENEC):

[0074]

[0063] With Tret, Tbase and the slope having been derived, ENEC may be determined using the above equation.

[0075]

[0064] It should again be noted that the above approach is at least partially based on an assumption of the gathered data being stored with a daily interval of time such that the interval of time between temporally adjacent entries is a single day. However, if the gathered data was both collected and stored with a shorter time interval than a daily time interval, then the opportunity of such higher frequency data may be used to improve the accuracy of the estimation of the normalized heating energy consumption (ENEC). TO do so, the above initial equation describing a balance of power levels may be modified to add another term that enables better fitting to account for the smaller interval of time At:

[0076]

[0065] The second of the three methods may employ a fitted line of increasing heat energy. The reference temperature Tref may be derived in a manner similar to the first method.

[0077]

[0066] A set of data points in the gathered data for days on which there was a non-zero level of heating energy consumed to heat a building may be separated from another set of data points in the gathered data for days on which there was a zero level of heating energy consumed for the same building.

[0078]

[0067] In an embodiment the linear least squares fitting can be performed to derive a sloping line through the set of data points for the days with a non-zero level of total consumption of heating energy. In this way, a direct relationship can be derived between the heating energy consumed by a boiler Eboiler (or other energy management appliance used for heating) and the outdoor temperature Toutdoor. Also in this way, the slope of that line may be derived, along with the base temperature Tbase for the point along that line at which there is a zero amount of total energy consumed to heat the building.

[0079]

[0068] With Tret, Tbase and the slope having been derived, the normalized energy consumption (ENEC) may be determined by evaluating the linear fit at the reference temperature (Tref).

[0080]

[0069] In further embodiments the third of the three methods can employ the probability density functions. Again, in a manner similar to or as disclosed for the first and second methods, the base Temperature (Tbase) and the reference temperature (Tref) may be derived.

[0081]

[0070] In an embodiment, the third method comprises separating a set of data points in the gathered data for days on which there was a non-zero level of heating energy consumed to heat a building from another set of data points in the gathered data for days on which there was a zero level of heating energy consumed for the same building. Again these two sets of data partially overlap in their outdoor temperatures Toutdoor. Efforts by the inventors in the present application have revealed that the base temperature Tbase is usually a temperature within this range of overlapping temperatures.

[0082]

[0071] In a further step separate probability density functions can be generated for the two sets of data points. Graphical plots can be generated for these two separate probability density functions generated from the two sets of data points.

[0083]

[0072] In some implementations in a further step a rolling filter is separately applied to each of the two probability density functions. The graphical plots for the resulting two smoothened probability density functions, respectively, and the resulting overlapping region.

[0084]

[0073] In some implementations in a further step, the overlapping region is interpolated to increase its temperature resolution.

[0085]

[0074] In some implementations in a further step following such interpolation, the overlapping region may be resampled to generate a pool of samples at the increased resolution.

[0086]

[0075] In some implementations in a further step the base temperature (Tbase) is derived as the median of the pool of samples. Efforts by the inventors in the present application have revealed that such a median relatively easily provides a good approximation of the base temperature (Tbase). Such efforts have also revealed that the corresponding interquartile range of temperatures usually reliably provide the uncertainty for this base temperature. This uncertainty can be graphically depicted as a region of uncertainty.

[0087]

[0076] With Tref, and Tbase having been derived, ENEC may be determined using the aforementioned equation:

[0088]

[0077] Again, in this equation, the variable EC(Tbase, current, Toutdoor, current) is the total heating energy consumption for the current day, with the current base temperature Tbase, and with the average of the entire set of outdoor temperatures measured throughout the current day Toutdoor, current.

[0089]

[0078] In some implementations the third method may enable the calculation of the base temperature (Tbase) with relatively high accuracy. However, supporting its use of probability density functions in the third method requires a relatively large set of data points spanning numerous days that must include a combination of days during which heating is required and days during which heating is not required. In contrast, the second method may require a smaller set of data points spanning fewer days, and its smaller data set may not be required to include a combination of days during which heating is required and days during which heating is not required. However, its use of line fitting may result in less accuracy than the third method. The first method may offer a combination of some of the advantages of both the third method and second method. More specifically, its use of a calculated balance of levels of heat energy may enable the use of a smaller set of data points spanning fewer days, and its smaller set of data points may not be required to include a combination of days during which heating is required and days during which heating is not required. Additionally, its accuracy may be greater than the second method, though not greater than the first method.

[0090]

[0079] Thus, as is explained based on the embodiments above the three methods to determine (Tbase) can be selectively selected, allowing the user to leverage the unique benefits of each respective method. This flexibility ensures a focused and tailored application for the specific requirements of the respective building. By enabling such selective selection, the approach maximizes adaptability and efficiency, ensuring that the most suitable method or combination of methods is utilized to address the building’s unique characteristics and needs.

[0091]

[0080] In some implementations, advantage may be taken of these various characteristics of these differing methods by employing different ones of these differing methods at different times of the year. By way of example, it may be that an implementation makes use of the greater accuracy of the third method by employing the third method during the Spring and / or Fall seasons during which there are combinations of days during which heating is required and days during which heating is not required. Such an implementation may then make use of the lack of need for such a combination of days by employing the first method and / or the second method during the Summer and / or Winter seasons.

[0092]

[0081] In an embodiment the methods to determine the base temperature (Tbase) may be combined sequentially. Methods could be applied one after the other, with the second and third methods refining or validating the results from the first. For example, the third method could establish an initial determination of the base temperature (Tbase) as a probabilistic determination of the base temperature Tbase. The probabilistic determination reflects the complexity of real-world conditions by incorporating uncertainty rather than relying on oversimplified assumptions. The second method could be used to adjust or validate the initial determination based on additional data (e.g., local climate data). In addition the second method can establish a deterministic determination of the base temperature (Tbase) grounded in observed patterns. The first method may be used to fine-tune the value of the base temperature (Tbase) even further. This allows for even further minimizing of deviations or errors between predicted and actual energy consumption (EC). This final step may allow for an accurate and reliable value for the base temperature (Tbase) by reconciling all available data and constraints.

[0093]

[0082] In an embodiment the methods could be combined by assigning weights to each, reflecting their reliability or priority. base -t Tfrase, method 1 T 2 Tb as e, method 2 T S ybase, method 3

[0094] This approach would use statistical modeling to merge the outputs from the three methods into a single value.

[0095]

[0083] In an embodiment the three methods could be applied in parallel, and their results compared to find a consensus or median value. If discrepancies exists, the final Tbase may be adjusted based on pre-defined rules or thresholds.

[0096]

[0084] In an embodiment the combination may be a context-dependent use wherein one of the three methods may be prioritized based on specific parameters. For example, a method which is particularly accurate for a specific location may override the other methods. Alternatively, the three methods could be used interactively (cycling between the methods) until a consistent value is achieved.

[0097]

[0085] In some implementations, the first predetermined time period (pdAt) is a predetermined recurring interval of time, in particular one hour or one day or one week or two weeks or one month. The plurality of average outdoor temperatures include daily average outdoor temperatures, and the total central heating energy consumptions include daily total heating energy consumptions on days for which the total central heating energy consumption was zero and on days for which the total heating energy consumption was non-zero.

[0098]

[0086] In some implementations, the predetermined reference outdoor temperature (Tret) is selected to be near the median of daily average outdoor temperatures (Toutdoor) that lie below the base temperature (Tbase).

[0099]

[0087] In some implementations, the predetermined reference outdoor temperature (Tret) is 3° C or 5° C. This is a reference temperature (Tret) which has a good operating range and a low nonlinear error within a ± 1 °C range for numerous applications.

[0100]

[0088] In some implementations, calculating the weather independent normalized heating energy consumption for the building based on the first data set includes using a linear fit through the data of the first data set.

[0101]

[0089] In some implementations, the weather independent normalized heating energy consumption for the building is determined by evaluating the linear fit at the predetermined reference outdoor temperature.

[0102]

[0090] In some implementations, the linear fit includes a least squares fit. The least squares fit is a mathematical method used in regression analysis to find the best-fitting curve or line to a set of data points. It minimizes the sum of the squared differences (residuals) between the observed data points and the values predicted by the model. The primary objective of the least squares fit is to minimize the sum of the squared residuals. The residuals are the differences between the observed values and the predicted values. This has the additional advantage that the least squares fit minimizes the sum of the squared residuals (differences between observed and predicted values). This ensures that the overall error in the fit is as small as possible. In addition, the least squares fit is straightforward to apply and interpret.

[0091] In some implementations, calculating the weather independent normalized heating energy consumption for the building based on the first data set includes fitting the first data set to a model of a heat balance for the building. The heat balance has the advantage that it allows taking into account a non-steady state of the building.

[0103]

[0092] In some implementations, calculating the weather independent normalized heating energy consumption for the building based on the first data set includes calculating a heating energy consumption per heating degree day, wherein heating degree days are determined by subtracting the average temperature at the building location over a day from the base temperature if the average temperature at the building location over the day is lower than the base temperature, and determining the heating degree days to be zero for days on which the average temperature at the building location over the day is greater than or equal to the base temperature.

[0104]

[0093] In some implementations, calculating the base temperature (Tbase) includes: calculating a zero energy probability density function based on outdoor temperature (Toutdoor) and corresponding measured total central heating energy consumptions in the first data set for which the measured total central heating energy consumption was zero, the zero energy probability density function providing a probability of having a total central heating energy consumption of zero for a given outdoor temperature (Toutdoor) ; calculating a nonzero energy probability density function based on outdoor temperature (Toutdoor) and corresponding measured total central heating energy consumptions (EC) in the first data set for which the measured total central heating energy consumption (EC) was non-zero, the nonzero energy probability density function providing a probability of having a non-zero total central heating energy consumption (EC) for a given outdoor temperature (Toutdoor) ; determining an overlap range of outdoor temperatures (Toutdoor) over which the zero energy probability density function and the nonzero probability density function overlap; and calculating the base temperature (Tbase) to be a temperature within the overlap range.

[0105]

[0094] In some implementations, the first predetermined time period (pdAt) has a duration of one hour or one day.

[0095] In some implementations, determining an average outdoor temperature (Toutdoor) includes accessing the average outdoor temperature (Toutdoor) for a location of the building in a database, in particular in a remotely maintained database. Centralized data from a database can seamlessly integrate with other components of the energy management system. A database typically maintains historical weather data, allowing analysis of trends and patterns. This enables the system to calculate energy savings over extended periods, incorporating seasonal variations. A remotely maintained database further allows for multiple users or systems being able to simultaneously access and update data within the database. Remote databases are typically equipped with robust backup and disaster recovery solutions. Easily integrates with various software, applications, and analytics tools. Expands functionality and allows for more in-depth data analysis and optimization. A remotely maintained database simplifies data management while offering scalability, security, accessibility, and cost benefits

[0106]

[0096] In some implementations, determining an average outdoor temperature includes measuring the outdoor temperature at the location of the building using a temperature sensor. Direct measurement ensures the outdoor temperature reflects the immediate and specific conditions at the building's location. This provides highly accurate data for energy management calculations, improving the precision of savings and system adjustments. The temperature sensor can in particular be a temperature sensor of an existing energy management system allowing to easily update the existing energy management system in particular by updating a data processing device comprised in the energy management system.

[0107]

[0097] In some implementation the method further comprises the step of providing such results of such analyses to a user of the energy management system, visually presented on a viewing screen of the energy management system, in particular on a controller of the energy management system (e.g., a central controller of the energy management system, and / or a loop controller of an energy management appliance of the energy management system, such as a thermostat, control panel, and / or wall-mounted interface), and / or on a viewing screen of a (remote) portable device (e.g., a tablet computer, a portable computer, a laptop, a smartphone, and / or a smart display). Such a visualization may be provided through a web browser, through an application programming interface (API), and / or using any of a variety of other communication protocols for transmitting visual data and / or using any of a variety of other application level visualization viewing routines.

[0108]

[0098] In some implementations the method further comprises the step of generating a report command, wherein the report command includes actionable insights and recommendations and / or commands for controlling an energy management system to optimize energy consumption. Actionable recommendations generated by the method can feed back into self-learning algorithms, enabling the system to refine and improve over time. This further enhances the ability of an energy system using the method to deliver increasingly accurate and effective energy-saving strategies. In summary, the addition of a report command enhances the energy management system by combining actionable insights with automation, empowering users to save energy, reduce costs, and operate sustainably. It bridges the gap between system intelligence and user accessibility, creating a seamless and effective energy optimization process.

[0109]

[0099] In some implementations the method further comprises the step of a. updating the modeled savings predictions, in particular continuously, based on at least the information stored in the first data set; and / or b. generating energy predictions using the developed modeled savings predictions and presenting the modeled savings predictions through a user interface; and / or c. generating a report based on the modeled savings predictions and / or future energy consumption trends, wherein the report includes actionable insights and recommendations for predictive support and / or maintenance to optimize the energy management system and / or to optimize the performance of the energy management system; and / or d. initiating predictive maintenance and / or support actions based on the recommendations in the report command, in particular initiating scheduling commands for maintenance tasks, initiating adjusting operational parameters, and / or notifying relevant personnel.

[0110]

[0100] The updating of the modeled savings predictions has the advantage of rea-time accuracy and dynamic adaptability. The visualization is user-friendly. Actionable insights and recommendations allows for reports providing a detailed summary of savings predictions, future trends, and system performance. This offers users a holistic understanding of their energy usage and potential improvements. Actionable guidance helps users and system administrators implement practical energy-saving strategies. Predictive maintenance and / or support actions minimize downtime and ensure consistent performance. This further reduces manual intervention, improving operational efficiency. Early detection and resolution of maintenance needs lower repair costs over time.

[0111]

[0101] In particular combining the steps leads to an even more improved efficiency keeping the energy management system optimized with continuous updates and targeted maintenance. Provides users with clear insights, actionable recommendations, and intuitive interfaces. Reduces energy waste and system inefficiencies even further, contributing to even lower emissions. Proactively manages system performance, ensuring longevity and reducing disruptions and operational reliability.

[0112]

[0102] In an embodiment the normalized heating energy consumption ENEc for heating a building for the current day, with the current day's average outdoor temperature for the entirety of the current day, and with the current base temperature (unchanging as a result of an unchanging setpoint temperature and unchanging thermal characteristics of the building) may be calculated as:

[0113]

[0103] In this equation, the variable EC(Tbase, current, Toutdoor, current) is the total heating energy consumption for the current day, with the current base temperature (Tbase), and with the average of the entire set of outdoor temperatures measured throughout the current day (Toutdoor , current).

[0114]

[0104] However, as discussed earlier, multiple factors beyond outdoor temperatures exert influence over when heating a building is or is not required to maintain the setpoint temperature within. By way of example, and as previously discussed, the exposure of portions of a building to sunlight during the day can influence whether energy must be consumed to heat the building during at least a portion of the day, and / or at what outdoor temperature such energy consumption begins to be required. On a clear day, a building may be exposed to more sunlight, which may entirely obviate the need to consume such energy during a portion of that day, or at least lower (Tbase) such that the outdoor temperature must drop lower before heating-related energy consumption is required. In contrast, during a cloudy day (or at night), a lower amount or lack of sunlight may raise the base temperature (Tbase) such that the outdoor temperature need not drop as low before heating-related energy consumption is required.

[0115]

[0105] As a result, such data as average daily temperatures and daily total heating energy consumption may be subject to weather influences. In particular, the set of data points for days during which the total heating energy consumption is zero may partially overlap, by multiple degrees of temperature, with the set of data points for days during which the total heating energy consumption is a nonzero amount.

[0116]

[0106] This overlap may complicate efforts to derive the base temperature (Tbase), and the slope of the sloped line of non-zero levels of heating energy consumption. Accordingly, this complicates efforts to accurately derive the normalized heating energy consumption (ENEC), and in turn, this complicates efforts to generate and maintain a model of energy consumption for heating a building.

[0117]

[0107] However, as a result of the efforts of the inventors in the present application, it has been determined that the normalized heating energy consumption (ENEC) is able to be determined with a degree of accuracy that enables a relatively accurate model of energy consumption for heating a building to be generated and maintained. Also, such an estimation has been found to be possible through the use of any one of three different approaches that are based on gathered data that includes average daily temperatures and total daily consumption of energy for heating a building that spans a period of numerous days, such as a period of multiple weeks or multiple months.

[0118]

[0108] In an embodiment the method comprises a further step for estimating energy savings for the particular day d for which a prediction of energy savings is to be made, the base temperature Tbase may be used to calculate the heating degree days HDD using the aforementioned equation: Tbase ^outdoor, d ' (TbaseToutdoor d

[0119] HDD = d = l Tbase — Toutdoor, d 0

[0120]

[0109] It is assumed that, when the outdoor temperature is below the base temperature (Tbase), the heating energy consumption (EC) scales linearly with the outdoor temperature. Thus, the heating energy consumption (EC) also scales linearly with the heating degree days (HDD). It is assumed that the slope of this linear scaling relationship is constant, which may be expressed as: constant

[0121]

[0110] It is also assumed that changes made to the setpoint temperature (Tsetpoint) should directly change the base temperature (Tbase).

[0122]

[0111] The assumption about the linearity of the between heating energy consumption EC and HDD, and the assumption about the relationship between the base temperature Tbase and the setpoint temperature Tsetpoint can be combined to compute the predicted energy savings for a reduction in the heating energy consumption EC as follows:

[0123] Energy Savings

[0124]

[0112] ATsetpoint represents the change in the setpoint temperature from the setpoint temperature Tsetpoint that may have been used in the aforementioned analyses to derive the base temperature Tbase. The term energy savings within the application is defined as a saving in energy consumption based on the following: By lowering the setpoint temperature (Tsetpoint) by one degree relative to a first setpoint temperature (Tsetpoint), the energy consumption (EC) is reduced to 90% of the energy consumption (EC) associated with the first setpoint temperature (Tsetpoint), resulting in a 10% energy savings. The energy savings may be represented either as 90% of the energy consumption (EC) of the first setpoint temperature (Tsetpoint) or as a 10% reduction in the energy consumption (EC) relative to the first setpoint temperature (Tsetpoint). This representation indicates a comparison between the original energy consumption and the reduced energy consumption, with the difference, equal to 10%, quantifying the energy savings achieved through the modification of the setpoint temperature (Tsetpoint). The method explicitly accounts for the energy consumption (EC) in relation to the setpoint temperature (Tsetpoint) and the base temperature (Tbase), thereby enabling precise quantification of the impact of changes to the setpoint temperature (Tsetpoint) on total energy consumption (EC) and energy savings potential.

[0125]

[0113] In some implementations the method further comprises the step of modeling savings predictions and / or future energy consumption trends based on

[0126] EC(TbaseT Tsetpoint HDD(Tbase+ Tsetpoint)

[0127] Energy Savings =

[0128] EC(Tbase) HDD(Tbase) wherein ATsetpoint[°C] is the change in setpoint temperature with respect to the setpoint temperature (Tsetpoint) used during a predetermined analysis period. This implementation has the advantage that the formula explicitly accounts for the energy consumption (EC) when the setpoint temperature (Tsetpoint) is adjusted relative to the base temperature (Tbase). This allows for a particularly optimal quantification of how small or large changes in the setpoint temperature (Tsetpoint) affect total energy consumption, enabling precise evaluation of savings potential. By incorporating future energy consumption trends, the model enables predictions of how setpoint temperature (Tsetpoint) changes, insulation changes or user behavior adjustments, impact the energy consumption (EC). This allows for proactive adjustments to heating schedules and system settings to maximize energy efficiency ahead of time.

[0129]

[0114] As previously discussed, this energy savings determination may be used in further analyses to derive recommendations in considering changes to using more environmentally friendly technologies for heating, and / or in considering the addition of insulation and / or other changes to make to a building.

[0130]

[0115] In some implementations, in case the granularity of the data points is increased from daily temperatures and measures of heating energy consumption to hourly, then more sophisticated predictive analyses may be performed to deal with energy-related variables that are otherwise unseen with a daily granularity, such as variations in the production of electricity that occur within each day. Such an hourly granularity (or other granularity finer than whole days) also makes visible such aspects of energy as peak demand hours for electricity received from a grid.

[0131]

[0116] In some implementations, a visual representation indicating the degree of efficiency of heating energy consumption for a specific month (e.g., December 2023) to heat a particular building, such as a house or other structure, may be provided. This visualization can be displayed on a viewing screen of an energy management system, in particular an energy management system controller, or on a (remote) portable device, such as a tablet or smartphone.

[0132]

[0117] In an embodiment, a method is provided for guiding a user of an energy management system to improve the efficiency of heating energy consumption for a specific building, such as a house or other structure. The method involves determining energy savings over a predetermined period, for example, at least the most recent month, during which the current setpoint temperature (Tsetpoint) of the energy management system has been consistently maintained, and for which a corresponding set of data points is available. It should be noted that, depending on the current seasons and / or the quantity of months that such a set of data points spans, different ones of the earlier-described methods may be used alone or in combination. More specifically, where such a set of data points is available for such a continuously used current setpoint temperature (Tsetpoint, current) , and where the multiple months covered by such a set of data points and setpoint temperature extend through a season of Spring or Fall where there are days during which heating is required and days during which heating is not required, then the third method may be used. However, where either the available set of data points or the continuous use of the current setpoint temperature (Tsetpoint, current) does not extend backward in time through multiple months, or where such multiple months do not include a combination of days in which heating was required and days in which heating was not required, then either the second method and / or the first method may be used. The third method may be used in combination with the first and / or second method.

[0118] In an embodiment an indication of determined energy savings, such as an indication of a degree of efficiency of heating energy consumption, may be visually presented on a viewing screen of an energy management system, in particular on a controller of the energy management system, or of a (remote) portable device. This implementation further enhances user engagement, convenience, and transparency while supporting real-time decision-making and promoting sustainable energy practices.

[0133]

[0119] In some implementations, determined energy savings are displayed to a user of an app operating on a mobile device. This has the additional advantage that displaying energy savings on a mobile app ensures convenience, engagement, and real-time access, empowering users to take control of their energy consumption while promoting sustainability and cost-efficiency.

[0134]

[0120] In some implementations, determined energy savings are expressed as at least one of a fraction, a percentage, an amount of energy in kWh, an amount of carbon emissions, or an amount of money. This flexibility in representation ensures savings are both understandable and actionable, empowering users and stakeholders to make informed decisions and drive energy efficiency.

[0135]

[0121] In some implementations, the energy savings are determined on an hourly basis. Hourly estimation of energy savings enhances the precision, responsiveness, and flexibility of energy management systems, empowering users to optimize efficiency and reduce costs effectively.

[0136]

[0122] In an embodiment a determination may be made as to whether or not to visually present a suggestion to a user of the energy management system to change the setpoint temperature to increase such efficiency, and thereby increase such energy savings. It should be noted that, the current level of energy efficiency or energy savings may be compared to a predetermined minimum threshold level thereof as part of making such a determination.

[0137]

[0123] In an embodiment input indicative of a change to the setpoint temperature may be awaited. It should be noted that such awaiting of such input may take place regardless of whether the suggestion to make such a change is visually presented, or not.

[0138]

[0124] In an embodiment in case such input to change the setpoint temperature is received, then: 1 ) the setpoint temperature may be so changed,

[0139] 2) a new set of data points for a new month may be collected, and / or

[0140] 3) at least a new indication of energy savings for at least the new month may be determined based on at least the new set of data points and visually presented. It should be noted that, as a result of having a new setpoint temperature and just the new month's set of data points, it may be that the new determination may need to be generated using either the first method and / or the second method.

[0141]

[0125] It is another object to provide an energy management system that serves to heat a building, and that incorporates such relatively simple and inexpensive processing and / or data storage resources to generate and maintain such a normalized model of heating-related energy consumption of the building.

[0142]

[0126] In some implementations, the disclosed technology provides an energy management system comprising a data processing device, in particular a controller, in particular a processor, and a memory to store program instructions operable to cause the processor to perform operations according to the method. The energy management system in particular serves to heat a building, and incorporates a simple and inexpensive processing and / or data storage resources to generate and maintain the normalized model of heating-related energy consumption of the building. Such an energy management system implementing an energy-saving method integrates advanced analytics, predictive modeling, and smart device communication to create a highly efficient, cost-effective, and sustainable solution for managing heating and energy consumption in buildings. Such an energy management system can be called a self-learning energy management system. The energy management system can continually analyze, learn and improve its energy-saving operations based on real-time data, historical patterns as well as predictions. It is specifically designed to optimize energy consumption while adapting to changing conditions and user behaviors without requiring constant manual input or predefined rules. This all is possible using an inexpensive data processing device, such as a controller, according to the invention. Such an energy management system can further tailor energy management to the unique characteristics of the building and its occupants in a straightforward way and easy to implement and execute. The energy management system according to the invention thus allows to reduce waste and to optimize resource usage. The energy management system further minimizes energy bills through smart adjustments and proactive measures. The energy management system also supports eco-friendly practices by reducing energy consumption and carbon emissions. The energy management system operates autonomously while empowering users with detailed reports and insights.

[0143]

[0127] In some implementations, the disclosed technology provides a computer program product including program instructions operable to cause a processor to perform the method according to the invention.

[0144]

[0128] In some implementations the computer program product can comprise instructions to cause the energy management system according to the invention to execute the steps of the method according to the invention.

[0145]

[0129] It is another object to provide an inexpensive controller for one or more energy management systems that serve to heat a building, where the controller generates and maintains such a normalized model of heating-related energy consumption at the building.

[0146]

[0130] The object is achieved by a data processing device comprising means for carrying out the method according to the invention. The method according to the invention allows for such a straightforward that in its most simple implementation only a thermostat is needed to provide the required measurements. The low computational requirements and low hardware requirements of the method allow for the method to be run on a data processing device which is inexpensive and could even be an existing data processing device, if the data processing device can be updated to carry out the method according to the invention. So due to the low computational and hardware requirements of the method according to the invention it is possible to update the data processing device of an existing energy management system.

[0147]

[0131] Alternatively or additionally, such a controller may be a virtual controller located remotely (e.g., cloud-based), but its relatively minimal processing and / or data storage requirements may enable the provision of such a virtual controller for a minimal fee or no fee, at all. Indeed, such minimal processing and / or data storage requirements may enable a greater quantity of such virtual controllers to be maintained within a smaller quantity of remotely located servers, and / or may enable older servers of lesser capability than newer ones to be used. In this way, older servers may be given a new function by which their productive years in use may be extended. Further, with such a remotely located controller, continuity of data gathered at the building may be maintained by being preserved in remotely located storage on such a remotely located server, thereby allowing components of an energy management system to be entirely replaced without a risk of loss of such data.

[0148]

[0132] In some implementations the data processing device is a controller, in particular a computer system or embedded controller, or a processor. Such a controller may be a physical controller installed at the building, and may be capable of generating and maintaining such a model without the aid of processing and data storage resources of remotely located devices.

[0149]

[0133] In some implementations such a controller may be incorporated into a thermostat by which the setpoint temperature of the building may be manually set.

[0150]

[0134] In some implementations the controller is configured to re-generate, on a recurring basis, the normalized model of the consumption of heating energy at a building to perform various analyses based on the method according to the invention. Such a recurring basis may be hourly, daily, weekly, bi-weekly, monthly, or still other recurring intervals of time. Such analyses may include the derivation of recommendations for improvements to insulation and / or other recommendations to improve the ability of the building to conserve heat energy, thereby increasing the efficiency and / or effectiveness with which the building is able to be heated. Such analyses may include the derivation of recommendations to add electrical, thermal and / or other forms of energy storage to the building to improve the efficiency and / or cost effectiveness with which the building is heated. Such analyses may include detecting changes in patterns of user behavior, and providing notices of such changes to users, especially where such changes result in increased consumption of heating energy. Such analyses may include predicting levels of consumption of heating energy in view of weather forecasts for upcoming hours and / or days. Such analyses may include the derivation of recommendations for user behavioral changes to increase the efficiency and / or cost effectiveness with which the building is heated.

[0151]

[0135] In some implementations the data processing device, in particular the controller, may comprise one or more microprocessors or processors and / or integrated circuits and / or a memory and / or a storage interface and / or a communication interface. These system components may be interconnected via a bus, which may include one or more internal and / or external buses (e.g. a PCI bus, universal serial bus, IEEE 1394 “Firewire” bus, SCSI bus, Serial-ATA bus, etc.), to which these various hardware components may be electronically coupled. This design provides a versatile, scalable, and efficient solution for data processing, making the device suitable for advanced applications with reliable performance and connectivity.

[0152]

[0136] In some implementations the one or more microprocessor or processor and / or integrated circuits are configured to execute program instructions stored in the memory. This configuration promotes flexibility, scalability, and cost efficiency, while supporting dynamic adaptability and advanced functionalities, making it ideal for modern systems that require efficient and versatile operations.

[0153]

[0137] In some implementations the controller or the one or more processors may include one or more microprocessors and / or integrated circuits able to execute program instructions stored in the memory. When the data processing device, in particular the controller starts up, the processor(s) may initially execute program instructions of a boot routine and / or the program instructions that make up the operating system.

[0154]

[0138] In some implementations the data processing device, in particular the controller (100) or at least one microprocessor or processor, is incorporated into a networking device, in particular at the edge of a network installed at the building. Incorporating the processing device into a networking device at the network edge enhances speed, security, reliability, and efficiency, making it an ideal solution for modern, intelligent energy management systems.

[0155]

[0139] In some implementations the data processing device, in particular the controller (100) or at least one microprocessor or processor, in particular at least one of the components thereof, is virtualized and / or cloud-based. Virtualizing and / or hosting the data processing device in the cloud enhances scalability, accessibility, resource efficiency, and reliability while reducing costs and hardware dependencies. This approach aligns with modern demands for flexibility and robust functionality.

[0140] In some implementations the memory, which may be a random -access memory or any other type of memory, is configured to contain data, an operating system, and a program, in particular a computer program product according to the invention. The data may be any data that serves as input to or output from any program in the controller. The operating system may be an operating system such as MICROSOFT WINDOWS, LINUX, FreeRTOS, or any other operating system suitable for use on a computer system or microcontroller. This memory configuration enhances efficiency, scalability, and adaptability while reducing costs and system complexity, making it an ideal approach for modern technology solutions.

[0156]

[0141] In some implementations the program may be any program or set of programs that comprise(s) program instructions that when executed by the data processing device, in particular by the controller or at least one microprocessor or processor, cause the control of actions taken by the data processing device, in particular the controller or at least one microprocessor or processor. Additionally or alternatively, the program can comprise program instructions that, when executed by the data processing device, in particular the controller or at least one microprocessor or processor, cause the data processing device, in particular controller or at least one microprocessor or processor, to carry out one or more of the methods according to the invention. Program or set of programs within the application refers to one or more sequences of program instructions, implemented in software, firmware, or a combination thereof, that are configured to be executed by a data processing device, such as a microprocessor, processor, or controller. These instructions, when executed, perform specific computational functions, control actions of the device, and / or enable the execution of methods disclosed herein. The term encompasses individual programs as well as collections of interdependent or independent programs that collectively achieve the intended functionality.

[0157]

[0142] In some implementations the storage interface is configured to connect storage devices to the data processing device, in particular the controller. Configuring the storage interface delivers flexibility, scalability, and efficiency while enhancing the capability of the energy management system to handle data- intensive tasks and maintain reliable operations.

[0143] In some implementations the storage interface comprises a storage device, wherein the storage device can be a solid-state drive using an integrated circuit assembly to store data persistently. Alternatively or additionally, the storage device may be a hard drive using any of a variety of types of magnetic storage media to store and retrieve digital data. Alternatively or additionally, the storage device may be an optical drive, or a card reader that receives a removable non-volatile semiconductor memory card. Alternatively or additionally, the storage interface is configured to provide a universal serial bus connection to which the storage device is configured be hot-pluggable, and the storage device may be a flash memory device, in particular a USB thumb drive. Such storage interface configurations provide advantages for data processing devices, particularly controllers. The configurations support various types of storage devices, including SSDs, HDDs, optical drives, card readers, and USB flash drives, offering diverse storage options to meet specific needs. The configurations enable easy expansion of storage capacity by allowing the connection of additional or removable devices.The configurations facilitate the organization, retrieval, and backup of data through versatile storage solutions. The configurations allow devices to be connected or disconnected while the system is operational, minimizing downtime, in particular by way of hot-plugging. The configurations reduce the need for built- in storage upgrades, providing an economical approach to storage scalability. The configurations ensure interoperability with both legacy and modern systems, supporting a wide range of applications. The configurations allow for adapting to evolving storage technologies, prolonging system usability and relevance. The configurations ensure persistent and dependable storage for critical data using advanced storage technologies. These advantages of the configurations, alone or in combination, enhance the functionality, adaptability, and reliability of the data processing system, ensuring optimized performance and long-term efficiency.

[0158]

[0144] In some implementations, the data processing device, in particular the controller is configured to use virtual memory addressing techniques. The virtual memory addressing techniques allow the programs of the controller to behave as if they have access to a large, contiguous address space instead of access to multiple, smaller storage spaces, such as the memory and the storage device.

[0145] In some implementations the communication interface is configured to communicatively connect the controller to other controllers, computer systems, or connected or network-enabled devices via a communication channel. Data and / or program instructions may be sent to the controller as signals via the communication channel. The connected or network-enabled devices can comprise "data-driven devices", "peripheral systems", and / or "computational nodes. Examples of suitable devices comprise smart thermostats, network- connected heating appliances, or heating systems, HVAC units or HVAC systems, sensors, in particular temperature sensors, occupancy sensors, which detect room usage, humidity sensors to maintain optimal heating conditions, smart radiators with network-enabled controls for zone specific heating, energy storage units, smart metering devices, home automation controllers, smart windows and / or smart blinds. Connecting the data processing device of the invention to controllers, computer systems or connected or network enabled devices allows for the efficient automated control of such devices based on the energy consumption determination according to the invention. This allows the data processing device the dynamic management of connected devices such as thermostats, radiators and the like based on the determinations. For example, generating control commands to automatically adjust heating schedules or temperatures in specific zones also taking into account additional information such as occupancy data. This allows for reducing heating in unoccupied rooms or adjusting settings based on the determined energy consumption data. In summary, connecting the data processing device according to the invention enables automatic, intelligent, adaptive, and efficient energy management, which further reduces costs, enhances comfort, and further contributes to sustainability efforts.

[0159]

[0146] In some implementations, the communication channel is a serial or parallel connection, a wired, wireless, mesh or cellular network or a combination of communication channels. The flexibility to use different types of communication channels ensures compatibility with a wide range of devices and systems. For example, wireless connections can be used for mobile devices like smart thermostats, while wired connections may serve stationary systems like heating controllers or boilers. Different communication channels suit varying building layouts or environmental conditions. Wired connections are ideal for permanent, robust connections in industrial or high-interference areas. Wireless connections are well suited for retrofitting older buildings without extensive rewiring. Mesh connections are effective in large, multi-zone buildings where devices need to relay data across a network. Mesh and cellular networks further facilitate integration with Internet of Things (loT)-enabled devices, allowing centralized monitoring and control. For example, sensors across the building can transmit real-time temperature and energy data via wireless or mesh networks to the energy management system. Further, the ability to mix and match communication channels helps optimize costs. Wired connections are inexpensive for stationary devices. Cellular networks are more suitable for remote or mobile systems where wiring isn’t feasible. A combination of communication channels further enables in a straightforward way multiple communication channels in an energy management system, for example the energy management system according to the invention. This ensures flexibility, reliability, and efficient integration with diverse devices and setups, making the data processing device adaptable to various building environments and future needs.

[0160]

[0147] In some implementations the communication interface comprises a combination of hardware and software that enables communications on the communication channel. Such a hybrid hardware-software approach allows the communication interface to bridge the gap between physical devices (hardware) and data management / processing (software). This ensures smooth interaction and compatibility across various communication channels, whether wired, wireless, mesh, or cellular. The combination thus allows the communication interface to adapt to multiple types of communication channels. Hardware can support physical connections (e.g., wired, serial, or parallel links). Software can handle complex protocols (e.g., wireless encryption, routing algorithms, or cellular network integration). A software-driven system can update and optimize communication methods without needing new hardware, reducing costs associated with upgrades. The combination of hardware and software in the communication interface ensures efficient, flexible, and future-ready connectivity, making it essential for managing diverse communication needs in modern systems such as energy management, industrial control, or loT networks.

[0148] In some implementations the software in the communication interface comprises software that uses one or more communication protocols to communicate over the communication channel, including and not limited to, network protocols such as TCP / IP (Transmission Control Protocol / lnternet Protocol). TCP / IP is a widely adopted communication standard, ensuring interoperability between a wide range of devices and systems. This allows for seamless integration of devices. For example, devices from different manufacturers can communicate seamlessly using these standard protocols, avoiding compatibility issues. This is in particular relevant for energy management systems, in particular according to the invention, which rely on various connected devices, such as smart meters, HVAC systems, temperature sensors, and thermostats. In larger buildings with multiple zones, TCP / IP facilitates scalable communication across a network of devices, allowing the system to assess and manage energy consumption zone by zone. In addition, using a standardized communication protocol allows the data processing device to act as a central hub, gathering data from all connected systems. Protocols like TCP / IP further allow the data processing device to be remotely accessible in a straightforward manner. TCP / IP protocols support encryption and secure authentication, protecting sensitive energy usage data. The data processing device can further easily be integrated with Internet of Things (loT)-enabled devices, creating a robust ecosystem for energy management. For example loT-connected smart blinds, thermostats, and lighting systems can work together under the guidance of the data processing device to support optimizing energy consumption based on the determination of the invention. Thus, the resulting data processing device is particularly an intelligent, reliable, and a particularly efficient solution for use in optimizing energy consumption in buildings.

[0161]

[0149] In some implementations the data processing device, in particular the controller, is configured to re-generate on a recurring basis, a normalized model of the consumption of heating energy (ENEC) of the building, in particular in accordance with the method according to the invention. This allows for ensuring that the model reflect the most current data on energy consumption and heating requirements. For example, the adaptation to seasonal variations, changes in occupancy, or updates to the infrastructure of the building are incorporated in the determination in accordance with the invention. In other words, the data processing device remains responsive and adaptive rather than relying on static or outdated assumptions. In addition, recurring updates improve the accuracy of predictive analytics. In summary, the data processing device remains accurate, responsive, and optimized in its operation for the building's current conditions. This leads to greater efficiency, cost savings, and sustainability, while also empowering dynamic and informed decision-making for energy consumption.

[0162]

[0150] In some implementations the recurring basis is hourly, daily, weekly, biweekly, monthly, or a predetermined recurring interval of time.

[0163]

[0151] In some implementations the data processing device, in particular the controller, is configured to perform an analysis based the normalized model of the consumption of heating energy (ENEC) obtained based on the method according to the invention.

[0164]

[0152] In some implementations the analysis comprises the derivation of recommendations for improvements to insulation and / or other recommendations to improve the ability of the building to conserve heating energy. This allows for further optimizing energy conservation, further reduction of costs, further enhanced comfort, further improved sustainability, and further ensures a data- driven, tailored approach to energy management.

[0165]

[0153] In some implementations the analysis further comprises a. the derivation of recommendations to add electrical, thermal and / or other forms of energy storage to the building; thus, adding electrical, thermal, or other energy storage enables the building to store excess energy generated during off-peak hours or from renewable sources (e.g., solar panels), ensuring this energy is available when demand peaks.; and / or b. the analysis further comprises detecting changes in patterns of user behavior, and providing notices of such changes to users; such notifications about changes in user behavior can help occupants understand how their actions impact energy consumption, promoting more energy-conscious habits.; and / or c. wherein the analysis further comprises predicting levels of consumption of heating energy in view of weather forecasts for upcoming hours and / or days; thus, for example the energy management system can adjust heating schedules based on anticipated weather conditions, ensuring the building is prepared for upcoming temperature changes; and / or d. the analysis further comprises the derivation of recommendations for user behavioral changes to increase the efficiency and / or cost effectiveness with which the building is heated. Providing actionable recommendations (e.g., reducing thermostat settings or closing doors in heated areas) directly impacts energy conservation.

[0166]

[0154] When implemented together, these analytical features create a holistic energy management system that integrates technological optimizations (e.g., energy storage and predictive analytics), behavioral adjustments (e.g., tailored user notifications and recommendations), and proactive planning based on weather forecasts. These elements collectively maximize energy efficiency, lower costs, and enhance sustainability for the building even further.

[0167]

[0155] In some implementation the method further comprises the step of providing such results of such analyses to a user of the energy management system, visually presented on a viewing screen of the energy management system, in particular on a controller of the energy management system (e.g., a central controller of the energy management system, and / or a loop controller of an energy management appliance of the energy management system, such as a thermostat, control panel, and / or wall-mounted interface), and / or on a viewing screen of a (remote) portable device (e.g., a tablet computer, a portable computer, a laptop, a smartphone, and / or a smart display). Such a visualization may be provided through a web browser, through an application programming interface (API), and / or using any of a variety of other communication protocols for transmitting visual data and / or using any of a variety of other application level visualization viewing routines.

[0168]

[0156] In some implementations the data processing device, in particular the controller, is incorporated into a thermostat by which the setpoint temperature (Tsetpoint) of the building may be set, in particular manually set. Incorporating the data processing device into the thermostat provides a streamlined, user-friendly, and energy-efficient solution for managing the building’s heating system. It balances advanced automation with manual flexibility, empowering users while enhancing the overall effectiveness of the energy management system.

[0157] In some implementations the data processing device, in particular the controller, is a remotely located controller, and wherein the data processing device, in particular the controller, is configured to communicate with a remote storage, in particular a remote server, wherein the continuity of data gathered of the building is maintained the controller being configured to preserve the data in the remote storage, in particular on the remotely located server. The remotely located controller and its communication with remote storage allow for data continuity, scalability, security, remote access, advanced analytics, and costefficiency, making the system robust and adaptable even for remotely located buildings. This setup further supports reliable energy management.

[0169]

[0158] The invention further relates to a networking device wherein the networking device is configured to perform the method according to the invention. Such a networking device in a straightforward way ensures seamless execution, scalability, and integration with modern technological ecosystems. The networking device further enhances connectivity, efficiency, security, and adaptability, thus allowing for seamless executing and implementing of the invention according to the application.

[0170]

[0159] In an embodiment the networking device comprises: a. a processor; and / or b. a memory operatively connected to the processor; and / or c. a communication interface configured to connect the device to a network.

[0171]

[0160] In an embodiment the networking device is a router, hub, switch, or an edge device such as a modem. In this way, the need to provide, either remotely or locally at the building, a physically separate controller incorporating more sophisticated and expensive processing and / or data storage resources is averted.

[0172]

[0161] The invention further relates to a computer readable data carrier having stored thereon the computer program product according to the invention.

[0173]

[0162] The invention further relates to a data carrier signal carrying the computer program product according to the invention.

[0174]

[0163] It is another object to provide an energy management appliance that serves to heat a building, and that incorporates such relatively simple and inexpensive processing and / or data storage resources to generate and maintain such determinations of energy savings in heating-related energy that is consumed to heat the building.

[0175]

[0164] The objective is solved by an energy management appliance configured to operate based on the method according to the invention.

[0176]

[0165] In some implementations the energy management appliance comprises a data processing device according to the invention, in particular a controller and a memory storing instructions which, when executed by the data processing device perform operations comprising implementing the method according to the invention.

[0177]

[0166] In some implementations the energy management appliance is a device generating usable energy and / or manages, controls, and / or senses conditions relevant to usable energy in a form such as heat, cold, mechanical, and / or electrical energy.

[0178]

[0167] In some implementations the energy management appliance is a boiler, a heat pump, an air conditioning unit, a fire place and / or a ventilation device.

[0179]

[0168] In some implementations the energy management appliance is comprised in an energy management system, in particular an energy management system according to the invention.

[0180]

[0169] The invention further relates to the use of a method according to the invention determining, reporting, actioning, and / or predicting energy savings for heating a building, in particular predictions for cost savings, carbon footprint reduction, performance monitoring, and / or predictive maintenance and / or support and / or control, in particular remote support and / or control, of an energy management system, in particular an energy management system according to the invention, or of an energy management appliance, in particular an energy management appliance according to the invention.

[0181]

[0170] In the context of the present specification, unless expressly provided otherwise, the words “first”, “second”, “third”, etc. have been used as adjectives only for the purpose of allowing for distinction between the nouns that they modify from one another, and not for the purpose of describing any particular relationship between those nouns.

[0171] In the context of the present specification, unless expressly provided otherwise, directions indicated by terms such as “top”, “bottom”, “upper”, “lower”, “above”, “below”, etc., are used in their usual sense - i.e., relative to a gravitational direction or axis.

[0182]

[0172] It will be understood that, although the embodiments and / or implementations presented herein have been described with reference to specific features and structures, various modifications and combinations may be made without departing from the disclosure. For example, it is contemplated that in some implementations, the features described above may be used in different arrangements, or in other combinations. The specification and drawings are, accordingly, to be regarded simply as an illustration of the discussed implementations or embodiments and their principles as defined by the appended claims, and are contemplated to cover any and all modifications, variations, combinations or equivalents that fall within the scope of the present disclosure. It should also be understood that, although methods have been presented and expressed herein in terms of heating a building, similar methods may be used in cooling a building.

[0183]

[0173] Additional and / or alternative features, aspects and advantages of implementations of the present technology will become apparent from the following description, the accompanying drawings and the appended claims.

[0184] BRIEF DESCRIPTION OF THE DRAWINGS

[0185]

[0174] In the figures, the subject-matter of the disclosure is schematically shown, wherein identical or similarly acting elements are usually provided with the same reference signs.

[0186]

[0175] FIG. 1 is a block diagram of an example controller that could be used in some implementations of an energy management system.

[0187]

[0176] FIG. 2 is a block diagram of an example energy management system.

[0188]

[0177] FIG. 3 is a block diagram of an example energy management appliance of an energy management system.

[0189]

[0178] FIG. 4 is a block diagram of a portion of structure of a building.

[0190]

[0179] FIGS. 5 and 6 are graphs of relationships between variables.

[0180] FIGS. 7 and 8 are graphs of relationships between data points and variables.

[0191]

[0181] FIGS. 9, 10 and 1 1 are block diagrams of alternate methods of estimating base temperature.

[0192]

[0182] FIGS. 12, 13 and 14 are graphs of relationships between data points and variables.

[0193]

[0183] FIG. 15 is a block diagram of a method of estimating energy savings.

[0194]

[0184] FIGS. 16 and 17 are depictions of visual presentations on viewing screens.

[0195]

[0185] FIG. 18 is a block diagram of a method of guiding a user to increase energy savings.

[0196] DETAILED DESCRIPTION

[0197]

[0186] The examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the present technology and not to limit its scope to such specifically recited examples and conditions. It will be appreciated that those skilled in the art may devise various arrangements that, although not explicitly described or shown herein, nonetheless embody the principles of the present technology.

[0198]

[0187] Furthermore, as an aid to understanding, the following description may describe relatively simplified implementations of the present technology. As persons skilled in the art would understand, various implementations of the present technology may be of a greater complexity.

[0199]

[0188] In some cases, what are believed to be helpful examples of modifications to the present technology may also be set forth. This is done merely as an aid to understanding, and, again, not to define the scope or set forth the bounds of the present technology. These modifications are not an exhaustive list, and a person skilled in the art may make other modifications while nonetheless remaining within the scope of the present technology. Further, where no examples of modifications have been set forth, it should not be interpreted that no modifications are possible and / or that what is described is the sole manner of implementing that element of the present technology.

[0189] Moreover, all statements herein reciting principles, aspects, and implementations of the present technology, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof, whether they are currently known or developed in the future. Thus, for example, it will be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present technology.

[0200]

[0190] With these fundamentals in place, we will now consider some nonlimiting examples to illustrate various implementations of aspects of the present disclosure.

[0201]

[0191] Controller

[0202]

[0192] FIG. 1 depicts an example controller 100, which may be any type of computer system or embedded controller. It will be recognized that some or all the components of the controller 100 may be virtualized and / or cloud-based. As depicted, the controller 100 may include one or more processors 102, a memory 1 10, a storage interface 120, and a communication interface 140. These system components may be interconnected via a bus 150, which may include one or more internal and / or external buses (e.g. a PCI bus, universal serial bus, IEEE 1394 “Firewire” bus, SCSI bus, Serial-ATA bus, etc.), to which these various hardware components may be electronically coupled.

[0203]

[0193] The memory 1 10, which may be a random-access memory or any other type of memory, may contain data 1 12, an operating system 114, and a program 1 16. The data 1 12 may be any data that serves as input to or output from any program in the controller 100. The operating system 1 14 may be an operating system such as MICROSOFT WINDOWS, LINUX, FreeRTOS, or any other operating system suitable for use on a computer system or microcontroller. The program 1 16 may be any program or set of programs that include program instructions that may be executed by the processor to control actions taken by the controller 100. In particular, the program 1 16 may include program instructions that, when executed by the processor, cause the processor to carry out one or more of the methods described below.

[0204]

[0194] The storage interface 120 may be used to connect storage devices, such as the depicted storage device 125, to the controller 100. The storage device 125 may be a solid-state drive using an integrated circuit assembly to store data persistently. Alternatively, the storage device 125 may be a hard drive using any of a variety of types of magnetic storage media to store and retrieve digital data. As another alternative, the storage device 125 may be an optical drive, or a card reader that receives a removable non-volatile semiconductor memory card. As still another alternative, the storage interface 120 may provide a universal serial bus connection to which the storage device 125 may be hot-pluggable, and the storage device 125 may be a flash memory device (e.g., a USB thumb drive).

[0205]

[0195] In some implementations, the controller 100 may use well-known virtual memory addressing techniques that allow the programs of the controller 100 to behave as if they have access to a large, contiguous address space instead of access to multiple, smaller storage spaces, such as the memory 1 10 and the storage device 125. Therefore, while the data 1 12, the operating system 1 14, and the programs 116 are depicted as residing in the memory 110, those skilled in the art will recognize that these items may not all be wholly contained in the memory 1 10 at the same time.

[0206]

[0196] The one or more processors 102 may include one or more microprocessors and / or other integrated circuits able to execute program instructions stored in the memory 1 10. When the controller 100 starts up, the processor(s) 102 may initially execute program instructions of a boot routine and / or the program instructions that make up the operating system 1 14.

[0207]

[0197] The communication interface 140 may be used to communicatively connect the controller 100 to other controllers, computer systems, or still other devices (not shown) via a communication channel 160. The communication channel 160 may be a serial or parallel connection, a wired, wireless, mesh or cellular network, or any other type of communication channel or combination of channels. Data and / or program instructions may be sent to the controller 100 as signals via the communication channel. The communication interface 140 may include a combination of hardware and software that enables communications on the communication channel 160. The software in the communication interface 140 may include software that uses one or more communication protocols to communicate over the communication channel 160, including and not limited to, network protocols such as TCP / IP (Transmission Control Protocol / lnternet Protocol).

[0208]

[0198] It will be understood that the depicted controller 100 is merely an example, and that the technology disclosed herein may be used with a wide variety of other controllers or computer systems, or still other computing devices having different configurations.

[0209]

[0199] Energy Management System

[0210]

[0200] FIG. 2 depicts an example energy management system 200. The energy management system 200 is generally used to provide various energy management needs, such as heating, cooling, hot water, electricity, etc. at a building, such as a residential dwelling. However, it will be understood that the energy management system 200 may be used in other types of buildings, such as apartment buildings or other multi-dwelling buildings, office buildings, or any other type of building at which heating, cooling, hot water, and / or electricity systems are installed and / or controlled. In such buildings, each portion of the building, such as each residence, floor, or office suite, may be considered a separately controllable zone in the energy management system, in some implementations. It will also be appreciated that an energy management system, such as energy management system 200, may be used in still other applications for controlling the delivery of heating, cooling, hot water, and / or electricity. For example, such an energy management system may be used in a block system, which delivers energy from a central plant to numerous buildings or dwellings across one or more blocks, typically in an urban area.

[0211]

[0201] As depicted, the energy management system 200 may include one or more energy management appliances 202. As used herein, an energy management appliance may be a device that generates usable energy and / or manages, controls, and / or senses conditions relevant to usable energy in a form such as heat, cold, mechanical, and / or electrical energy. Thus, the energy management appliances 202 may include devices such as boilers, heat pumps, air conditioning units, fire places, ventilation devices, etc. The energy management appliances 202 may include energy producers, such as photovoltaic panels or wind turbines. The energy management appliances 202 may also include other devices that are part of energy management system 200, such as buffers or batteries, controllers and communication devices, sensors, thermostats, room units, controllable valves for radiators, and / or other devices or modules (including software modules), that generate, manage, control, and / or sense conditions relevant to the operation of energy management system 200.

[0212]

[0202] The energy management appliances 202 may include one or more boilers 204. Each boiler 204 may be powered using fossil fuels, such as natural gas, propane or oil, or may be powered using other fuel sources, such as hydrogen, or by some combination of such fuel sources. The operation of boilers is generally well known in the art. The boilers 204 provide heat, usually transferred to a fluid carrier medium, which is typically water. Hot water or steam supplied by a boiler such as boilers 204 may be used, for example, for domestic hot water supply and / or heating applications.

[0213]

[0203] The energy management appliances 202 may include one or more electric heat pumps 206. The operation of electric heat pumps is well known in the art. Heat pumps 206 providing heating and / or cooling by moving heat between environments. While often more energy efficient than boilers, the efficiency of heat pumps typically depends on outdoor temperatures, and operate over a limited temperature range.

[0214]

[0204] The energy management appliances 202 may include renewable energy generation devices, such as photovoltaic panels 208 and / or a wind turbine 210. Such renewable energy generation devices typically generate electricity. However, solar thermal panels use sunlight to heat water or other fluids directly.

[0215]

[0205] The energy management appliances 202 may include buffer storage 212. Buffer storage temporarily stores energy of the various types typically managed in an energy management system 200. An example of buffer storage may be an insulated hot water tank used to store hot water, or a chemical energy storage system (e.g., a battery) used to store electrical energies. For example, in some implementations, one or more organic redux flow batteries, lead-acid batteries, or lithium ferro phosphate batteries may be used to store electrical energy. Other less common buffer storage systems, such as ice storage or mechanical energy storage systems, are also known in the art.

[0216]

[0206] The energy management appliances 202 may also include other devices such as room units 214, sensors 216, a smart meter 218, and other similar devices. It will be understood that in some implementations, these devices, which are not directly involved in the generation of usable energy in the form of heat, cold, and / or electricity, might not typically be considered to be energy management appliances, but may nonetheless be part of the depicted example energy management system 200.

[0217]

[0207] Although many types of energy management appliances are discussed above, it will be understood that this is not an exhaustive list. For example, energy management systems may include energy management appliances fuelled by wood pellets or still other fuel sources. In some energy management systems, combined heat and power (CHP) energy management appliances may be used. More broadly, and as will be recognized by those skilled in the art, there is a wide variety of types of energy management appliance that may be used in an energy management system such as the depicted example energy management system 200.

[0218]

[0208] In accordance with the technology disclosed herein, the depicted example energy management system 200 may also include a system manager 220 that provides centralized control over activities occurring within the energy management system 200. The system manager 220 may be implemented using a controller, such as the controller 100 depicted and described in reference to FIG. 1 , or any other suitable controller, microcontroller, or computer system. As depicted, the system manager 220 may be implemented as a physically separate and distinct unit, and may be located either within or external to the energy management system 200.

[0219]

[0209] Alternatively, the system manager 220 may be implemented as a software module that operates on any controller or computing device connected to or incorporated into an energy management appliance of the energy management system 200. As still another alternative, the system manager 220 may be implemented as a software module that operates in a virtual environment provided by a remotely located server (e.g., cloud-based), where such a virtual environment may be one of numerous such virtual environments provided within such a server such that multiple instances of the system manager 220 may be supported for multiple corresponding buildings.

[0210] As will be readily recognized by those skilled in the art, different implementations of the system manager 220 may have widely differing capabilities. By way of example, a physically separate and distinct form of the system manager 220 may incorporate considerable processing and / or data storage capabilities, thereby making such an implementation of the system manager 220 capable of performing relatively complex operations with large sets of data, including generating and maintaining a model of the entirety of the energy management system 200 and / or the building at which the energy management system 200 is installed. This may enable such an implementation of the system manager 220 to generate predictions of upcoming demands that will be made of the energy management system 200, and thus, may enable the system manager 220 to preemptively use components of the energy management system 200 to prepare for such demands. In contrast, and also by way of example, a form of the system manager 220 that is incorporated into a controller of an energy management appliance 202 (e.g., as a software module) may have access to relatively minimal processing and / or data storage resources, thereby limiting the complexity of the operations that it is able to perform.

[0220]

[0211] Because different energy management appliances 202 have varying (and sometimes conflicting) capabilities, characteristics, and requirements, the system manager 220 attempts to use information stored within one or more of the energy management appliances 202 to coordinate activities among the energy management appliances 202 within the energy management system 200 to meet user demands. Generally, the system manager 220 receives requests from users through, for example, room units 214. The system manager 220 attempts to fulfill these requests by sending commands to one or more of the energy management appliances 202. Information from the sensors 216 and / or the energy management appliances 202 concerning current conditions of operation are also received by system manager 220, and are used to coordinate activities within the energy management system 200 to meet user requests. Once the system manager 220 has determined how a request may be at least partially fulfilled, the system manager 220 sends such commands as on / off commands and / or setpoints to the one or more energy management appliances 202 that will be used to at least partially fulfill the request. It should be noted that, in some instances, a received request may be a forecasted demand. In such cases, fulfilling the request may be used to prepare for handling the forecasted demand. Such a forecasted demand may be generated using a model or simulation, either on an external device, or within the system manager 220, itself.

[0221]

[0212] System manager 220 may communicate with the energy management appliances 202, as well as other devices connected to energy management system 200, via a communication channel 230. The communications channel 230 may employ any of a variety of optical, electrically wired, and / or wireless communications technologies. For example, system manager 220 and energy management appliances 202 may communicate wirelessly using a wireless communication protocol, such as WIFI, Bluetooth, or Zigbee. It will further be understood that although the communication channel 230 is depicted as being based on a network having a bus topology, this is merely for ease of illustration, and any other suitable network topology may be used. For example, a mesh network, star network, ring network, tree network or hybrid network topology may be used. Though not specifically depicted, it should be understood that the communication channel 230 may include such well-known components as network hubs, repeaters and / or media converters.

[0222]

[0213] In some implementations, the system manager 220 may include an external communication module (not shown) that may allow it to communicate over the Internet or other wide-area network (not shown), and with sources of information and / or with controllers or other devices outside of the energy management system 200. This may permit the system manager 220 to receive such information as weather forecasts, current weather conditions (e.g., weather conditions detected by network-connected local weather stations), current energy pricing, current energy usage restrictions imposed by a government or grid operator, and / or other information on external conditions and limitations that may be relevant to operation of the energy management system 200. The system manager 220 may also receive commands and / or user preferences or settings via the Internet or other wide-area network. For example, a user may use an app running on a mobile phone, tablet, computer system, or other external device to issue commands to the system manager 220. In some implementations, devices, such as room units, thermostats, and / or sensors may not be considered to be energy management appliances or to be a part of the energy management system 200. In these implementations, such devices may communicate with the system manager 220 as external sources. Additionally, in some implementations, the system manager 220 may be in communication with external services, such as predictive maintenance services, cloud-based and / or Al-based energy control systems, local or regional energy usage control systems, and the like.

[0223]

[0214] Energy Management Appliance

[0224]

[0215] FIG. 3 depicts an example energy management appliance of the example energy management system 200 of FIG. 2. More specifically, the boiler 302 depicted in FIG. 3 may be one of the boilers 204 of the energy management system 200 of FIG. 2.

[0225]

[0216] As depicted, the boiler 302 may include a tank 304, a heat exchanger 306, a burner 308, a supply pipe 310, a return pipe 312, a controller 314, a temperature sensor 316 and / or a communication module 318. While these particular example components are specifically depicted, it will be understood that numerous other components that are not specifically depicted may make up a typical boiler, such as temperature and pressure relief valves, an expansion tank, a circulator pump, and the like. Boilers for domestic hot water supply are well- known in the art.

[0226]

[0217] The controller 314 may be connected to one or more sensors, such as the temperature sensor 316, which may be used to measure the temperature of the water in the boiler 302. In a typical boiler, there may be any of a variety of other sensors, such as pressure sensors, flow sensors, oxygen and / or carbon monoxide sensors, flame sensors, water level sensors, and the like. The controller 314 may also be connected to the communication module 318, which may facilitate communication with a system manager of an energy management system that may include the boiler 302 (e.g., the system manager 220 of the energy management system 200 of FIG. 2). The communication module 318 may be integrated with controller 314 or may be a separate module that is connected to controller 314 through an interface.

[0227]

[0218] The controller 314 may regulate the operation of boiler 302 based on input from sensors, and / or based on settings and / or commands received from a system manager. The controller 314 may use a combination of software algorithms, control logic, and / or feedback loops to control the operation of the components of boiler 302 by, for example, adjusting a fuel flow rate, changing pump speeds, and opening / closing valves to maintain a desired temperature in the boiler 302, and / or to control other functions and / or operating parameters of boiler 302. The controller 314 may also use data collected by sensors to monitor conditions within boiler 302, to detect deviations from desired setpoints, and / or to determine whether there have been any malfunctions or other conditions that could affect the operation, safety, and / or reliability of the boiler 302.

[0228]

[0219] The controller 314 may receive commands from, and send information to, a system manager of an energy management system. The information sent to the system manager may include, for example, information collected by sensors of the boiler 302, information on conditions within the boiler 302, and / or information concerning alarms and / or warnings regarding malfunctions occurring within the boiler 302. The controller 314 may receive commands from the system manager to, for example, adjust setpoints, provide specified information, start or stop the boiler 302, start or stop subsystems of the boiler 302, and the like.

[0229]

[0220] It will be understood that the boiler 302 is merely an illustrative example of an energy management appliance, and that any type of energy management appliance that is part of the energy management system 200 of FIG. 2 may include a controller, similar to the controller 314, and a communication module similar to the communication module 318. The variety of components that may be incorporated into such energy management appliances will depend on the type of energy management appliance. It will further be understood that in some implementations, multiple energy management appliances could be controlled by a single controller, similar to the controller 314 of the boiler 302. Alternatively, multiple energy management appliances, each with its own controller, may communicate with the system manager, using a single shared communication module.

[0230]

[0221] It should further be noted that controllers for energy management appliances, such as the controller 314 of the boiler 302, may be referred to herein as closed loop controllers. Generally, a closed loop controller is a type of control system that uses feedback from one or more sensors to adjust the output of a control signal to maintain a desired setpoints. While the controllers for many energy management appliances fit this description, the term closed loop controller may be used herein for any controller that is incorporated into an energy management appliance, regardless of whether such a built-in controller makes such use of such sensor feedback.

[0231]

[0222] Building Characteristics

[0232]

[0223] FIG. 4 depicts a cross-section of a portion of the structure of an example building 400 at which the example energy management system 200 may be installed. More specifically, a cross-sectional view is presented of a portion of the exterior structure of the example building 400 that includes portions of a basement and a ground floor thereof.

[0233]

[0224] As depicted, the example building 400 employs a wood frame construction in which at least a substantial portion of the structure above ground is built using dimensional lumber. The depicted portion of the structure at the ground floor includes portions of an exterior wood framed wall 410 and a wood framed floor 420 that are supported above a basement. The depicted portion of the structure of the basement includes portions of an exterior wall 430 and a floor 440 that may both be formed from poured concrete. As also depicted, at least a portion of the exterior wall 430 of the basement is in contact primarily with a portion of the ground 490 on which the building 400 is constructed. In contrast, the exterior wall 410 of the ground floor is exposed to the outdoor weather, but is not in contact with the ground 490.

[0234]

[0225] The depicted portion of the exterior wall 410 of the ground floor may include an interior surface material 412 (e.g., plaster, drywall, cement fiber board, wood, etc.), wall studs 414 (e.g., vertically extending pieces of dimensional lumber), exterior sheathing 416 (e.g., sheets of plywood or oriented strand board), and an exterior surface material 418 (e.g., wood, brick, stucco, metal sheets, etc.). The depicted portion of the floor 420 of the ground floor may include at least a subfloor material 422 (e.g., sheets of plywood or oriented strand board), and a combination of floor joists 424, a rim joist 426 and a sill plate 428 (e.g., horizontally extending pieces of dimensional lumber).

[0235]

[0226] The depicted portion of the exterior wall 430 of the basement may be formed entirely of poured concrete, and may be shaped to include a widened portion at the base that defines a footer. The depicted portion of the floor 440 of the basement may be formed entirety of a relatively thick sheet of poured concrete that extends no further than to the interior surfaces of the exterior walls 430 of the basement.

[0236]

[0227] Although not specifically depicted, voids in at least the exterior wood framed walls of the building 400, such as the depicted portion of the exterior wall 410, may be filled largely with insulation material (e.g., fiberglass, cellulose, mineral wool, recycled denim, etc.) to improve the ability of those exterior walls to reduce the passage of heat energy therethrough. More specifically, when the outdoor temperature is higher than the setpoint temperature that a user has specified for the interior of the building 400, such insulated exterior walls serve to resist the penetration of heat energy into the interior of the building 400 from the outside. Correspondingly, when the outdoor temperature is lower than the setpoint temperature that a user has specified for the interior of the building 400, such insulated exterior walls serve to resist the escape of heat energy from the interior of the building 400 to the outside.

[0237]

[0228] Beyond better insulation of the exterior walls 410 that are above the level of the earth 490, various steps may be taken with regard to windows (not specifically depicted) of the building 400. Among such steps may be the use of windows employing multiple layers of glass that are separated by a vacuum or particular gases, instead of windows employing just a single layer of glass. In this way, the windows also become insulated to a greater degree, thereby resisting the penetration of heat energy therethrough in ways similar to what is achieved by insulating the exterior walls 410.

[0238]

[0229] Beyond the characteristics of components of the building, itself, there may be characteristics of the contents of the building that may alter the effectiveness and / or efficiency with which the interior may be heated. By way of example, the presence of people and / or other creatures within a building may result in the generation of more heat therein without the expenditure of energy by an energy management system to heat the interior. As will be familiar to those skilled in the art, the bodies of human beings and many other creatures employ various biological processes to maintain particular body temperatures as part of being alive, and such body heat is released into the interior of a building by each human being or other creature within it. Also by way of example, a wide variety of devices and / or types of machinery that may be present within a building (and that perform functions unassociated with an energy management system), and may release various amounts of heat into the interior of the building as part of their normal operation. Further, a number of such other devices and / or types of machinery may be carried by persons who routinely enter and / or exit the building (e.g., portable computers, smart phones, portable beverage coolers, etc.).

[0239]

[0230] Also, regarding heating of the interior of the building 400 (e.g., the interior of the depicted ground floor), various steps may be taken to decrease the amount of energy that may need to be consumed by improving the ability of the building 400 to make use of the radiant heat energy provided by the sun. As is widely known, portions of the exterior wall 410 where the exterior surface material 418 faces towards Earth's equator may receive considerable exposure to sunlight during the day such that the radiant heat energy from the sun may be absorbed and used to provide some of the heat energy that may be relied upon to heat the interior of the building. This may be the case even when the insulation within the exterior walls 410 renders the exterior walls 410 relatively highly resistant to the penetration of heat energy from the outdoors.

[0240]

[0231] Various steps may be taken to improve such absorption and / or penetration of heat energy from sunlight through the exterior walls 410, including preventing the blockage of sunlight to the exterior surface material 418 thereof by trees, etc., and / or painting such exterior surface material 418 with a darker color that is more absorptive of the radiant heat energy of sunlight, and less reflective thereof. Another step may be to arrange the placement and / or relative sizes of the windows of the building 400 so that a greater proportion of the total window surface area is incorporated into portions of the exterior walls 410 that face towards Earth's equator. In this way, during daylight hours, at least some of the sunlight that reaches the building 400 is able to penetrate through such windows and the heat energy thereof is able to assist in heating the interior.

[0241]

[0232] In contrast to the wood-framed exterior wall 410 of the ground floor, the poured concrete exterior wall 430 of the basement may be more prone to allowing heat energy to pass therethrough. This is often not deemed to be an issue of concern as basements are often not a portion of a building in which people dwell, and so there is often little or no concern about the temperature within the interior of a basement. Also, the fact that at least the majority of the exterior walls 430 and floor 440 of a basement are in contact with the ground 490, and the fact that the temperature of the earth 490 remains relatively constant throughout the year often serves to constrain the temperature within the basement to a relatively narrow range that is often deemed to be comfortable enough for most of the purposes for which a basement is typically used (e.g., to house machinery used to provide various utility services within a building). Thus, it is not uncommon for little or no effort to be made in consuming energy to actively control the temperature within the basement.

[0242]

[0233] Theory

[0243]

[0234] FIGS. 5, 6, 7 and 8, taken together, provide graphs 500, 600, 700 and 800, respectively, depicting aspects of various relationships among values for temperatures and amounts of energy consumed by one or more energy management appliances (e.g., one or more of the energy management appliances 202 of the energy management system 200) to heat a building (e.g., the building 400) under a variety of weather conditions. These same figures, taken together, also graphically depict aspects of relationships among various values that are derived as part of a system manager (e.g., the system manager 220 of the energy management system 200) generating and maintaining a model of such energy consumption that is normalized against weather influences and / or generating determinations of energy savings for heating energy consumed to heat a building.

[0244]

[0235] As will be familiar to those skilled in the art, where it comes to heating a building, the base temperature Tbase is the outdoor temperature below which energy needs to be consumed to heat the building to maintain the building at a particular setpoint temperature selected by a user for the interior of the building. If the outdoor temperature is above the base temperature Tbase, then no such energy needs to be consumed to heat the building. This is depicted in FIG. 5 where, for a particular setpoint temperature that is assumed to be unchanging, the base temperature Tbase is depicted as being the outdoor temperature at which a flat line 502 of a zero level of heating energy consumption, and a sloped line 504 of a non-zero linearly increasing level of heating energy consumption meet.

[0236] It is this same relationship among the base temperature Tbase, outdoor temperatures and energy consumed to heat a building that forms the basis for heating degree days (HDDs) as described by the following equation:

[0245]

[0237] In this equation, Toutdoor, d is the average outdoor temperature for day d. This same relationship is depicted in FIG. 6.

[0246]

[0238] Referring to both FIGS. 5 and 6, in theory, with the values for the base temperature Tbase and the reference temperature Tret known, and with the slope of the sloped line 504 of non-zero levels of heating energy consumption also known, it should be possible to derive a value for the heating energy consumption to heat the building that is normalized against the influences of the weather (i.e., ENEC). It should also be noted that such a derivation of the normalized heat energy consumption ENEC presumes that both the setpoint temperature and thermal characteristics of the building (e.g., level of insulation of the walls, windows, etc.) are unchanging.

[0247]

[0239] More specifically, the normalized heating energy consumption ENEC for heating a building for the current day, with the current day's average outdoor temperature for the entirety of the current day, and with the current base temperature (unchanging as a result of an unchanging setpoint temperature and unchanging thermal characteristics of the building) may be calculated as:

[0248] EC(Tbase, current, Toutdoor, current)

[0249] ENEC = HDD(Tbase, current, Tref) ' -

[0250] HDD(Tbase, current, Toutdoor, current)

[0251]

[0240] In this equation, the variable EC (Tbase, current, Toutdoor, current) is the total heating energy consumption for the current day, with the current base temperature Tbase, and with the average of the entire set of outdoor temperatures measured throughout the current day Toutdoor .current-

[0241] However, as discussed earlier, multiple factors beyond outdoor temperatures exert influence over when heating a building is or is not required to maintain the setpoint temperature within. By way of example, and as previously discussed, the exposure of portions of a building to sunlight during the day can influence whether energy must be consumed to heat the building during at least a portion of the day, and / or at what outdoor temperature such energy consumption begins to be required. On a clear day, a building may be exposed to more sunlight, which may entirely obviate the need to consume such energy during a portion of that day, or at least lower Tbase such that the outdoor temperature must drop lower before heating-related energy consumption is required. In contrast, during a cloudy day (or at night), a lower amount or lack of sunlight may raise the base temperature Tbase such that the outdoor temperature need not drop as low before heating-related energy consumption is required.

[0252]

[0242] As a result, and as depicted in FIG. 7, such data as average daily temperatures and daily total heating energy consumption may be subject to weather influences. In particular, the set of data points 702 for days during which the total heating energy consumption is zero may partially overlap, by multiple degrees of temperature, with the set of data points 704 for days during which the total heating energy consumption is a non-zero amount.

[0253]

[0243] As depicted in FIG. 8, this overlap may complicate efforts to derive the base temperature Tbase, and the slope of the sloped line 504 of non-zero levels of heating energy consumption. Accordingly, this complicates efforts to accurately derive the normalized heating energy consumption ENEC, and in turn, this complicates efforts to generate and maintain a model of energy consumption for heating a building.

[0254]

[0244] However, as a result of the efforts of the inventors in the present application, it has been determined that the normalized heating energy consumption ENEC is able to be determined with a degree of accuracy that enables a relatively accurate model of energy consumption for heating a building to be generated and maintained. Also, such an estimation has been found to be possible through the use of any one of three different approaches that are based on gathered data that includes average daily temperatures and total daily consumption of energy for heating a building that spans a period of numerous days, such as a period of multiple weeks or multiple months.

[0255]

[0245] Methods

[0256]

[0246] Each of FIGS. 9, 10 and 1 1 depicts a block diagram of one of the three methods 900, 1000 and 1 100, respectively, for estimating the normalized heating energy consumption ENEC. Each of these three methods is able to be performed with relatively limited processing and / or data storage resources such that a system manager with relatively limited processing and / or data storage resources may be used. Thus, it may be that each of these three methods is able to be performed within a loop controller of an energy management appliance (e.g., the controller 314 of the boiler 302), or within a network communications device (e.g., a network hub, switch, modem, edge device, etc.). In this way, the need to provide a physically separate, more sophisticated, and more expensive computer or other device serving as a system manager may be averted.

[0257]

[0247] Each of these three methods includes deriving the base temperature Tbase. As will be readily recognized by those skilled in the art, when estimating levels of energy consumed in heating a building (as opposed to cooling a building), the reference temperature Tret should be chosen to be lower than the base temperature Tbase, so as to be within a range of temperatures at which the consumption of heating energy is required. However, the reference temperature Tref should not be chosen to be too low. As a result of the efforts of the inventors in the present application, it has been determined that consistently good results in estimating the normalized heating energy consumption ENEC are achieved by setting the reference temperature Tref to be at the median temperature of the daily average temperatures for the set of daily average outdoor temperatures that are associated with the set of data points 704 (see FIG. 4) for days on which heating energy was consumed for heating. Alternatively, good results in estimating the normalized heating energy consumption ENEC have been found to be achieved by setting the reference temperature Tref to either of the experimentally derived values of 3°C or 5°C.

[0258]

[0248] Turning to FIG. 9, the first of the three methods may employ a calculated balance of levels of heat energy. The reference temperature Tref may be derived at block 902 in the manner just described. For a relatively simple building, such as an individual house, it is assumed that a balance of power levels exists as described by the following initial equation:

[0259] ^boiler T ^internal UA (^indoor ^outdoor)

[0260]

[0249] Pboiier is the total daily power introduced into the building, in particular in the house or a part of the building, in particular an apartment, by a boiler (or other form of energy management appliance that is used to heat the house), Pinternal is the total daily power level added to the interior of the building, in particular house, by all other devices within the building, in particular house, in the form of heat, U is the overall heat transfer coefficient between the interior of the building, in particular house, and the environment outside of the building, in particular house, A is the surface area of the building, in particular house, Tindoor is the daily average indoor temperature within the building, in particular house, and Toutdoor is the daily average temperature outside of the building, in particular house.

[0261]

[0250] In using the above initial equation describing a balance of power levels, it is assumed that the gathered data is stored as data points separated by a daily interval of time, and not a shorter interval of time (e.g., hourly). It should be noted that such storage of the gathered data with a daily interval of time (e.g., daily averages and daily totals), as opposed to an hourly interval of time, serves both to reduce storage requirements, and to filter out or smoothen out dynamic heating behavior that may occur across a day long period of time.

[0262]

[0251] Since, as previously discussed, the base temperature Tbase is the outdoor temperature below which heating is required to keep the indoor temperature Tindoor at the setpoint temperature Tsetpoint, the above initial equation enables the base temperature Tbase to be derived at block 904 as follows:

[0263] P

[0264] „ > „ ' internal

[0265] ' base ' setpoint

[0266]

[0252] Since it is assumed that the setpoint temperature Tsetpoint is a steady state temperature, the further assumption may be made that Tindoor = Tsetpoint. Thus, the above initial equation may be rewritten to derive Pboiier as a function of Toutdoor:

[0267]

[0253] The following corresponding equation describes the daily aggregated balance of heat energy:

[0268]

[0254] At represents a single day of time. The multiplication product At ■ UA represents the slope of the sloped line 504 (see FIG. 5) for the relationship between the outdoor temperature Toutdoor and total amount of heating energy consumed for the set of data points 704 (see FIG. 7) associated with days on which heating energy was consumed. Thus, at block 906, that slope may be derived as At ■ UA.

[0269]

[0255] From the above equation for calculating Eboiler, the following equation is able to be derived for estimating ENEC:

[0270]

[0256] At block 908, with Tret, Tbase and the slope having been derived, ENEC may be determined using the above equation.

[0271]

[0257] It should again be noted that the above approach is at least partially based on an assumption of the gathered data being stored with a daily interval of time such that the interval of time between temporally adjacent entries is a single day. However, if the gathered data was both collected and stored with a shorter time interval than a daily time interval, then the opportunity of such higher frequency data may be used to improve the accuracy of the estimation of the normalized heating energy consumption ENEC. TO do so, the above initial equation describing a balance of power levels may be modified to add another term that enables better fitting to account for the smaller interval of time At:

[0272]

[0258] Turning to FIG. 10, the second of the three methods may employ a fitted line of increasing heat energy. In a manner similar to the method of FIG. 9, at 1002, the reference temperature Tref may be derived at block 1002.

[0273]

[0259] At block 1004, a set of data points 704 (see FIG. 7) in the gathered data for days on which there was a non-zero level of heating energy consumed to heat a building may be separated from another set of data points 702 (again, see FIG. 7) in the gathered data for days on which there was a zero level of heating energy consumed for the same building.

[0274]

[0260] At block 1006, a linear least squares fitting may be performed to derive a sloping line through the set of data points for the days with a non-zero level of total consumption of heating energy. In this way, a direct relationship may be derived between the heating energy consumed by a boiler Eboiier (or other energy management appliance used for heating) and the outdoor temperature Toutdoor. Also in this way, the slope of that line may be derived, along with the base temperature Tbase for the point along that line at which there is a zero amount of total energy consumed to heat the building.

[0275]

[0261] At block 1008, with Tret, Tbase and the slope having been derived, ENEC may be determined by evaluating the linear fit at the reference temperature Tref.

[0276]

[0262] Turning to FIG. 1 1 , the third of the three methods may employ probability density functions. Again, in a manner similar to the methods of FIGS.

[0277] 9 and 10, at 1 102, the reference temperature Tref may be derived at block 1 102.

[0278]

[0263] At block 1104, a set of data points 704 (see FIG. 7) in the gathered data for days on which there was a non-zero level of heating energy consumed to heat a building may be separated from another set of data points 702 in the gathered data for days on which there was a zero level of heating energy consumed for the same building. Again, as was depicted in FIG. 7, these two sets 702 and 704 partially overlap in their outdoor temperatures Toutdoor. Efforts by the inventors in the present application have revealed that the base temperature Tbase is usually a temperature within this range of overlapping temperatures.

[0264] At block 1 106, separate probability density functions are generated for the two sets of data points 702 and 704. FIG. 12 depicts graphical plots for these two separate probability density functions 1202 and 1204 generated from the two sets of data points 702 and 704, respectively.

[0279]

[0265] At block 1 108, a rolling filter is separately applied to each of the two probability density functions 1202 and 1204. FIG. 13 depicts graphical plots for the resulting two smoothened probability density functions 1302 and 1304, respectively, and the resulting overlapping region 1306.

[0280]

[0266] At block 1 1 10, the overlapping region 1306 may be interpolated to increase its temperature resolution.

[0281]

[0267] At block 1 112, following such interpolation, the overlapping region 1306 may be resampled to generate a pool of samples at the increased resolution.

[0282]

[0268] At block 11 14, the base temperature Tbase is derived as the median of the pool of samples. Efforts by the inventors in the present application have revealed that such a median relatively easily provides a good approximation of the base temperature Tbase. Such efforts have also revealed that the corresponding interquartile range of temperatures usually reliably provide the uncertainty for this base temperature. FIG. 14 graphically depicts this uncertainty as a region of uncertainty 1406.

[0283]

[0269] At block 1 1 16, with Tref, and Tbase having been derived, ENEC may be determined using the aforementioned equation:

[0284]

[0270] Again, in this equation, the variable EC(Tbase, current, Toutdoor, current) is the total heating energy consumption for the current day, with the current base temperature Tbase, and with the average of the entire set of outdoor temperatures measured throughout the current day Toutdoor, current.

[0285]

[0271] Referring back to FIGS. 9-1 1 , the third method 1 100 of FIG. 1 1 may enable the calculation of the base temperature Tbase with relatively high accuracy. However, supporting its use of probability density functions in the third method 1 100 requires a relatively large set of data points spanning numerous days that must include a combination of days during which heating is required and days during which heating is not required. In contrast, the second method 1000 of FIG. 10 may require a smaller set of data points spanning fewer days, and its smaller data set may not be required to include a combination of days during which heating is required and days during which heating is not required. However, its use of line fitting may result in less accuracy than the third method 1 100. The first method 900 of FIG. 9 may offer a combination of some of the advantages of both the third method 1 100 and second method 1000. More specifically, its use of a calculated balance of levels of heat energy may enable the use of a smaller set of data points spanning fewer days, and its smaller set of data points may not be required to include a combination of days during which heating is required and days during which heating is not required. Additionally, its accuracy may be greater than the second method 1000, though not greater than the first method 900.

[0286]

[0272] In some implementations, advantage may be taken of these various characteristics of these differing methods by employing different ones of these differing methods at different times of the year. By way of example, it may be that an implementation makes use of the greater accuracy of the third method 1 100 by employing the third method 1 100 during the Spring and / or Fall seasons during which there are combinations of days during which heating is required and days during which heating is not required. Such an implementation may then make use of the lack of need for such a combination of days by employing the first method 900 and / or the second method 1000 during the Summer and / or Winter seasons.

[0287]

[0273] FIG. 15 depicts a block diagram of a method 1500 for estimating energy savings following the performance of any of the above three methods set forth in reference to FIGS. 9, 10 and 1 1 for estimating the normalized heating energy consumption ENEC.

[0288]

[0274] At block 1502, for the particular day d for which a prediction of energy savings is to be made, the base temperature Tbase may be used to calculate the heating degree days HDD using the aforementioned equation:

[0275] Again, and in accordance with the relationships depicted in FIGS. 5 and 6, it is assumed that, when the outdoor temperature is below the base temperature Tbase, the heating energy consumption EC scales linearly with the outdoor temperature (see FIG. 6). Thus, the heating energy consumption EC should also scale linearly with the heating degree days HDD. It is assumed that the slope of this linear scaling relationship should be constant, which may be expressed as:

[0289] EC

[0290] HDD =C°nStant

[0291]

[0276] It is also assumed that changes made to the setpoint temperature Tsetpoint should directly change the base temperature.

[0292]

[0277] At block 1504, the assumption about the linearity of the between heating energy consumption EC and HDD, and the assumption about the relationship between the base temperature Tbase and the setpoint temperature Tsetpoint can be combined to compute the predicted energy savings for a reduction in the heating energy consumption EC as follows:

[0293] Energy Savmgs

[0294]

[0278] ATsetpoint represents the change in the setpoint temperature from the setpoint temperature Tsetpoint that may have been used in the aforementioned analyses to derive the base temperature Tbase.

[0295]

[0279] As previously discussed, this energy savings determination may be used in further analyses to derive recommendations in considering changes to using more environmentally friendly technologies for heating, and / or in considering the addition of insulation and / or other changes to make to a building.

[0296]

[0280] Further, if the granularity of the data points is increased from daily temperatures and measures of heating energy consumption to hourly, then more sophisticated predictive analyses may be performed to deal with energy-related variables that are otherwise unseen with a daily granularity, such as variations in the production of electricity that occur within each day. Such an hourly granularity (or other granularity finer than whole days) also makes visible such aspects of energy as peak demand hours for electricity received from a grid.

[0297]

[0281] FIG. 16 depicts an example of a visual presentation 1600 of an indication 1602 of a degree of efficiency of heating energy consumption for a specified month (e.g., the month of December of 2023) to heat a particular building, in particular house or other structure, in particular a part of the building, in particular an apartment, (e.g., the building 400 of FIG. 4). FIG. 17 depicts an example of a visual presentation 1700 of a recommendation 1702 to change a setpoint temperature Tsetpoint used in heating a particular building, in particular house or other structure, in particular a part of the building, in particular an apartment. Again, such visualizations may be presented on a viewing screen of a controller of an energy management system (e.g., the controller 100 of FIG. 1 ) or a viewing screen of a remote portable device (e.g., a tablet computer or smart phone).

[0298]

[0282] FIG. 18 depicts a block diagram of a method 1800 for guiding a user of an energy management system through improving a degree of efficiency of heating energy consumption to heat a particular building, in particular house or other structure, in particular a part of the building, in particular an apartment.

[0299]

[0283] At block 1802, energy savings may be determined for at least the most recent month for which the current setpoint temperature of an energy management system has been continuously used, and for which a set of data points are available. It should be noted that, depending on the current seasons and / or the quantity of months that such a set of data points spans, different ones of the earlier-described methods may be used. More specifically, where such a set of data points is available for such a continuously used current setpoint temperature, and where the multiple months covered by such a set of data points and setpoint temperature extend through a season of Spring or Fall where there are days during which heating is required and days during which heating is not required, then the third method 1 100 of FIG. 1 1 may be used. However, where either the available set of data points or the continuous use of the current setpoint temperature does not extend backward in time through multiple months, or where such multiple months do not include a combination of days in which heating was required and days in which heating was not required, then either the second method 1000 of FIG. 10 or the first method 900 of FIG. 9 may be used.

[0300]

[0284] At block 1804, an indication of such energy savings, such as an indication of a degree of efficiency of heating energy consumption, may be visually presented on a viewing screen of a controller of the energy management system or of a remote portable device (e.g., the visual presentation 1600 of the indication 1602).

[0301]

[0285] At block 1806, a determination may be made as to whether or not to visually present a suggestion to a user of the energy management system to change the setpoint temperature to increase such efficiency, and thereby increase such energy savings. It should be noted that, the current level of energy efficiency or energy savings may be compared to a predetermined minimum threshold level thereof as part of making such a determination.

[0302]

[0286] At block 1808, input indicative of a change to the setpoint temperature may be awaited. It should be noted that such awaiting of such input may take place regardless of whether the suggestion to make such a change is visually presented, or not.

[0303]

[0287] At block 1810, presuming such input to change the setpoint temperature is received, then: 1 ) the setpoint temperature may be so changed, 2) a new set of data points for a new month may be collected, and / or 3) at least a new indication of energy savings for at least the new month may be determined based on at least the new set of data points and visually presented. It should be noted that, as a result of having a new setpoint temperature and just the new month's set of data points, it may be that the new determination may need to be generated using either the first method 900 or the second method 1000.

[0304]

[0288] It will be understood that, although the embodiments and / or implementations presented herein have been described with reference to specific features and structures, various modifications and combinations may be made without departing from the disclosure. For example, it is contemplated that in some implementations, the features described above may be used in different arrangements, or in other combinations. The specification and drawings are, accordingly, to be regarded simply as an illustration of the discussed implementations or embodiments and their principles as defined by the appended claims, and are contemplated to cover any and all modifications, variations, combinations or equivalents that fall within the scope of the present disclosure. It should also be understood that, although methods have been presented and expressed herein in terms of heating a building, similar methods may be used in cooling a building.

[0305] REFERENCE SIGNS

[0306] 100 controller

[0307] 102 processors

[0308] 110 memory

[0309] 112 data

[0310] 114 operating system

[0311] 116 program

[0312] 120 storage interface

[0313] 125 storage device

[0314] 140 communication interface

[0315] 150 bus

[0316] 160 communication channel

[0317] 200 energy management system

[0318] 202 energy management appliances

[0319] 204 boiler

[0320] 206 heat pump

[0321] 208 photovoltaic panel

[0322] 210 wind turbine

[0323] 212 buffer storage

[0324] 214 room units

[0325] 216 sensors

[0326] 218 smart meter

[0327] 220 system manager

[0328] 230 communication channel

[0329] 302 boiler

[0330] 304 tank

[0331] 306 heat exchanger

[0332] 308 burner

[0333] 310 supply pipe

[0334] 312 return pipe

[0335] 314 controller

[0336] 316 sensor 318 communication module

[0337] 400 building

[0338] 410 exterior wall

[0339] 412 interior surface material

[0340] 414 wall studs

[0341] 416 exterior sheathing

[0342] 418 exterior surface material

[0343] 420 floor

[0344] 422 subfloor material

[0345] 424 floor joists

[0346] 430 exterior wall

[0347] 440 floor

[0348] 500 graph

[0349] 502 flat line

[0350] 504 sloped line

[0351] 600 graph

[0352] 700 graph

[0353] 702 set of points for days of zero heating energy consumption

[0354] 704 set of points for days of non-zero heating energy consumption

[0355] 800 graph

[0356] 900 method

[0357] 1000 method

[0358] 1100 method

[0359] 1200 graph

[0360] 1202 probability density function

[0361] 1204 probability density function

[0362] 1300 graph

[0363] 1302 smoothened probability density function

[0364] 1304 smoothened probability density function

[0365] 1306 overlapping region

[0366] 1400 graph

[0367] 1406 region of uncertainty

[0368] 1500 method 1600 visual presentation

[0369] 1602 indication of a degree of efficiency

[0370] 1700 visual presentation

[0371] 1702 recommendation 1800 method

Claims

CLAIMS:1 . A computer-implemented method (900, 1000, 1 100) for characterizing energy consumption for heating an interior of a building (400), the method comprising: receiving, in particular by a data processing device, in particular a controller (100), a setpoint temperature (Tsetpoint) for the building (400); determining, in particular by a data processing device, in particular a controller (100), an average outdoor temperature (Toutdoor) over a first predetermined time period (pdAt) for a location of the building (400); measuring, in particular based on sensor readings communicated to the data processing device, in particular to the controller (100), a total central heating energy consumption (EC) for the building (400) over the first predetermined time period (pdAt); adding, in particular via the data processing device, in particular the controller (100), the determined average outdoor temperature (Toutdoor) and measured total central heating energy consumption (EC) to a first data set (112) comprising a plurality of average outdoor temperatures (Toutdoor) and corresponding total central heating energy consumptions (EC) over a plurality of predetermined time periods (pdAt) having durations substantially the same as the first predetermined time period (pdAt); determining, in particular by the data processing device, in particular the controller (100), a weather independent normalized heating energy consumption (ENEC) for maintaining the setpoint temperature (Tsetpoint) in the building (400) based on a predetermined reference outdoor temperature (Tret) and the first data set (1 12); and characterizing, in particular by a data processing device, in particular the controller (100), the heating energy consumption (EC) for heating the building (400) based on the normalized heating energy consumption (ENEC) and a base temperature (Tbase) wherein the base temperature (Tbase) is the outdoor temperature (Toutdoor) at which heating is required to keep an indoor temperature (Tindoor) minimally at the setpoint temperature (Tsetpoint) and wherein the base temperature (Tbase) is selectively obtained by way of optimization and / or linear fit and / or distribution based approach.

2. The method of claim 1 , wherein the reference outdoor temperature (Tref) is lower than the base temperature (Tbase) in case of heating.

3. The method of claim 1 or 2, wherein the normalized heating energy consumption (ENEC) is determined based onand / or4. Method according to any of the preceding claims, wherein the base temperature (Tbase) is obtained by way of a. an optimization operation, wherein the optimization operation is based onwherein Pintemai is the total daily power level added to the interior of the building by all other devices within the building in the form of heat; and / or b. a linear fit, wherein the base temperature (Tbase) is determined by way of zero-crossing of the linear curve defined bywherein a is -At ■ UA and b is At ■ UA ■ Tsetpoint- Einternaland / orc. a distribution based approach, wherein the average outdoor temperature (Toutdoor) and the total central heating energy consumption (EC) are determined for a predetermined time period (pdAt), and wherein the data is stored in the first data set (112) and aggregated for a further predetermined time period (pdAt), the resulting dataset is divided into samples of nonzero energy consumption (nonzeroEC) and zero energy consumption (zeroEC) and visualized as probability density functions (PDF) of the nonzero energy consumption function (1204) and the zero energy consumption function (1202) and the overlap range (1306) of the zero energy consumption function (1202) and the nonzero energy consumption function (1204) is determined.

5. The method of any of the preceding claims, wherein: the first predetermined time period (pdAt) has a duration of one day; and / or the plurality of average outdoor temperatures (Toutdoor) comprise daily average outdoor temperatures (Toutdoor) ; and / or the total central heating energy consumptions (EC) comprise daily total heating energy consumptions (EC) on days for which the total central heating energy consumption was zero (zeroEC) and on days for which the total heating energy consumption was non-zero (nonzeroEC).

6. The method of any of the preceding claims, wherein the predetermined reference outdoor temperature (Tret) is selected to be near the median of daily average outdoor temperatures (Toutdoor) that lie below the base temperature (Tbase).

7. The method of any of the preceding claims, wherein the predetermined reference outdoor temperature (Tret) is 3° C or 5° C.

8. The method of any one of the preceding claims, wherein calculating the weather independent normalized heating energy consumption (ENEC) for thebuilding (400) based on the first data set (1 12) comprises using a linear fit through data points of the first data set (1 12).

9. The method of any of the preceding claims, wherein the weather independent normalized heating energy consumption (ENEC) for the building (400) is determined by evaluating the linear fit at the predetermined reference outdoor temperature T(ref).

10. The method of claim any of the preceding claims, wherein the linear fit comprises a least squares fit.1 1 . The method of any one of claims any of the preceding claims, wherein calculating the weather independent normalized heating energy consumption (ENEC) for the building (400) based on the first data set (1 12) comprises fitting the first data set (1 12) to a model of a heat balance for the building (400).

12. The method of any one of the preceding claims, wherein calculating the weather independent normalized heating energy consumption (ENEC) for the building (400) based on the first data set (1 12) comprises calculating a heating energy consumption (EC) per heating degree day (HDD), wherein the heating degree days (HDD) are determined by: subtracting the average temperature (Toutdoor) at the building (400) location over a day from the base temperature (Tbase) if the average temperature (aveToutdoor) at the building location over the day is lower than the base temperature (Tbase); and determining the heating degree days (HDD) to be zero for days on which the average temperature (Toutdoor) at the building location over the day is greater than or equal to the base temperature (Tbase).

13. The method of any of the preceding claims, wherein calculating the base temperature (Tbase) comprises: calculating the zero energy probability density function (1202) based on outdoor temperature (Toutdoor) and corresponding measured total central heatingenergy consumptions (EC) in the first data set (1 12) for which the measured total central heating energy consumption was zero (zeroEC), the zero energy probability density function (1202) providing a probability of having a total central heating energy consumption of zero (zeroEC) for a given outdoor temperature (Toutdoor) ; calculating the nonzero energy probability density function (1204) based on outdoor temperature (Toutdoor) and corresponding measured total central heating energy consumptions (EC) in the first data set (1 12) for which the measured total central heating energy consumption was nonzero (nonzeroEC), the nonzero energy probability density function (1204) providing a probability of having a nonzero total central heating energy consumption (nonzeroEC) for a given outdoor temperature (Toutdoor) ; determining the overlap range (1306) of outdoor temperatures (Toutdoor) over which the zero energy probability density function (1202) and the nonzero probability density function (1204) overlap; and calculating the base temperature (Tbase) to be a temperature within the overlap range (1306).

14. The method of any one of the preceding claims, wherein the first predetermined time period (pdAt) has a predetermined recurring interval of time, in particular one hour or one day or one week or two weeks or one month.

15. The method of any one of the preceding claims, wherein determining an average outdoor temperature (Toutdoor) comprises accessing the average outdoor temperature (Toutdoor) for a location of the building in a remotely maintained database.

16. The method of any one the preceding claims, wherein determining an average outdoor temperature (Toutdoor) comprises measuring the outdoor temperature (Toutdoor) at the location of the building (400) using a temperature sensor (216).

17. The method of any of the preceding claims, wherein the method further comprises the step of modeling energy savings predictions and / or future energy consumption trends based on„ „Energy Savingswherein ATsetpoint[°C] is the change in setpoint temperature with respect to the setpoint temperature (Tsetpoint) used during a predetermined analysis period.

18. The method according to any of the preceding claims, further comprising the step of generating a report command, wherein the report command includes actionable insights and recommendations and / or commands for controlling an energy management system (200) to optimize energy consumption.

19. The method of any of the preceding claims, comprising the further step of a. updating the modeled savings predictions, in particular continuously, based on at least the information stored in the first data set; and / or b. generating energy predictions using the developed modeled savings predictions and presenting the modeled savings predictions through a user interface; and / or c. generating a report based on the modeled savings predictions and / or future energy consumption trends, wherein the report includes actionable insights and recommendations for predictive support and / or maintenance to optimize the energy management system (200) and / or to optimize the performance of the energy management system (200); and / or d. initiating predictive maintenance and / or support actions based on the recommendations in the report command, in particular initiating scheduling commands for maintenance tasks, initiating adjusting operational parameters, and / or notifying relevant personnel.

20. An energy management system (200) comprising:a data processing device, in particular a controller (100), in particular a processor (102); and a memory (110) to store program instructions (116) operable to cause the processor (102) to perform operations according to any of claims 1 to 19.21 . A computer program product comprising program instructions (116) operable to cause a processor (102) to perform operations according to any of claims 1 to 19.

22. A computer program product according to claim 21 , comprising instructions to cause the energy management system (200) of claim 20 to execute the steps of the method of any of the preceding claims 1 to 19.

23. A data processing device comprising means for carrying out the method of at least one of the claims 1 to 19.

24. The data processing device according to claim 23, wherein the data processing device is a controller (100), in particular a computer system or embedded controller (100), or a processor.

25. The data processing device according to claim 23 or 24, comprising a. one or more microprocessors or processors; b. and / or integrated circuits; and / or c. a memory; and / or d. a storage interface; and / or e. a communication interface.

26. The data processing device according to claim 25 wherein the one or more microprocessors and / or processors and / or integrated circuits are configured to execute program instructions stored in the memory (110).

27. The data processing device according to claims 23 to 26, wherein the data processing device, in particular the controller (100) or at least one microprocessor or processor, is incorporated into a networking device.

28. The data processing device according to claims 23 to 27, wherein the data processing device, in particular the controller (100) or at least one microprocessor or processor, in particular at least one of the components thereof, is virtualized and / or cloud based.

29. The data processing device according to claims 25, wherein the memory, in particular a random-access memory, is configured to contain data, an operating system, and a program, in particular a computer program product according to claim 21 or 22.

30. The data processing device according to claim 25, wherein the program or set of programs comprises program instructions that, when executed by the data processing device, in particular the controller (100) or at least one microprocessor or processor, cause the a. control of actions taken by the data processing device and / or b. data processing device to carry out one or more of the methods according to claims 1 to 19.31 . The data processing device according to any of claims 23 to 30, wherein the storage interface (120) is configured to connect storage devices (125) to the data processing device, in particular the controller (100).

32. The data processing device according to claim 25 or 31 , wherein the storage interface (120) comprises a storage device (125) and wherein a. the storage device (125) is a solid-state drive using an integrated circuit assembly to store data persistently or b. the storage interface (120) is a hard drive or c. the storage device (125) is an optical drive, ord. the storage device (125) is a card reader that receives a removable nonvolatile semiconductor memory card or e. the storage interface (120) is configured to provide a universal serial bus connection to which the storage device (125) is configured to be hot- pluggable, and the storage device (125) may be a flash memory device, in particular a USB thumb drive.

33. The data processing device according to claim 25, wherein the data processing device, in particular the controller (100), is configured to use virtual memory addressing techniques.

34. The data processing device according to claim 25, wherein the communication interface (140) is configured to communicatively connect the controller (100) to other controllers, computer systems, or or connected or network-enabled devices via a communication channel (160).

35. The data processing device according to claim 34, wherein the communication channel (160) is a serial or parallel connection, a wired, wireless, mesh or cellular network or a combination of communication channels (160).

36. The data processing device according to any of claims 25 or 34, wherein the communication interface (140) comprises a combination of hardware and software that enables communications on the communication channel (160).

37. The data processing device according to any of claims 25 or 34 to 36, wherein the software in the communication interface (140) comprises software, wherein the software uses one or more communication protocols to communicate over the communication channel (160), in particular network protocols, in particular the TCP / IP (Transmission Control Protocol / lnternet Protocol).

38. The data processing device according to any of claims 23 to 37, wherein the data processing device, in particular the controller (100), is configured to regenerate on a recurring basis, a normalized model of the consumption of heatingenergy (ENEC) of a building (400), in particular in accordance with the method of any of claims 1 to 19.

39. The data processing device according to claim 38, wherein the recurring basis is hourly, daily, weekly, bi-weekly, monthly, or a predetermined recurring interval of time.

40. The data processing device according to claims 23 to 39, wherein the data processing device, in particular the controller (100), is configured to perform an analysis based the normalized model of the consumption of heating energy (ENEC) obtained based on the method according to claims 1 to 19.

41. The data processing device according to claim 40, wherein the analysis comprises the derivation of recommendations for improvements to insulation and / or other recommendations to improve the ability of the building (400) to conserve heating energy.

42. The data processing device according to claim 40 or 41 , wherein the analysis further comprises a. the derivation of recommendations to add electrical, thermal and / or other forms of energy storage to the building (400) and / or b. the analysis further comprises detecting changes in patterns of user behavior, and providing notices of such changes to users, and / or c. wherein the analysis further comprises predicting levels of consumption of heating energy in view of weather forecasts for upcoming hours and / or days; and / or d. the analysis further comprises the derivation of recommendations for user behavioral changes to increase the efficiency and / or cost effectiveness with which the building is heated.

43. The data processing device according to claim 23 to 42, wherein the data processing device, in particular the controller (100), is incorporated into athermostat by which the setpoint temperature of the building may be set, in particular manually set.

44. The data processing device according to claim 23 to 43, wherein the data processing device, in particular the controller (100), is a remotely located controller (100), and wherein the data processing device, in particular the controller (100), is configured to communicate with a remote storage, in particular a remote server, wherein the continuity of data gathered of the building (400) is maintained the controller being configured to preserve the data in the remote storage, in particular on the remotely located server.

45. A networking device wherein the networking device is configured to perform the method according to claim 1 to 19.

46. The networking device according claims 45, wherein the networking device comprises: a. a processor; and / or b. a memory operatively connected to the processor; and / or c. a communication interface configured to connect the device to a network.

47. The networking device according to claim 45 or 46, wherein the networking device is a router, hub, switch, or an edge device such as a modem.

48. A computer readable data carrier having stored thereon the computer program product according to claim 21 or 22.

49. A data carrier signal carrying the computer program product according to claim 21 or 22.

50. An energy management appliance (202) configured to operate based on the method according to claims 1 to 19.51 . The energy management appliance (202) according to claim 50, wherein the energy management appliance (202) comprises a data processing device according to claims 23 to 43, in particular a controller and a memory storing instructions which, when executed by the data processing device perform operations comprising implementing the method according to claims 1 to 19.

52. The energy management appliance (202) according to claim 50 or 51 , wherein the energy management appliance ((202)) is a device generating usable energy and / or manages, controls, and / or senses conditions relevant to usable energy in a form such as heat, cold, mechanical, and / or electrical energy.

53. The energy management appliance (202) according to any of claims 50 to52, wherein the energy management appliance is a boiler, a heat pump, an air conditioning unit, a fire place and / or a ventilation device.

54. The energy management appliance (202) according to any of claims 50 to53, wherein the energy management appliance (202) is comprised in an energy management system (200), in particular an energy management system (200) according to claim 20.

55. Use of a method according to claim 1 to 19 for determining, reporting, actioning, and / or predicting energy savings for heating a building, in particular predictions for cost savings, carbon footprint reduction, performance monitoring, and / or predictive maintenance and / or support and / or control, in particular remote support and / or control, of an energy management system (200), in particular an energy management system (200) according to claim 20 or of an energy management appliance (202), in particular an energy management appliance (202) according to claims 50 to 54.

Images