Energy scheduling method and device, electronic equipment and computer readable storage medium
By detecting and predicting household electricity consumption in a collaborative network and sending electricity consumption scheduling instructions to other households, the problem of difficulty in regulating energy when families face sudden high energy consumption risks is solved, real-time energy scheduling and avoiding waste is achieved.
Patent Information
- Application Number
- CN202411857756.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-17
- Publication Date
- 2025-05-13
AI Technical Summary
When families face sudden high energy consumption risks, it is difficult for them to effectively regulate energy, resulting in energy waste and increased costs.
By detecting household electricity consumption in the collaborative network, families that may reach the power consumption threshold are predicted, and power consumption scheduling instructions are sent to other households to adjust the electricity consumption.
Real-time energy scheduling is achieved, high energy consumption risks are avoided, energy waste is avoided, and the energy regulation capacity of households is improved.
Smart Images
Figure CN119990579A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of energy processing technology, and in particular to an energy scheduling method, an energy scheduling device, an electronic device and a computer-readable storage medium. Background Art
[0002] In recent years, with the continuous improvement of people's living standards and the enhancement of environmental awareness, home energy saving has become a hot topic. Traditional home energy saving methods mainly rely on energy efficiency optimization within the home, but there are certain limitations.
[0003] First, when a household faces sudden high energy consumption risks, such as extreme weather or large-scale events, the household's energy regulation capabilities often cannot meet actual needs, resulting in energy waste and increased costs. Second, the utilization rate of equipment within the household may be uneven, resulting in a certain degree of energy waste. Summary of the invention
[0004] In view of the above problems, embodiments of the present invention are proposed to provide an energy scheduling method, an energy scheduling device, an electronic device and a computer-readable storage medium that overcome the above problems or at least partially solve the above problems.
[0005] An embodiment of the present invention discloses an energy scheduling method, the method comprising:
[0006] Detecting whether there is a first household in the collaborative network whose electricity consumption reaches an electricity consumption threshold after a preset time period;
[0007] If so, determining a second family from other families in the collaborative network; the other families are families in the collaborative network other than the first family;
[0008] A first power consumption scheduling instruction is sent to the second household, so that the second household performs a corresponding power consumption adjustment action in response to the first power consumption scheduling instruction.
[0009] In one or more embodiments, detecting whether there is a first household in the collaborative network whose electricity consumption reaches the electricity consumption threshold after a preset time period includes:
[0010] Obtaining the current power consumption of each household in the collaborative network at the current moment; each household has a one-to-one corresponding power consumption threshold and household energy consumption prediction model;
[0011] The energy consumption prediction models of each household and the current power consumption of each household are used to make predictions for each household, and it is detected whether there is a first household among the households whose power consumption reaches the corresponding power consumption threshold after a preset time period.
[0012] In one or more embodiments, each household energy consumption prediction model is generated by:
[0013] Obtain historical electricity consumption data corresponding to each household, as well as household-related information corresponding to each household;
[0014] The historical electricity consumption data and relevant information of each household are used to build a model to obtain a household energy consumption prediction model corresponding to each household.
[0015] In one or more embodiments, obtaining the historical electricity consumption data corresponding to each household includes:
[0016] The historical electricity consumption data corresponding to each household is obtained through an electricity meter with communication function.
[0017] In one or more embodiments, the power consumption threshold corresponding to each household is obtained in the following manner:
[0018] Obtain the energy consumption intention and electricity consumption adjustment strategy corresponding to each household;
[0019] Each energy consumption intention, each power consumption adjustment strategy and each household energy consumption prediction model are used to generate the corresponding power consumption threshold for each household.
[0020] In one or more embodiments, determining the second home from other homes in the collaborative network includes:
[0021] Calculating a target power consumption required by the first household based on the current power consumption of the first household and a power consumption threshold;
[0022] Obtain the current power consumption and power consumption adjustment strategies corresponding to other households;
[0023] A second household that can dispatch the target power consumption within the preset time period is calculated based on each current power consumption and each power consumption adjustment strategy.
[0024] In one or more embodiments, further comprising:
[0025] After the preset time period ends, sending a second power consumption scheduling instruction to the second household, so that the second household responds to the second power consumption scheduling instruction and cancels the power consumption adjustment action;
[0026] Generate a first adjustment suggestion for the electricity consumption adjustment strategy of the first household, and generate a second adjustment suggestion for the electricity consumption adjustment strategy of the second household, and send the first adjustment suggestion to the first household, and send the second adjustment suggestion to the second household.
[0027] Accordingly, an embodiment of the present invention discloses an energy scheduling device, the device comprising:
[0028] A detection module, used to detect whether there is a first household in the collaborative network whose electricity consumption reaches an electricity consumption threshold after a preset time period;
[0029] a determination module, configured to determine a second family from other families in the collaborative network, if any; the other families are families in the collaborative network other than the first family;
[0030] The sending module is used to send the first power consumption scheduling instruction to the second household, so that the second household responds to the first power consumption scheduling instruction and performs a corresponding power consumption adjustment action.
[0031] In one or more embodiments, the detection module includes:
[0032] An acquisition submodule is used to acquire the current power consumption of each household in the collaborative network at the current moment; each household has a one-to-one corresponding power consumption threshold and household energy consumption prediction model;
[0033] The prediction submodule is used to predict each household using the household energy consumption prediction model and each current power consumption, and detect whether there is a first household among the households whose power consumption reaches the corresponding power consumption threshold after a preset time period.
[0034] In one or more embodiments, further comprising:
[0035] The acquisition submodule is further used to acquire the historical electricity consumption data corresponding to each household, as well as household-related information corresponding to each household;
[0036] The modeling submodule is used to use various historical electricity consumption data and relevant information of each household to build a model to obtain a household energy consumption prediction model corresponding to each household.
[0037] In one or more embodiments, the acquisition submodule is specifically used to:
[0038] The historical electricity consumption data corresponding to each household is obtained through an electricity meter with communication function.
[0039] In one or more embodiments, further comprising:
[0040] The acquisition submodule is also used to obtain the energy consumption intention and power consumption adjustment strategy corresponding to each household;
[0041] The generation submodule is used to generate the power consumption threshold corresponding to each household by adopting each energy consumption intention, each power consumption adjustment strategy and each household energy consumption prediction model.
[0042] In one or more embodiments, the determining module is specifically configured to:
[0043] Calculating a target power consumption required by the first household based on the current power consumption of the first household and a power consumption threshold;
[0044] Obtain the current power consumption and power consumption adjustment strategies corresponding to other households;
[0045] A second household that can dispatch the target power consumption within the preset time period is calculated based on each current power consumption and each power consumption adjustment strategy.
[0046] In one or more embodiments, further comprising:
[0047] The sending module is further configured to send a second power consumption scheduling instruction to the second household after the preset time period ends, so that the second household responds to the second power consumption scheduling instruction and cancels the power consumption adjustment action;
[0048] A generation module is used to generate a first adjustment suggestion for the electricity consumption adjustment strategy of the first household, and to generate a second adjustment suggestion for the electricity consumption adjustment strategy of the second household, and to send the first adjustment suggestion to the first household, and to send the second adjustment suggestion to the second household.
[0049] Correspondingly, an embodiment of the present invention discloses an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the various steps of the above-mentioned energy scheduling method embodiment when executed by the processor.
[0050] Accordingly, an embodiment of the present invention discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various steps of the above-mentioned energy scheduling method embodiment are implemented.
[0051] The embodiments of the present invention include the following advantages:
[0052] The cloud platform detects whether there is a first family in the collaborative network whose electricity consumption reaches the electricity consumption threshold after a preset time period; if so, a second family is determined from other families in the collaborative network; the other families are families in the collaborative network other than the first family; a first electricity consumption scheduling instruction is sent to the second family, so that the second family responds to the first electricity consumption scheduling instruction and performs the corresponding electricity consumption adjustment action. In this way, when a family in the collaborative network has a sudden high energy consumption risk, the cloud platform can determine whether it is possible to schedule the electricity consumption of other families in the collaborative network. If so, it can schedule the electricity consumption of other families, thereby timely avoiding the risk of high energy consumption, realizing real-time energy scheduling, and avoiding energy waste. BRIEF DESCRIPTION OF THE DRAWINGS
[0053] Figure 1 This is a flow chart of the steps of Embodiment 1 of an energy scheduling method of the present invention;
[0054] Figure 2 is an architectural diagram of a collaborative network of the present invention;
[0055] Figure 3 This is a flow chart of the steps of Embodiment 2 of an energy scheduling method of the present invention;
[0056] Figure 4 It is a structural block diagram of an energy scheduling device embodiment of the present invention. DETAILED DESCRIPTION
[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.
[0058] One of the core concepts of the embodiments of the present invention is that when a household in a collaborative network faces a sudden high energy consumption risk, the cloud platform can determine whether electricity consumption of other households in the collaborative network can be scheduled. If so, electricity consumption of other households can be scheduled, thereby timely avoiding the high energy consumption risk, realizing real-time scheduling of energy, and avoiding energy waste.
[0059] Reference Figure 1 , showing a flow chart of the steps of an energy scheduling method embodiment 1 of the present invention, which can be applied to a cloud platform, the cloud platform can be connected to the electric meters of multiple households, and the cloud platform and the multiple households form a collaborative network, referring to Figure 2, shows the architecture of the collaborative network, where the electric meter can collect the power consumption data of each electrical device in the home in real time, as well as the historical power consumption data, and upload the power consumption data to the cloud platform through the Internet of Things. The cloud platform can send power consumption scheduling instructions to any household, and after obtaining the power consumption scheduling instructions, the household can perform the corresponding power consumption adjustment action (detailed later).
[0060] It should be noted that households joining the collaborative network need to obtain the consent of the owner, that is, they can join the collaborative network after the owner agrees. In this way, when a household in the collaborative network has a sudden high energy consumption risk, the cloud platform can determine whether it is possible to dispatch the electricity consumption of other households in the collaborative network. If so, it can dispatch the electricity consumption of other households, thereby timely avoiding high energy consumption risks and achieving overall collaborative energy saving.
[0061] The method may specifically include the following steps:
[0062] Step 101 , detecting whether there is a first household in the collaborative network whose electricity consumption reaches an electricity consumption threshold after a preset time period.
[0063] The cloud platform can monitor the power consumption of each household in the collaborative network, and detect whether the power consumption of each household is likely to reach the power consumption threshold after a preset time period. For example, it can detect whether the power consumption of each household is likely to reach the power consumption threshold after 1 hour.
[0064] If it is detected that the power consumption of a certain household (referred to as "the first household" for ease of description) may reach a power consumption threshold after a preset time period, then preparations can be made to execute power consumption scheduling.
[0065] It should be noted that the preset time period may be other values besides 1 hour. In practical applications, the specific value of the preset time period may be set according to actual needs, and the embodiment of the present invention does not limit this.
[0066] In the embodiment of the present invention, the detecting whether there is a first household in the collaborative network whose electricity consumption reaches the electricity consumption threshold after a preset time period includes:
[0067] Obtaining the current power consumption of each household in the collaborative network at the current moment; each household has a one-to-one corresponding power consumption threshold and household energy consumption prediction model;
[0068] The energy consumption prediction model of each household and each current power consumption are used to make predictions for each household, and it is detected whether there is a first household among the households whose power consumption reaches a corresponding power consumption threshold after a preset time period.
[0069] Specifically, the cloud platform can obtain the current power consumption of each household in the collaborative network in real time (recorded as "current power consumption"). Since each household has a one-to-one corresponding power consumption threshold and household energy consumption prediction model, the current power consumption of each household can be input into the household energy consumption prediction model corresponding to each household. Each household energy consumption prediction model uses the corresponding current power consumption to make predictions and obtain the corresponding prediction results of each household. Among them, the prediction results may include the power consumption threshold that may be reached and the power consumption threshold that will not be reached.
[0070] If the prediction result of a household is that it is likely to reach the electricity consumption threshold, then the household can be determined as the first household.
[0071] It should be noted that the purpose of the prediction result is to determine whether the power consumption threshold may be reached after a preset time period. In addition to the above-mentioned form, the prediction result can also be in other forms. In actual applications, the specific form of the prediction result can be set according to actual needs, and the embodiment of the present invention does not limit this.
[0072] Furthermore, the number of first families may be one, multiple, or zero. For ease of understanding, the embodiment of the present invention uses the number of first families as one for detailed description. In actual applications, the specific number of first families can be adjusted according to actual conditions, and the embodiment of the present invention does not limit this.
[0073] In the embodiment of the present invention, each household energy consumption prediction model is generated in the following manner:
[0074] Obtain historical electricity consumption data corresponding to each household, as well as household-related information corresponding to each household;
[0075] The historical electricity consumption data and relevant information of each household are used to build a model to obtain a household energy consumption prediction model corresponding to each household.
[0076] Specifically, the cloud platform can obtain the historical electricity consumption data corresponding to each household, as well as the household-related information corresponding to each household, wherein the household-related information may include the number, age, and historical electricity consumption behavior of family members.
[0077] Then, the machine learning algorithm is used to analyze and model the historical electricity consumption data and household-related information corresponding to each household, so as to obtain the household energy consumption prediction model corresponding to each household.
[0078] It should be noted that, in addition to the above information, the family-related information may also include other information. In practical applications, the specific indicators of the family-related information may be set according to actual needs, and the embodiments of the present invention do not limit this.
[0079] Furthermore, the machine learning algorithm may include at least one of decision tree regression, random forest regression, and neural network regression, and may also include other algorithms. In practical applications, the specific algorithm adopted by the machine learning algorithm can also be set according to actual needs, and the embodiments of the present invention do not limit this.
[0080] Furthermore, in the modeling process, in addition to using historical electricity consumption data, other data such as meteorological data and holiday information can also be combined to improve the accuracy of the prediction. In actual applications, the specific data used in the modeling process can also be set according to actual needs, and the embodiments of the present invention do not limit this.
[0081] In the embodiment of the present invention, the step of obtaining the historical electricity consumption data corresponding to each household includes:
[0082] The historical electricity consumption data corresponding to each household is obtained through an electricity meter with communication function.
[0083] Specifically, each household in the collaborative network is equipped with a smart meter with communication function, which can collect the power consumption data of each household's electrical equipment in real time, as well as historical power consumption data, and upload the power consumption data to the cloud platform through the Internet of Things. In other words, the cloud platform can obtain the historical power consumption data of each household through the smart meter of each household.
[0084] In the embodiment of the present invention, the power consumption threshold corresponding to each household is obtained in the following manner:
[0085] Obtain the energy consumption intention and electricity consumption adjustment strategy corresponding to each household;
[0086] Each energy consumption intention, each power consumption adjustment strategy and each household energy consumption prediction model are used to generate the corresponding power consumption threshold for each household.
[0087] Specifically, the cloud platform can obtain the energy consumption intention and electricity consumption adjustment strategy corresponding to each household. Among them, the energy consumption intention represents the expectations of the household owner for energy consumption. For example, the energy consumption intention of household A is "to control the monthly electricity bill within 200 yuan", and the energy consumption intention of household B is "to reduce the monthly electricity bill by 20%". The electricity consumption adjustment strategy represents the strategy for adjusting electricity consumption that the household owner has accepted. For example, the electricity consumption adjustment strategy of household A is "automatic adjustment of air conditioning temperature", and the electricity consumption adjustment strategy of household B is "turn off some non-essential electrical equipment after 8 pm".
[0088] Then, the energy consumption intention and electricity consumption adjustment strategy of each household are input into the corresponding household energy consumption prediction model to obtain the electricity consumption threshold value of each household.
[0089] It should be noted that the energy consumption intention and electricity consumption adjustment strategy can be obtained through the owner's input when the family joins the collaborative network. After obtaining the energy consumption intention and electricity consumption adjustment strategy corresponding to the family, they can be stored in the storage space of the cloud platform. In this way, when determining the electricity consumption threshold corresponding to the family, it can be obtained from the storage space.
[0090] Step 102: if so, determine a second family from other families in the collaborative network; the other families are families in the collaborative network other than the first family.
[0091] If the first family exists, then the family that can be scheduled for electricity consumption (referred to as the "second family") can be determined from other families in the collaborative network, that is, the energy consumption of the second family is scheduled to be used by the first family. Among them, the other families in the collaborative network are families other than the first family.
[0092] In the embodiment of the present invention, determining the second home from other homes in the collaborative network includes:
[0093] Calculating a target power consumption required by the first household based on the current power consumption of the first household and a power consumption threshold;
[0094] Obtain the current power consumption and power consumption adjustment strategies corresponding to other households;
[0095] A second household that can dispatch the target power consumption within the preset time period is calculated based on each current power consumption and each power consumption adjustment strategy.
[0096] Specifically, after determining the first household, the cloud platform can further calculate the electricity consumption required by the first household (recorded as "target electricity consumption") based on the first household's current electricity consumption and the electricity consumption threshold. That is to say, if the first household maintains its current electricity consumption and continues to use electricity at the same time, plus the target electricity consumption, it will not exceed the electricity consumption threshold.
[0097] After obtaining the target power consumption, the current power consumption and power consumption adjustment strategies corresponding to other households can be obtained, and then the other households can be screened according to the power consumption adjustment strategies, and the households that do not meet the power consumption scheduling can be filtered out. For example, the power consumption adjustment strategy of household B is "turn off some non-essential power equipment after 8 pm", and non-essential power equipment has been turned off, so household B cannot perform power consumption scheduling.
[0098] Then, the current power consumption and household energy consumption prediction model is used to determine the households that can dispatch the target power consumption within the preset time period from the remaining households (referred to as the "second household"). For example, if household C (the first household) needs 10 kWh of electricity in the next hour to avoid exceeding the power consumption threshold, and after calculation, it is determined that household A can dispatch 10 kWh of electricity in the next hour, then household A can be used as the second household.
[0099] It should be noted that the number of the second households can be one or more. For example, if household C needs 10 kWh of electricity, household A can dispatch 8 kWh of electricity, and household B can dispatch 2 kWh of electricity. In the embodiment of the present invention, only one second household is used for detailed description. In actual applications, the number of second households can be adjusted according to actual needs, and the embodiment of the present invention does not limit this.
[0100] Step 103: Send a first power consumption scheduling instruction to the second household, so that the second household responds to the first power consumption scheduling instruction and performs a corresponding power consumption adjustment action.
[0101] After the cloud platform determines the second family, it can send an electricity consumption scheduling instruction (recorded as "first electricity consumption scheduling instruction") to the second family. After the second family obtains the first electricity consumption scheduling instruction, it can execute the corresponding electricity consumption adjustment action. For example, family A can dispatch 10 kWh of electricity, and the electricity consumption adjustment strategy of family A is "automatic adjustment of air conditioning temperature". After calculation, it can be determined that the air conditioning temperature needs to be raised from 26°C to 28°C to dispatch 10 kWh of electricity. Then, the cloud platform can send a power consumption scheduling instruction to family A to "raise the air conditioning temperature by 2°C". After obtaining the power consumption scheduling instruction, family A can raise the air conditioning temperature from 26°C to 28°C.
[0102] In an embodiment of the present invention, the cloud platform detects whether there is a first family in the collaborative network whose electricity consumption reaches the electricity consumption threshold after a preset time period; if so, a second family is determined from other families in the collaborative network; the other families are families in the collaborative network other than the first family; a first electricity consumption scheduling instruction is sent to the second family, so that the second family responds to the first electricity consumption scheduling instruction and performs a corresponding electricity consumption adjustment action. In this way, when a family in the collaborative network has a sudden high energy consumption risk, the cloud platform can determine whether electricity consumption can be scheduled for other families in the collaborative network. If so, electricity consumption can be scheduled for other families, thereby timely avoiding high energy consumption risks, achieving real-time energy scheduling, and avoiding energy waste.
[0103] Reference Figure 3 , shows a flow chart of the steps of Embodiment 2 of an energy scheduling method of the present invention, which may specifically include the following steps:
[0104] Step 301 , detecting whether there is a first household in the collaborative network whose electricity consumption reaches an electricity consumption threshold after a preset time period.
[0105] The cloud platform can monitor the power consumption of each household in the collaborative network, and detect whether the power consumption of each household is likely to reach the power consumption threshold after a preset time period. For example, it can detect whether the power consumption of each household is likely to reach the power consumption threshold after 1 hour.
[0106] If it is detected that the electricity consumption of the first household may reach the electricity consumption threshold after a preset time period, then preparations can be made to execute electricity consumption scheduling.
[0107] It should be noted that the preset time period may be other values besides 1 hour. In practical applications, the specific value of the preset time period may be set according to actual needs, and the embodiment of the present invention does not limit this.
[0108] In the embodiment of the present invention, the detecting whether there is a first household in the collaborative network whose electricity consumption reaches the electricity consumption threshold after a preset time period includes:
[0109] Obtaining the current power consumption of each household in the collaborative network at the current moment; each household has a one-to-one corresponding power consumption threshold and household energy consumption prediction model;
[0110] The energy consumption prediction model of each household and each current power consumption are used to make predictions for each household, and it is detected whether there is a first household among the households whose power consumption reaches a corresponding power consumption threshold after a preset time period.
[0111] Specifically, the cloud platform can obtain the current power consumption of each household in the collaborative network at the current moment in real time. Since each household has a one-to-one corresponding power consumption threshold and household energy consumption prediction model, the current power consumption of each household can be input into the household energy consumption prediction model corresponding to each household. Each household energy consumption prediction model uses the corresponding current power consumption to make predictions and obtain the corresponding prediction results of each household. Among them, the prediction results may include the power consumption threshold that may be reached and the power consumption threshold that will not be reached.
[0112] If the prediction result of a household is that it is likely to reach the electricity consumption threshold, then the household can be determined as the first household.
[0113] It should be noted that the purpose of the prediction result is to determine whether the power consumption threshold may be reached after a preset time period. In addition to the above-mentioned form, the prediction result can also be in other forms. In actual applications, the specific form of the prediction result can be set according to actual needs, and the embodiment of the present invention does not limit this.
[0114] Furthermore, the number of first families may be one, multiple, or zero. For ease of understanding, the embodiment of the present invention uses the number of first families as one for detailed description. In actual applications, the specific number of first families can be adjusted according to actual conditions, and the embodiment of the present invention does not limit this.
[0115] In the embodiment of the present invention, each household energy consumption prediction model is generated in the following manner:
[0116] Obtain historical electricity consumption data corresponding to each household, as well as household-related information corresponding to each household;
[0117] The historical electricity consumption data and relevant information of each household are used to build a model to obtain a household energy consumption prediction model corresponding to each household.
[0118] Specifically, the cloud platform can obtain the historical electricity consumption data corresponding to each household, as well as the household-related information corresponding to each household, wherein the household-related information may include the number, age, and historical electricity consumption behavior of family members.
[0119] Then, the machine learning algorithm is used to analyze and model the historical electricity consumption data and household-related information corresponding to each household, so as to obtain the household energy consumption prediction model corresponding to each household.
[0120] It should be noted that, in addition to the above information, the family-related information may also include other information. In practical applications, the specific indicators of the family-related information may be set according to actual needs, and the embodiments of the present invention do not limit this.
[0121] Furthermore, the machine learning algorithm may include at least one of decision tree regression, random forest regression, and neural network regression, and may also include other algorithms. In practical applications, the specific algorithm adopted by the machine learning algorithm can also be set according to actual needs, and the embodiments of the present invention do not limit this.
[0122] Furthermore, in the modeling process, in addition to using historical electricity consumption data, other data such as meteorological data and holiday information can also be combined to improve the accuracy of the prediction. In actual applications, the specific data used in the modeling process can also be set according to actual needs, and the embodiments of the present invention do not limit this.
[0123] In the embodiment of the present invention, the step of obtaining the historical electricity consumption data corresponding to each household includes:
[0124] The historical electricity consumption data corresponding to each household is obtained through an electricity meter with communication function.
[0125] Specifically, each household in the collaborative network is equipped with a smart meter with communication function, which can collect the power consumption data of each household's electrical equipment in real time, as well as historical power consumption data, and upload the power consumption data to the cloud platform through the Internet of Things. In other words, the cloud platform can obtain the historical power consumption data of each household through the smart meter of each household.
[0126] In the embodiment of the present invention, the power consumption threshold corresponding to each household is obtained in the following manner:
[0127] Obtain the energy consumption intention and electricity consumption adjustment strategy corresponding to each household;
[0128] Each energy consumption intention, each power consumption adjustment strategy and each household energy consumption prediction model are used to generate the corresponding power consumption threshold for each household.
[0129] Specifically, the cloud platform can obtain the energy consumption intention and electricity consumption adjustment strategy corresponding to each household. Among them, the energy consumption intention represents the expectations of the household owner for energy consumption. For example, the energy consumption intention of household A is "to control the monthly electricity bill within 200 yuan", and the energy consumption intention of household B is "to reduce the monthly electricity bill by 20%". The electricity consumption adjustment strategy represents the strategy for adjusting electricity consumption that the household owner has accepted. For example, the electricity consumption adjustment strategy of household A is "automatic adjustment of air conditioning temperature", and the electricity consumption adjustment strategy of household B is "turn off some non-essential electrical equipment after 8 pm".
[0130] Then, the energy consumption intention and electricity consumption adjustment strategy of each household are input into the corresponding household energy consumption prediction model to obtain the electricity consumption threshold value of each household.
[0131] It should be noted that the energy consumption intention and electricity consumption adjustment strategy can be obtained through the owner's input when the family joins the collaborative network. After obtaining the energy consumption intention and electricity consumption adjustment strategy corresponding to the family, they can be stored in the storage space of the cloud platform. In this way, when determining the electricity consumption threshold corresponding to the family, it can be obtained from the storage space.
[0132] Step 302: If so, determine a second family from other families in the collaborative network; the other families are families in the collaborative network other than the first family.
[0133] If the first family exists, then a second family that can be scheduled for power consumption can be determined from other families in the collaborative network, that is, the energy consumption of the second family is scheduled to be used by the first family. The other families in the collaborative network are families other than the first family.
[0134] In the embodiment of the present invention, determining the second home from other homes in the collaborative network includes:
[0135] Calculating a target power consumption required by the first household based on the current power consumption of the first household and a power consumption threshold;
[0136] Obtain the current power consumption and power consumption adjustment strategies corresponding to other households;
[0137] A second household that can dispatch the target power consumption within the preset time period is calculated based on each current power consumption and each power consumption adjustment strategy.
[0138] Specifically, after determining the first household, the cloud platform can further calculate the target electricity consumption required by the first household based on the first household's current electricity consumption and the electricity consumption threshold. That is to say, if the first household maintains its current electricity consumption and continues to use electricity with the target electricity consumption, it will not exceed the electricity consumption threshold.
[0139] After obtaining the target power consumption, the current power consumption and power consumption adjustment strategies corresponding to other households can be obtained, and then the other households can be screened according to the power consumption adjustment strategies, and the households that do not meet the power consumption scheduling can be filtered out. For example, the power consumption adjustment strategy of household B is "turn off some non-essential power equipment after 8 pm", and non-essential power equipment has been turned off, so household B cannot perform power consumption scheduling.
[0140] Then, the current power consumption and household energy consumption prediction model are used to determine the second household that can dispatch the target power consumption within the preset time period from the remaining households. For example, if household C (the first household) needs 10 kWh of electricity in the next hour to avoid exceeding the power consumption threshold, and after calculation, it is determined that household A can dispatch 10 kWh of electricity in the next hour, then household A can be selected as the second household.
[0141] It should be noted that the number of the second households can be one or more. For example, if household C needs 10 kWh of electricity, household A can dispatch 8 kWh of electricity, and household B can dispatch 2 kWh of electricity. In the embodiment of the present invention, only one second household is used for detailed description. In actual applications, the number of second households can be adjusted according to actual needs, and the embodiment of the present invention does not limit this.
[0142] Step 303: Send a first power consumption scheduling instruction to the second household, so that the second household responds to the first power consumption scheduling instruction and performs a corresponding power consumption adjustment action.
[0143] After the cloud platform determines the second family, it can send the first power consumption scheduling instruction to the second family. After the second family obtains the first power consumption scheduling instruction, it can execute the corresponding power consumption adjustment action. For example, family A can dispatch 10 kWh of electricity, and the power consumption adjustment strategy of family A is "automatic adjustment of air conditioning temperature". After calculation, it can be determined that the air conditioning temperature needs to be raised from 26°C to 28°C to dispatch 10 kWh of electricity. Then, the cloud platform can send a power consumption scheduling instruction of "raising the air conditioning temperature by 2°C" to family A. After obtaining the power consumption scheduling instruction, family A can raise the air conditioning temperature from 26°C to 28°C.
[0144] Step 304: after the preset time period ends, a second power consumption scheduling instruction is sent to the second household, so that the second household responds to the second power consumption scheduling instruction and cancels the power consumption adjustment action.
[0145] Specifically, after the preset time period ends, the cloud platform can send an instruction to the second family to cancel the power consumption adjustment action (recorded as "second power consumption scheduling instruction"). After the second family obtains the second power consumption scheduling instruction, it can cancel the power consumption adjustment action. For example, continuing with the previous example, after 1 hour, the cloud platform can send a power consumption scheduling instruction of "air conditioning temperature up or down 2℃" to family A. After obtaining the power consumption scheduling instruction, family A can adjust the air conditioning temperature from 28℃ to 26℃.
[0146] Step 305: generate a first adjustment suggestion for the electricity consumption adjustment strategy of the first household, and generate a second adjustment suggestion for the electricity consumption adjustment strategy of the second household, and send the first adjustment suggestion to the first household, and send the second adjustment suggestion to the second household.
[0147] After the cloud platform sends the second power consumption scheduling instruction to the second family, it can generate an adjustment suggestion for the power consumption adjustment strategy of the first family (recorded as "first adjustment suggestion"), and an adjustment suggestion for the power consumption adjustment strategy of the second family (recorded as "second adjustment suggestion"). For example, the first adjustment suggestion is "suggesting to appropriately reduce energy consumption intention to increase execution flexibility", and the second adjustment suggestion is "suggesting to appropriately relax the temperature adjustment range". Of course, in actual applications, the specific content of the adjustment suggestion can be set according to actual needs, and the embodiment of the present invention does not limit this.
[0148] Furthermore, after obtaining the corresponding adjustment suggestions, the owners of the first and second households may update or not update the corresponding power consumption adjustment strategies. If the owners update the power consumption adjustment strategies, the cloud platform may update the stored power consumption adjustment strategies to the updated power consumption adjustment strategies after obtaining the updated power consumption adjustment strategies.
[0149] In an embodiment of the present invention, the cloud platform detects whether there is a first family in the collaborative network whose electricity consumption reaches the electricity consumption threshold after a preset time period; if so, a second family is determined from other families in the collaborative network; the other families are families in the collaborative network other than the first family; a first electricity consumption scheduling instruction is sent to the second family, so that the second family responds to the first electricity consumption scheduling instruction and performs a corresponding electricity consumption adjustment action. In this way, when a family in the collaborative network has a sudden high energy consumption risk, the cloud platform can determine whether electricity consumption can be scheduled for other families in the collaborative network. If so, electricity consumption can be scheduled for other families, thereby timely avoiding high energy consumption risks, achieving real-time energy scheduling, and avoiding energy waste.
[0150] Furthermore, after a preset time period, the cloud platform can send a second power consumption scheduling instruction for canceling the power consumption adjustment action to the second family. After the second family obtains the second power consumption scheduling instruction, it can cancel the power consumption adjustment action. At the same time, the cloud platform can generate adjustment suggestions for the first family and the second family. If the first family and the second family update the power consumption adjustment strategy based on the adjustment suggestion, the cloud platform can update the stored power consumption adjustment strategy. In this way, the use of the updated power consumption adjustment strategy can more accurately perform energy scheduling, further avoiding energy waste.
[0151] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.
[0152] Reference Figure 4 , shows a structural block diagram of an energy scheduling device embodiment of the present invention, which may specifically include the following modules:
[0153] The detection module 401 is used to detect whether there is a first household in the collaborative network whose electricity consumption reaches the electricity consumption threshold after a preset time period;
[0154] A determination module 402 is configured to determine a second family from other families in the collaborative network, if any; the other families are families in the collaborative network other than the first family;
[0155] The sending module 403 is used to send the first power consumption scheduling instruction to the second household, so that the second household responds to the first power consumption scheduling instruction and performs a corresponding power consumption adjustment action.
[0156] In an embodiment of the present invention, the detection module includes:
[0157] An acquisition submodule is used to acquire the current power consumption of each household in the collaborative network at the current moment; each household has a one-to-one corresponding power consumption threshold and household energy consumption prediction model;
[0158] The prediction submodule is used to predict each household using the household energy consumption prediction model and each current power consumption, and detect whether there is a first household among the households whose power consumption reaches the corresponding power consumption threshold after a preset time period.
[0159] In an embodiment of the present invention, it also includes:
[0160] The acquisition submodule is further used to acquire the historical electricity consumption data corresponding to each household, as well as household-related information corresponding to each household;
[0161] The modeling submodule is used to use various historical electricity consumption data and relevant information of each household to build a model to obtain a household energy consumption prediction model corresponding to each household.
[0162] In the embodiment of the present invention, the acquisition submodule is specifically used for:
[0163] The historical electricity consumption data corresponding to each household is obtained through an electricity meter with communication function.
[0164] In an embodiment of the present invention, it also includes:
[0165] The acquisition submodule is also used to obtain the energy consumption intention and power consumption adjustment strategy corresponding to each household;
[0166] The generation submodule is used to generate the power consumption threshold corresponding to each household by adopting each energy consumption intention, each power consumption adjustment strategy and each household energy consumption prediction model.
[0167] In the embodiment of the present invention, the determining module is specifically used to:
[0168] Calculating a target power consumption required by the first household based on the current power consumption of the first household and a power consumption threshold;
[0169] Obtain the current power consumption and power consumption adjustment strategies corresponding to other households;
[0170] A second household that can dispatch the target power consumption within the preset time period is calculated based on each current power consumption and each power consumption adjustment strategy.
[0171] In an embodiment of the present invention, it also includes:
[0172] The sending module is further configured to send a second power consumption scheduling instruction to the second household after the preset time period ends, so that the second household responds to the second power consumption scheduling instruction and cancels the power consumption adjustment action;
[0173] A generation module is used to generate a first adjustment suggestion for the electricity consumption adjustment strategy of the first household, and to generate a second adjustment suggestion for the electricity consumption adjustment strategy of the second household, and to send the first adjustment suggestion to the first household, and to send the second adjustment suggestion to the second household.
[0174] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.
[0175] An embodiment of the present invention further provides an electronic device, including:
[0176] It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned energy scheduling method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.
[0177] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned energy scheduling method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.
[0178] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.
[0179] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
[0180] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0181] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0182] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0183] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.
[0184] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.
[0185] The above is a detailed introduction to an energy scheduling method and an energy scheduling device provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.
Claims
1. An energy scheduling method, characterized in that: The method comprises: Detecting whether there is a first household in the collaborative network whose electricity consumption reaches an electricity consumption threshold after a preset time period; If so, determining a second family from other families in the collaborative network; the other families are families in the collaborative network other than the first family; A first power consumption scheduling instruction is sent to the second household, so that the second household performs a corresponding power consumption adjustment action in response to the first power consumption scheduling instruction.
2. The energy scheduling method according to claim 1, characterized in that: The detecting whether there is a first household in the collaborative network whose electricity consumption reaches the electricity consumption threshold after a preset time period includes: Obtaining the current power consumption of each household in the collaborative network at the current moment; each household has a one-to-one corresponding power consumption threshold and household energy consumption prediction model; The energy consumption prediction model of each household and each current power consumption are used to make predictions for each household, and it is detected whether there is a first household among the households whose power consumption reaches a corresponding power consumption threshold after a preset time period.
3. The energy scheduling method according to claim 2, characterized in that: The energy consumption prediction model for each household is generated in the following way: Obtain historical electricity consumption data corresponding to each household, as well as household-related information corresponding to each household; The historical electricity consumption data and relevant information of each household are used to build a model to obtain a household energy consumption prediction model corresponding to each household.
4. The energy scheduling method according to claim 3, characterized in that: The obtaining of the historical electricity consumption data corresponding to each household includes: The historical electricity consumption data corresponding to each household is obtained through an electricity meter with communication function.
5. The energy scheduling method according to claim 2, characterized in that: The power consumption threshold corresponding to each household is obtained in the following way: Obtain the energy consumption intention and electricity consumption adjustment strategy corresponding to each household; Each energy consumption intention, each power consumption adjustment strategy and each household energy consumption prediction model are used to generate the corresponding power consumption threshold for each household.
6. The energy scheduling method according to claim 1, characterized in that: The determining the second family from other families in the collaborative network includes: Calculating a target power consumption required by the first household based on the current power consumption of the first household and a power consumption threshold; Obtain the current power consumption and power consumption adjustment strategies corresponding to other households; A second household that can dispatch the target power consumption within the preset time period is calculated based on each current power consumption and each power consumption adjustment strategy.
7. The energy scheduling method according to claim 1, characterized in that: Also includes: After the preset time period ends, sending a second power consumption scheduling instruction to the second household, so that the second household responds to the second power consumption scheduling instruction and cancels the power consumption adjustment action; Generate a first adjustment suggestion for the electricity consumption adjustment strategy of the first household, and generate a second adjustment suggestion for the electricity consumption adjustment strategy of the second household, and send the first adjustment suggestion to the first household, and send the second adjustment suggestion to the second household.
8. An energy dispatching device, characterized in that: The device comprises: A detection module, used to detect whether there is a first household in the collaborative network whose electricity consumption reaches an electricity consumption threshold after a preset time period; a determination module, configured to determine a second family from other families in the collaborative network, if any; the other families are families in the collaborative network other than the first family; The sending module is used to send the first power consumption scheduling instruction to the second household, so that the second household responds to the first power consumption scheduling instruction and performs a corresponding power consumption adjustment action.
9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the energy scheduling method according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the energy scheduling method according to any one of claims 1 to 7 are implemented.