Method, device, equipment, medium and program product for adjusting electric load data

Abstract

The present invention relates to the technical field of data processing, and provides a method, device, equipment, medium and program product for adjusting power load data. The method includes: predicting the turning-on information of an electrical device in a first future time period based on a data set of the electrical device; determining the set temperature of the electrical device in a second future time period based on the turning-on information; determining the power load baseline of the electrical device in the first future time period based on the historical power load data of the electrical device; selecting the power load baseline of the electrical device in the second future time period from the power load baseline of the electrical device in the first future time period; and adjusting the power load data of the electrical device based on the difference between the power load baseline of the electrical device in the second future time period and the power load corresponding to the set temperature. The present invention fully considers the user's electricity consumption demand in the process of predicting adjustable load, and realizes the adjustment of power load data on the basis of ensuring the user's use experience.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method, device, equipment, medium and program product for adjusting power load data. Background Art

[0002] At present, more and more electrical equipment has the function of electricity metering, and can record and report the power load data during work at any time, which makes it possible to control electrical equipment to meet the peak shaving demand of the power grid.

[0003] Different from the relatively stable load of industrial and commercial machinery operation, the power load of household appliances is not only affected by objective factors such as weather, but also intervened by user behavior habits. The power load of traditional household appliances is directly adjusted based on the prediction results. However, if the adjustment is forcibly carried out during the time period when the user needs to use the equipment, it will have a serious negative impact on the user experience. Therefore, how to adjust the power load data on the basis of ensuring the user experience has become an urgent problem to be solved. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. For this purpose, the present invention proposes a method for adjusting power load data, which realizes the adjustment of power load data on the basis of ensuring the user experience by fully considering the user's electricity consumption demand in the process of estimating the adjustable load.

[0005] The present invention also proposes an adjustment device for power load data, an electronic device, a non-transitory computer-readable storage medium and a computer program product.

[0006] According to an embodiment of the first aspect of the present invention, a method for adjusting power load data includes:

[0007] Based on a data set of an electrical device, predicting the turn-on information of the electrical device in a first future time period; the data set includes the historical usage data of the electrical device;

[0008] Based on the turn-on information of the electrical device in the first future time period, determining the set temperature of the electrical device in a second future time period; the second time period is the time period when the electrical device is turned on in the first time period; the set temperature is the temperature parameter set by the user when using the electrical device;

[0009] Based on the historical power load data of the electrical device, determining the power load baseline of the electrical device in the first future time period;

[0010] Selecting the power load baseline of the electrical device in the second future time period from the power load baseline of the electrical device in the first future time period;

[0011] Adjust the power load data of the electrical equipment based on the difference between the power load baseline of the electrical equipment in the future second time period and the power load corresponding to the set temperature.

[0012] According to an embodiment of the present invention, the set temperature includes a maximum set temperature and a minimum set temperature; the adjusting the power load data of the electrical equipment based on the difference between the power load baseline of the electrical equipment in the future second time period and the power load corresponding to the set temperature includes:

[0013] Determine a first difference between the power load baseline of the electrical equipment in the future second time period and the power load corresponding to the maximum set temperature, and a second difference between the power load baseline of the electrical equipment in the future second time period and the power load corresponding to the minimum set temperature;

[0014] When the first difference is 0, use the second difference as the load adjustment value;

[0015] When the second difference is 0, use 0 as the load adjustment value;

[0016] When the first difference is greater than 0 and the second difference is greater than 0, determine the load adjustment value based on the first difference and the second difference;

[0017] Adjust the power load data of the electrical equipment based on the load adjustment value.

[0018] According to an embodiment of the present invention, the adjusting the power load data of the electrical equipment based on the load adjustment value includes:

[0019] Determine the adjustment priority of the electrical equipment based on the load adjustment value and the weight of the electrical equipment;

[0020] When the load to be adjusted is less than the load adjustment upper limit value, select at least one target electrical equipment based on the adjustment priority of the electrical equipment; the load adjustment upper limit value is the total load adjustment value in the future first time period;

[0021] Adjust the power load data of the target electrical equipment based on the load adjustment value.

[0022] According to an embodiment of the present invention, the determining the power load baseline of the electrical equipment in the future first time period based on the historical power load data of the electrical equipment includes:

[0023] When the adjustment time of the power load data is a legal working day, based on the historical power load data of multiple historical legal working days, determine the power load baseline of the electrical equipment in the future first time period; the power consumption loads in the first time period of the historical legal working days all conform to the power consumption loads corresponding to the user's power consumption behavior habits.

[0024] When the adjustment time of the power load data is a non-legal working day, based on the historical power load data of multiple consecutive historical non-legal working days, determine the power load baseline of the electrical equipment in the future first time period.

[0025] According to an embodiment of the present invention, the predicting the turning-on information of the electrical equipment in the future first time period based on the data set of the electrical equipment includes:

[0026] Extract feature data from the historical usage data in the data set, and perform cross-combinations of different features on the feature data.

[0027] Extract effective information from the cross-combined features; the effective information is a feature or a combination of features that is relevant to whether the electrical equipment is turned on.

[0028] Based on the effective information, predict the turning-on information of the electrical equipment in the future first time period.

[0029] According to an embodiment of the present invention, the determining the set temperature of the electrical equipment in the future second time period based on the turning-on information of the electrical equipment in the future first time period includes:

[0030] Based on the turning-on information of the electrical equipment in the future first time period, determine the second time period.

[0031] Extract the feature data of the second time period from the data set.

[0032] Based on the feature data of the second time period, determine the set temperature of the electrical equipment in the future second time period.

[0033] According to an embodiment of the second aspect of the present invention, an adjustment device for power load data includes:

[0034] A prediction module, configured to predict the turning-on information of the electrical equipment in the future first time period based on a data set of the electrical equipment; the data set includes the historical usage data of the electrical equipment.

[0035] A set temperature determination module is configured to determine a set temperature of the electrical equipment in a future second time period based on the turn-on information of the electrical equipment in a future first time period; the second time period is the time period when the electrical equipment is turned on within the first time period; the set temperature is the temperature parameter set by the user when using the electrical equipment.

[0036] A power load baseline determination module is configured to determine a power load baseline of the electrical equipment in a future first time period based on the historical power load data of the electrical equipment.

[0037] A selection module is configured to select a power load baseline of the electrical equipment in a future second time period from the power load baselines of the electrical equipment in a future first time period.

[0038] An adjustment module is configured to adjust the power load data of the electrical equipment based on the difference between the power load baseline of the electrical equipment in a future second time period and the power load corresponding to the set temperature.

[0039] An electronic device according to an embodiment of the third aspect of the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method for adjusting the power load data as described in any one of the above.

[0040] A non-transitory computer-readable storage medium according to an embodiment of the fourth aspect of the present invention stores a computer program thereon. When the computer program is executed by a processor, it implements the method for adjusting the power load data as described in any one of the above.

[0041] A computer program product according to an embodiment of the fifth aspect of the present invention includes a computer program. When the computer program is executed by a processor, it implements the method for adjusting the power load data as described in any one of the above.

[0042] One or more of the above technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0043] Fully consider the user's electricity consumption needs during the process of estimating the adjustable load, and realize the adjustment of the power load data on the basis of ensuring the user experience.

[0044] Effectively improve the success rate of C-end users participating in the virtual power plant's electricity peak shaving task, and can effectively reduce the user interruption behavior during the execution of the virtual power plant's electricity peak shaving task.

[0045] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. Description of the Drawings

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 It is a schematic flowchart of the method for adjusting power load data provided by the embodiments of the present invention.

[0048] Figure 2 It is a schematic structural diagram of the DNN deep learning model provided by the embodiments of the present invention.

[0049] Figure 3 It is a schematic structural diagram of the MMOE multi-task model provided by the embodiments of the present invention.

[0050] Figure 4 It is a schematic structural diagram of the device for adjusting power load data provided by the embodiments of the present invention.

[0051] Figure 5 It is a schematic structural diagram of the electronic device provided by the embodiments of the present invention. Specific Embodiments

[0052] The following will further describe in detail the embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0053] In the description of the embodiments of the present invention, it should be noted that the terms "first", "second", and "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0054] In the embodiments of the present invention, unless otherwise clearly specified and limited, the first feature being "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "below" and "beneath" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.

[0055] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0056] In related technologies, virtual power plants are a control technology based on dynamic power demand response, specifically an Internet of Things (IoT) technology that coordinates and controls distributed energy, controllable loads, and energy storage in a unified manner to respond to grid dispatch. At present, power demand response based on household appliances is still vacant. Residential power demand response has unique advantages, mainly including: (1) Residential smart appliances have a large-scale stock, and smart appliances can be monitored and controlled in the cloud, with considerable control potential; (2) Residential appliances are widely distributed and highly flexible, and can dynamically respond to power control needs in different provinces or regions; (3) Some categories of appliances have energy storage characteristics (such as electric water heaters), which can store energy during low power consumption, completely staggered from peak power consumption, and have strong control capabilities.

[0057] During the execution of the virtual power plant peak shaving task, users are usually required to keep the electrical appliances working in a low-power state for a period of time, or even turn them off for a period of time. However, it is difficult for many users to keep the electrical appliances working in a low-power state or turn them off for an hour. Users often restart the high-power function of the electrical appliances in the middle of the virtual power plant peak shaving task, thereby interrupting the peak shaving task. This will cause the actual adjustable load of electricity to be much lower than the estimated virtual power plant adjustable load in advance, resulting in the failure of the virtual power plant peak shaving task for the electrical appliance. In addition, if the device is forcibly adjusted during the time period when the user needs to use the device, it will have a serious negative impact on the user experience.

[0058] Based on the above problems, the present invention proposes a method for adjusting power load data. The method determines in advance whether the user has the need to use the product within the time period of responding to the virtual power plant control task. If so, the product power load is adjusted downward on the basis of ensuring the user experience as much as possible.

[0059] Figure 1 1 is a flow chart of a method for adjusting power load data provided by an embodiment of the present invention. Figure 1, an embodiment of the present invention provides a method for adjusting power load data, including:

[0060] Step 101, based on the dataset of the electrical equipment, predict the turn-on information of the electrical equipment in the first time period in the future.

[0061] It should be noted that the execution subject of the power load data adjustment method provided by the embodiment of the present invention can be a server or a computer device, such as a smart phone, a tablet computer, a notebook computer, a vehicle-mounted electronic device, a wearable device, etc.

[0062] Collect the historical usage data and historical weather data corresponding to the electrical equipment in each historical usage time period, and then construct a dataset of the electrical equipment based on the collected data. Among them, the historical usage time period can refer to different time periods within a day, such as 8:00 - 9:00, 10:00 - 11:00, 12:00 - 13:00; it can also refer to different time periods within a week, such as Monday, Tuesday, Wednesday, etc. The electrical equipment can be household equipment, such as an electric water heater, an air conditioner, an electric heater, etc.

[0063] For example, integrate historical date data (including whether it is a weekend, a holiday, etc.), historical weather data (including indoor and outdoor temperatures, etc.), and the working state information of electrical equipment such as electric water heaters, air conditioners, and electric heaters. Among them, the working state information can include information such as working mode, working gear, continuous working time, and power load.

[0064] 1) Select n electrical equipment (including electric water heaters, air conditioners, and electric heaters) in a certain province, collect the historical usage data reported by the n electrical equipment in the recent year, obtain the identification information (such as device id), device type, city identification information (such as city id) of the city where the equipment is located, and the data reporting date based on the historical usage data, and count the working mode, working gear size, hourly power load, and indoor temperature of the electrical equipment every hour of each day. The processed data is as follows in the table:

[0065]

[0066] 2) According to the historical weather data record, process the weather data of the area where the electrical equipment is located in the recent year to generate the following data table:

[0067]

[0068] 3) Organize the historical date data of the electrical equipment in the recent year to generate the following data table:

[0069]

[0070] Among them, the demand response day refers to the day when the electricity peak shaving demand of the power grid is received and successfully responded to. On that day, because the electrical equipment has implemented the electricity peak shaving regulation, the power load data needs to be different from the power load data on normal working days or holidays.

[0071] 4) Integrate and generate features from the data generated in steps 1) to 3) to generate the following data table:

[0072]

[0073] Based on the historical usage data and historical weather data corresponding to each historical usage period of the electrical equipment integrated in the above table, construct a dataset of the electrical equipment.

[0074] After constructing the dataset of the electrical equipment, based on this dataset, predict the startup information of the electrical equipment in the first future period. Among them, the first period can refer to different periods within a day, such as 8:00 - 9:00, 10:00 - 11:00, 12:00 - 13:00; it can also refer to different periods within a week, such as Monday, Tuesday, Wednesday, etc. The startup information can include the switch state of the equipment, such as whether the equipment is in the on state. It can be understood that for different types of electrical equipment, the definition of startup is different. For example, starting an electric water heater means using hot water; starting an air conditioner means turning on the cooling or heating mode; starting an electric heater means turning on the electric heater for heating.

[0075] For example, the deep learning method can be used to predict whether the user will start the electrical equipment in each hour period of the next day.

[0076] Step 102, based on the startup information of the electrical equipment in the first future period, determine the set temperature of the electrical equipment in the second future period.

[0077] After predicting the startup information of the electrical equipment in the first future period, take the period when the electrical equipment is started in the first period as the second period, and then determine the set temperature of the electrical equipment in the second future period. Among them, the number of the second periods can be one or more; the set temperature can include the maximum set temperature and the minimum set temperature , where is the id of different electrical equipment, is the hour period from 0 to 23.

[0078] For example, the deep learning method can be used to predict the maximum set temperature and the minimum set temperature that the user can accept in the second period of the next day.

[0079] Step 103: Determine the power load baseline of the electrical device for the future first time period based on the historical power load data of the electrical device.

[0080] Step 104: Select the power load baseline of the electrical device for the future second time period from the power load baseline of the electrical device for the future first time period.

[0081] Step 105: Adjust the power load data of the electrical device based on the difference between the power load baseline of the electrical device for the future second time period and the power load corresponding to the set temperature.

[0082] In order to estimate whether the electrical device can adjust the power consumption per hour in the next day, it is necessary to obtain the reference value or baseline of the power consumption per hour of the electrical device according to the historical hourly power consumption records of the electrical device, that is, the power load baseline. , where is base, is the id of different electrical devices, is the hourly period from 0 to 23; further, based on the power load baseline, estimate the power load that the electrical device can adjust downward, so as to judge whether the electrical device can respond to the power consumption peak shaving task. Among them, the power consumption peak shaving task refers to reducing the power demand by adjusting the load of electrical devices during the peak power demand period, so as to balance the power supply and demand, avoid grid overload or frequent power supply shortages.

[0083] Based on the historical power load data of the electrical device, determine the power load baseline of the electrical device for the future first time period. Specifically, through long-term power consumption records, the power consumption patterns of the electrical device in different time periods (such as different hours of each day, different seasons, etc.) can be obtained. For example, an air conditioner may consume more power during the daytime in summer, while less power at night or in winter. The power load baseline reflects the normal power consumption during these time periods. Among them, the power load baseline may vary with factors such as time, season, weather, and usage habits. For example, the power load baseline may be higher during the winter heating period than when using the air conditioner in summer because the load of electrical devices (such as electric heaters) is larger in winter.

[0084] The historical power load data can be historical power consumption data, which is used to reflect the actual power consumption of the electrical device in a certain period in the past. The historical power load data is obtained through long-term monitoring and recording of power consumption, and can include the power consumption within the time period, such as the power consumption per hour, per day, and per month; the load change situation, such as the fluctuation of the power load, which reflects the power demand of the device or system in different time periods; the power consumption pattern, such as seasonal changes, the difference in power consumption between day and night, etc.

[0085] In one embodiment, the power load baseline is determined as follows: when the adjustment time of the power load data is a legal working day, based on the historical power load data of multiple historical legal working days, determine the power load baseline of the electrical equipment in the first future time period; when the adjustment time of the power load data is a non-legal working day, based on the historical power load data of multiple consecutive historical non-legal working days, determine the power load baseline of the electrical equipment in the first future time period. Among them, the power consumption load in the first time period (such as each hour period) of the historical legal working days all conforms to the power consumption load corresponding to the user's power consumption behavior habits; the adjustment time of the power load data can be the peak shaving task response day.

[0086] For example, for all selected electrical equipment, screen the actual power load data of these electrical equipment every hour every day for the past 45 days; if it is less than 45 days, use all historical power load data for the calculation of the power load baseline. Calculate for each electrical equipment. When the peak shaving task response day is a legal working day (that is, when the future day is a legal working day), select the first 5 normal historical legal working days of the response day to form a set of reference days for the load baseline. Among them, 5 legal working days form a complete working week, and the user's habit of using electrical equipment mainly takes one working week as a behavior cycle. Then, calculate the power consumption load Pavi (unit: W) of the electrical equipment in each response period (such as each hour) of each reference day, and calculate the average load Pav (W) of the 5 reference days in each response period. If any Pavi < 0.75×Pav, it is determined that the power consumption load value of this hour on this reference day is an abnormal value that does not conform to the user's behavior habits, and the power consumption load value of this hour on this reference day is removed from the reference day set; at the same time, recursively select the power consumption load data of other historical legal working days in turn until the power consumption load data of 5 reference days that meet the requirements are selected; the forward recursion is limited to 45 days. If the legal working day 45 days ago still cannot select 5 reference days, the nearest holiday or the day of responding to the power consumption peak shaving task is also regarded as a legal working day to make up 5 days of reference days. After the reference days are selected, calculate the average value of the power load values of the reference days, and use this average value of the power load values as the power load baseline of this electrical equipment in each hour. When the peak shaving task response day is a non-legal working day, select the first 3 non-legal working days (such as Saturday) of the response day as the reference days for calculating the baseline load (that is, refer to the situation of the user using electrical equipment on 3 non-legal working days). If 3 non-legal working days cannot be selected, the nearest holiday or the response day is also regarded as a non-legal working day to make up 3 days of reference days. Among them, the calculation method of the power load baseline for non-legal working days is the same as that for legal working days, that is, calculate the average value of the power load values of the reference days, and use this average value of the power load values as the power load baseline of this electrical equipment in each hour.

[0087] In the embodiments of the present invention, by determining the power load baseline, a prediction benchmark for future power demand is provided, power dispatching and load management are optimized, and at the same time, data support is provided for grid regulation measures such as demand response and peak shaving and valley filling.

[0088] Further, from the power load baseline of the electrical equipment in the first future time period, the power load baseline of the electrical equipment in the second future time period is selected. For example, if the air conditioner is turned on from 7:00 to 8:00 in the future day, the load baseline data for the period from 7:00 to 8:00 needs to be extracted.

[0089] Further, based on the difference between the power load baseline of the electrical equipment in the second future time period and the power load corresponding to the set temperature, the power load data of the electrical equipment is adjusted.

[0090] In one embodiment, a first difference between the power load baseline of the electrical equipment in the second future time period and the power load corresponding to the maximum set temperature is determined, and a second difference between the power load baseline of the electrical equipment in the second future time period and the power load corresponding to the minimum set temperature is determined; when the first difference is 0, the second difference is used as the load adjustment value; when the second difference is 0, 0 is used as the load adjustment value; when the first difference is greater than 0 and the second difference is greater than 0, the load adjustment value is determined based on the first difference and the second difference; based on the load adjustment value, the power load data of the electrical equipment is adjusted.

[0091] For example, for the turned-on electrical equipment, based on the power load baseline of each hour of the electrical equipment, the differences between the power loads corresponding to the highest set temperature and the lowest set temperature and the baseline are calculated respectively, and the equipment pool that can perform the virtual power plant power peak shaving task is determined based on the differences.

[0092] (1) Calculate the power load baseline of each electrical equipment for each hour period of the future day Subtract the power load corresponding to the predicted output of the highest set temperature The first difference is used as the minimum adjustable load capacity, denoted as , where is the different electrical equipment id,

[0093] ;

[0094] (2) Calculate the power load baseline of each electrical equipment for each hour period of the future day Subtract the power load corresponding to the predicted output of the lowest set temperature The second difference is used as the maximum adjustable load capacity, denoted as :

[0095] ;

[0096] (3) Based on and , calculate the adjustable load value (i.e., the load adjustment value ) corresponding to each moment for the predicted electricity-consuming equipment to be turned on. The calculation method is as follows:

[0097] ;

[0098] That is, when the second difference is 0, take 0 as the load adjustment value ; when the first difference is 0, take the second difference as the load adjustment value ; when the first difference is greater than 0 and the second difference is greater than 0 (i.e., ), take as the load adjustment value .

[0099] In one embodiment, if the prediction result is that the electricity-consuming equipment is not turned on, the theoretical electricity load corresponding to the non-activated electricity-consuming equipment is 0, and the adjustable load value (i.e., the load adjustment value) is the power load baseline at the corresponding moment . However, considering the user experience and avoiding directly turning off the electricity load to 0 during the peak shaving regulation of the virtual power plant, in order to reserve a certain adjustment space, a method of proportionally reducing based on the power load baseline can be adopted when estimating the adjustable electricity load value. The calculation formula is as follows:

[0100] ;

[0101] where is the load adjustment value for the hour period corresponding to the electricity-consuming equipment; this calculation method uses the sigmoid function to convert the power load baseline into a proportional value between 0 and 1. Among them, the larger it is, the larger the adjustable proportion is, and the smaller it is, the smaller the adjustable proportion is. This calculation method meets the requirement that when predicting that the user does not turn on the electricity-consuming equipment, a certain adjustment space is reserved, and the adjustable electricity load value of each equipment can be estimated to the greatest extent according to the power load baseline of different electricity-consuming equipment.

[0102] In one embodiment, adjusting the power load data of an electrical device based on a load adjustment value includes: determining the adjustment priority of the electrical device based on the load adjustment value and the weight of the electrical device; when the load value to be adjusted is less than the load adjustment upper limit value, selecting at least one target electrical device based on the adjustment priority of the electrical device; the load adjustment upper limit value is the total load adjustment value within the first time period in the future; adjusting the power load data of the target electrical device based on the load adjustment value.

[0103] Specifically, if , then the electrical device enters the adjustable device pool and As the adjustable load value, the lowest set temperature predicted to be output is used as the adjustable temperature of the electrical device; for a device predicted not to be turned on, the standby state is set as the adjustable temperature; if , then the electrical device does not enter the adjustable device pool. The sum of the adjustable loads of all devices entering the adjustable device pool is the upper limit of the adjustable load capacity that the virtual power plant can respond to for each hour period in the future, that is, the load adjustment upper limit value :

[0104] ;

[0105] Among them, is the number of devices in the adjustable device pool.

[0106] Further, determining the adjustment priority of the electrical device based on the load adjustment value and the weight of the electrical device, where the adjustment priority is determined based on the weighted integral of the electrical device, the higher the weighted integral, the higher the adjustment priority; conversely, the lower the adjustment priority. For example, calculate the weighted integral of the devices entering the adjustable load device pool for each hour period :

[0107] ;

[0108] Among them, is the weight value of different types of electrical devices. For example, the electric water heater category responds first, followed by air conditioners and electric heaters. Therefore, the weight values of electric water heaters, air conditioners, and electric heaters decrease in turn, and are respectively , , ( The determination of the weight value needs to satisfy the sum equal to 1 and be an arithmetic progression).

[0109] When the load value to be adjusted is less than the load adjustment upper limit value, selecting at least one target electrical device based on the adjustment priority of the electrical device; then, adjusting the power load data of the target electrical device based on the load adjustment value. For example, according to the sorting score Sort the adjustable devices in each hourly period in descending order. When a virtual power plant peak shaving regulation task needs to be executed in the next day and the load value to be regulated in the virtual power plant peak shaving regulation task is less than the load regulation upper limit value, preferentially select the adjustable devices ranked in the front to issue commands for regulation in the cloud. When the situation occurs, preferentially select the adjustable devices ranked in the front to issue commands for regulation in the cloud.

[0110] Through scientifically managing and regulating the load of electrical equipment, as well as calculating and screening adjustable devices, the embodiment of the present invention can not only enable the virtual power plant to efficiently perform peak shaving regulation to ensure the stable operation of the power grid during peak electricity consumption, but also optimize the operation state of electrical equipment, extend the service life of the equipment, and at the same time promote the reasonable allocation and utilization of electric power resources, realizing the supply-demand balance and efficient operation of the power system.

[0111] The method for regulating power load data provided by the embodiment of the present invention predicts the turn-on information of electrical equipment in the next first time period based on a data set of electrical equipment. The data set includes historical usage data of the electrical equipment; based on the turn-on information of the electrical equipment in the next first time period, determine the set temperature of the electrical equipment in the next second time period; the second time period is the time period when the electrical equipment is turned on in the first time period; based on the historical power load data of the electrical equipment, determine the power load baseline of the electrical equipment in the next first time period; select the power load baseline of the electrical equipment in the next second time period from the power load baseline of the electrical equipment in the next first time period; based on the difference between the power load baseline of the electrical equipment in the next second time period and the power load corresponding to the set temperature, regulate the power load data of the electrical equipment. The present invention fully considers the electricity consumption needs of users in the process of estimating adjustable load, and realizes the regulation of power load data on the basis of ensuring the user experience.

[0112] Based on the above embodiment, the predicting the turn-on information of the electrical equipment in the next first time period based on the data set of the electrical equipment includes:

[0113] Step 1010, extract feature data from the historical usage data in the data set, and perform cross-combinations of different features on the feature data;

[0114] Step 1011, extract effective information from the cross-combined features; the effective information is a feature or a combination of features that is relevant to whether the electrical equipment is turned on;

[0115] Step 1012, based on the effective information, predict the turn-on information of the electrical equipment in the next first time period.

[0116] The deep learning approach can be adopted to predict whether the user will turn on the electrical equipment in each hour period of the next day. For example, models such as Deep Neural Network (DNN), decision tree model, Gradient Boosting Machines (GBM) model, Convolutional Neural Networks (CNN) model, and Long Short-Term Memory (LSTM) model can be used to predict whether the user will turn on the electrical equipment in each hour period of the next day.

[0117] In one embodiment, taking the DNN model as an example for analysis and explanation, refer to Figure 2 , the DNN model includes:

[0118] Input layer: The input data is divided into three categories, namely Continuous Features (continuous features), including user behavior and environmental information; Categorical Features (categorical features), including device information. These features are converted into Embeddings (embedded vectors) through their respective embedding layers.

[0119] Middle layer (hidden layer - hidden layers): The embedded vectors of continuous features and categorical features are concatenated in the middle layer (Concatenated Embeddings). These concatenated embedded vectors are processed through multiple hidden layers.

[0120] Output layer (Output targets): After being processed by the hidden layer, it is finally output to the target layer.

[0121] Among them, Embeddings (embedded vectors) is a technology that converts discrete categorical variables (such as categories, labels, etc.) into continuous vectors. Concatenated Embeddings (concatenated embedded vectors), when there are multiple embedded vectors from different sources, they are concatenated in a certain dimension to form a new vector, which contains the information of all the original embedded vectors and is convenient for subsequent neural network layers to process. Hidden Layers (hidden layers) are the layers located between the input layer and the output layer in a deep neural network. These layers extract high-level features from the input data through non-linear transformations, thus helping the model to make better predictions and classifications.

[0122] Specifically, the feature data is input into the DNN model. The DNN model can automatically perform cross - combinations of different features, and then extract effective information from the cross - combined features. For example, the combined influence of indoor temperature changes and the continuous working duration of the device on the change of the device working mode can be expressed through the feature cross - combination of the DNN model. In the DNN model, to reduce the data sparsity problem, after the Embedding process on discrete variables, they are combined with continuous variables to form the feature data input into the model, and finally, it outputs a prediction of whether the user will turn on the electrical equipment at each moment within 24 hours of the next day, including behaviors such as the electric water heater heating, air - conditioner cooling, air - conditioner heating, and electric heater heating.

[0123] By performing cross - combinations of different features on the feature data, the embodiments of the present invention can explore potential relationships, and can more accurately predict whether the electrical equipment is turned on, thereby improving the accuracy of the prediction.

[0124] Based on the above - mentioned embodiments, determining the set temperature of the electrical equipment in the future second time period based on the turning - on information of the electrical equipment in the future first time period includes:

[0125] Step 1020, determining the second time period based on the turning - on information of the electrical equipment in the future first time period;

[0126] Step 1021, extracting the feature data of the second time period from the data set;

[0127] Step 1022, determining the set temperature of the electrical equipment in the future second time period based on the feature data of the second time period.

[0128] After predicting the turning - on information of the electrical equipment in the future first time period, the time period when the electrical equipment is turned on in the first time period is used as the second time period. Then, the feature data of the second time period is extracted from the data set. Finally, based on the feature data of the second time period, the maximum set temperature and the minimum set temperature that the user can accept within the second time period of the next day can be predicted by means of deep learning. For example, models such as the Multi - gate Mixture - of - Experts (MMOE) model, Reinforcement Learning (RL) model, Deep Reinforcement Learning (DRL) model, and Transfer Learning model can be used to predict the maximum set temperature and the minimum set temperature that the user can accept within the second time period of the next day.

[0129] In one embodiment, taking the MMOE model as an example for analysis and explanation, refer toFigure 3 , the MMOE model includes:

[0130] Input layer (Input): The bottom layer of the model is the input layer, which receives input data.

[0131] Middle layer (Experts and Gates): Experts: There is a group of "expert" modules in the middle, including Expert Module 1 (Expert 0), Expert Module 2 (Expert 1), and Expert Module 3 (Expert 2). These modules are the core computing units of the model and are responsible for processing input data. Gates: There are two gate modules on both sides of the expert modules, including Gate Module A (Gate A) and Gate Module B (Gate B). The gate modules are used to select and combine the outputs of the expert modules.

[0132] Output layer (Output): Output A (Output A) and Output B (Output B): The two outputs of the model correspond to different tasks respectively. These outputs further process the outputs of the gate modules and expert modules through tower-like structures (Tower A and Tower B).

[0133] Data flow: The input data first enters the input layer (Input), and then the input data is distributed to the middle expert modules for processing. The gate modules select and combine the outputs of the expert modules according to the input data and task requirements. For example, Gate A may select the outputs of Expert 0 and Expert 1, while Gate B may select the outputs of Expert 1 and Expert 2. The outputs of the gate modules enter the corresponding tower-like structures (Tower A and Tower B) respectively. The tower-like structures further process the data and finally generate two different outputs (Output A and Output B), which correspond to different tasks respectively. For example, Output A may be used to predict the "extremely cold value" (i.e., the minimum set temperature), while Output B may be used to predict the "extremely hot value" (i.e., the maximum set temperature).

[0134] For users who are predicted to turn on electric water heaters, air conditioners, and electric heaters, filter out the corresponding feature data of these users, and use the maximum set temperature and minimum set temperature of the electrical equipment used by the user at each moment as the learning objectives of the model; input the feature data into the MMOE model to predict the maximum set temperature and minimum set temperature that the user can accept in each hour period of the next day, so as to determine the adjustable comfort interval of the user. The MMOE multi-task model can simultaneously process multiple relatively independent learning objectives, avoiding the problem of insufficient sample data volume for a certain task on the one hand, and being able to avoid repeated calculations and reduce the resource consumption of training multiple models.

[0135] In the embodiment of the present invention, by predicting the maximum set temperature and the minimum set temperature that an electrical device can accept in each hour period of the next day, the user's electricity consumption demand is fully considered in the process of estimating the adjustable load, so as to adjust the power load data on the basis of ensuring the user experience.

[0136] In order to further analyze and explain the method for adjusting the power load data proposed by the present invention, the following embodiments are referred to.

[0137] The embodiment of the present invention mainly realizes the adjustment of the power load data through the following steps:

[0138] Step 1: Update the working state information of products such as water heaters, air conditioners, and electric heaters, as well as weather information, in the HIVE (Hadoop Integrated Virtual Environment) database every day. For example, update data such as whether the air conditioner device a is in the on state, the continuous working duration, the average working wind speed, the maximum temperature, the minimum temperature, the average working power, and the weather information of the current day for each hour of the latest day.

[0139] Step 2: Obtain data from the HIVE database and perform feature processing, that is, process the historical working state data of the above-mentioned air conditioner device a into required feature data, and then store the processed feature data in the python cluster for convenient subsequent data reading.

[0140] Step 3: Input the processed feature data into the DNN model as shown in Figure 2 to predict whether each type of product will be turned on in each hour of the next day; input the device data with the prediction result of being turned on into the MMOE model as shown in Figure 3 to predict the set temperature of each type of product in each hour corresponding to the turned-on device of the next day, and then calculate the power load corresponding to the lowest set temperature and the power load corresponding to the highest set temperature. For example, it is predicted that the above-mentioned air conditioner device a will turn on the cooling function at 3 pm of the next day, and it is predicted that the maximum set temperature in the time period from 3 pm to 4 pm is 27 °C (the power load corresponding to maintaining 27 °C can be 700 W), and the minimum set temperature is 22 °C (the power load corresponding to maintaining 22 °C can be 1200 W).

[0141] Step 4: Calculate the power load baseline of the electrical equipment for each hour of the next day, and calculate the adjustable load value of the electrical equipment for each hour of the next day. The electrical equipment that meets the requirements enters the adjustable equipment pool and waits to perform the specific virtual power plant peak shaving task for the next day. At the same time, calculate the upper limit of the total adjustable load capacity for each hour of the next day. For example, if the power load baseline of the above-mentioned air conditioner equipment a at 3 pm is 1000W, it is estimated that if the air conditioner equipment a maintains a working temperature of 27 degrees Celsius from 3 pm to 4 pm the next day, the adjustable load is 1000W - 700W = 300W. The air conditioner equipment a is included in the adjustable equipment pool, and the upper limit of the adjustable load capacity of the virtual power plant at 3 pm the next day increases by 300W.

[0142] Step 5: Wait for the grid system to issue a virtual power plant peak shaving task. First, judge whether it has the ability to meet the grid peak shaving demand for the corresponding hour period. If it can, accept the virtual power plant task issued by the grid system and select the corresponding equipment from the adjustable equipment pool, and reduce the power consumption of the target equipment by changing the set temperature or heating / cooling state of the equipment until the virtual power plant task is completed.

[0143] In the embodiment of the present invention, through the deep learning algorithm, the habit rules of users using the high-power functions of electrical equipment are learned. During the process of estimating the adjustable load of the virtual power plant, the possible power consumption demands of users are fully considered. On the basis of trying to maintain the user experience, the adjustable load of the virtual power plant is recalculated, which can effectively improve the success rate of C-end users participating in the virtual power plant peak shaving task and can effectively reduce the user interruption behavior during the execution of the virtual power plant peak shaving task.

[0144] Next, the power load data adjustment device provided by the embodiment of the present invention will be described. The power load data adjustment device described below can be mutually corresponding and referred to the power load data adjustment method described above.

[0145] Reference Figure 4 The power load data adjustment device provided by the embodiment of the present invention includes a prediction module 401, a set temperature determination module 402, a power load baseline determination module 403, a selection module 404, and an adjustment module 405.

[0146] The prediction module 401 is used to predict the turn-on information of the electrical equipment in the first future time period based on the data set of the electrical equipment; the data set includes the historical usage data of the electrical equipment;

[0147] A set temperature determination module 402 is configured to determine a set temperature of the electrical device in a future second time period based on the turn-on information of the electrical device in the future first time period; the second time period is the time period when the electrical device is turned on in the first time period; the set temperature is the temperature parameter set by the user when using the electrical device;

[0148] A power load baseline determination module 403 is configured to determine a power load baseline of the electrical device in the future first time period based on the historical power load data of the electrical device;

[0149] A selection module 404 is configured to select a power load baseline of the electrical device in the future second time period from the power load baselines of the electrical device in the future first time period;

[0150] An adjustment module 405 is configured to adjust the power load data of the electrical device based on the difference between the power load baseline of the electrical device in the future second time period and the power load corresponding to the set temperature.

[0151] The power load data adjustment device provided by the embodiments of the present invention predicts the turn-on information of an electrical device in a future first time period based on a data set of the electrical device, where the data set includes historical usage data of the electrical device; determines the set temperature of the electrical device in a future second time period based on the turn-on information of the electrical device in the future first time period; the second time period is the time period when the electrical device is turned on in the first time period; determines the power load baseline of the electrical device in the future first time period based on the historical power load data of the electrical device; selects the power load baseline of the electrical device in the future second time period from the power load baselines of the electrical device in the future first time period; and adjusts the power load data of the electrical device based on the difference between the power load baseline of the electrical device in the future second time period and the power load corresponding to the set temperature. The present invention fully considers the user's power consumption requirements during the process of estimating the adjustable load, and realizes the adjustment of the power load data on the basis of ensuring the user experience.

[0152] In one embodiment, the set temperature includes a maximum set temperature and a minimum set temperature, and the adjustment module 405 is specifically configured to:

[0153] Determine a first difference between the power load baseline of the electrical equipment in the future second time period and the power consumption load corresponding to the maximum set temperature, and a second difference between the power load baseline of the electrical equipment in the future second time period and the power consumption load corresponding to the minimum set temperature; when the first difference is 0, use the second difference as the load adjustment value; when the second difference is 0, use 0 as the load adjustment value; when the first difference is greater than 0 and the second difference is greater than 0, determine the load adjustment value based on the first difference and the second difference; adjust the power load data of the electrical equipment based on the load adjustment value.

[0154] In one embodiment, the adjustment module 405 is specifically configured to:

[0155] Determine the adjustment priority of the electrical equipment based on the load adjustment value and the weight of the electrical equipment; when the load value to be adjusted is less than the load adjustment upper limit value, select at least one target electrical equipment based on the adjustment priority of the electrical equipment; the load adjustment upper limit value is the total load adjustment value in the future first time period; adjust the power load data of the target electrical equipment based on the load adjustment value.

[0156] In one embodiment, the adjustment module 405 is specifically configured to:

[0157] When the adjustment time of the power load data is a legal working day, determine the power load baseline of the electrical equipment in the future first time period based on the historical power load data of multiple historical legal working days; the power consumption loads in the first time period of the historical legal working days all conform to the power consumption loads corresponding to the user's power consumption behavior habits; when the adjustment time of the power load data is a non-legal working day, determine the power load baseline of the electrical equipment in the future first time period based on the historical power load data of multiple consecutive historical non-legal working days.

[0158] In one embodiment, the prediction module 401 is specifically configured to:

[0159] Extract feature data from the historical usage data in the dataset, perform cross-combinations of different features on the feature data; extract effective information from the cross-combined features; the effective information is a feature or feature combination that is relevant to whether the electrical equipment is turned on; predict the turn-on information of the electrical equipment in the future first time period based on the effective information.

[0160] In one embodiment, the set temperature determination module 402 is specifically configured to:

[0161] Determine the second time period based on the turning-on information of the electrical device in the future first time period; extract the characteristic data of the second time period from the data set; determine the set temperature of the electrical device in the future second time period based on the characteristic data of the second time period.

[0162] Figure 5 An example of a schematic physical structure diagram of an electronic device is shown as Figure 5 shown. The electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540. Among them, the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logical instructions in the memory 530 to execute the following methods: predict the turning-on information of the electrical device in the future first time period based on the data set of the electrical device; the data set includes the historical usage data of the electrical device; determine the set temperature of the electrical device in the future second time period based on the turning-on information of the electrical device in the future first time period; the second time period is the time period when the electrical device is turned on in the first time period; the set temperature is the temperature parameter set by the user when using the electrical device; determine the power load baseline of the electrical device in the future first time period based on the historical power load data of the electrical device; select the power load baseline of the electrical device in the future second time period from the power load baseline of the electrical device in the future first time period; adjust the power load data of the electrical device based on the difference between the power load corresponding to the set temperature and the power load baseline of the electrical device in the future second time period.

[0163] In addition, when the logical instructions in the above-mentioned memory 530 are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0164] On the other hand, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is configured to execute the power load data adjustment method provided in the above embodiments. For example, it includes: predicting the turn-on information of the electrical device in a first future time period based on a data set of the electrical device; the data set includes historical usage data of the electrical device; determining the set temperature of the electrical device in a second future time period based on the turn-on information of the electrical device in the first future time period; the second time period is the time period when the electrical device is turned on within the first time period; the set temperature is the temperature parameter set by the user when using the electrical device; determining the power load baseline of the electrical device in the first future time period based on the historical power load data of the electrical device; selecting the power load baseline of the electrical device in the second future time period from the power load baseline of the electrical device in the first future time period; adjusting the power load data of the electrical device based on the difference between the power load baseline of the electrical device in the second future time period and the power load corresponding to the set temperature.

[0165] In another aspect, an embodiment of the present invention discloses a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is capable of executing the power load data adjustment method provided in the above method embodiments. For example, it includes: predicting the turn-on information of the electrical device in a first future time period based on a data set of the electrical device; the data set includes historical usage data of the electrical device; determining the set temperature of the electrical device in a second future time period based on the turn-on information of the electrical device in the first future time period; the second time period is the time period when the electrical device is turned on within the first time period; the set temperature is the temperature parameter set by the user when using the electrical device; determining the power load baseline of the electrical device in the first future time period based on the historical power load data of the electrical device; selecting the power load baseline of the electrical device in the second future time period from the power load baseline of the electrical device in the first future time period; adjusting the power load data of the electrical device based on the difference between the power load baseline of the electrical device in the second future time period and the power load corresponding to the set temperature.

[0166] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0167] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0169] The above embodiments are only used to illustrate the present invention, rather than to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, those of ordinary skill in the art should understand that various combinations, modifications, or equivalent replacements of the technical solutions of the present invention do not deviate from the spirit and scope of the technical solutions of the present invention, and should all be covered by the scope of the claims of the present invention.

Claims

1. A method for adjusting power load data, characterized in that, Including: Based on the dataset of the electrical equipment, predicting the turning-on information of the electrical equipment in the first future time period; The dataset includes the historical usage data of the electrical equipment; Based on the turning-on information of the electrical equipment in the first future time period, determining the set temperature of the electrical equipment in the second future time period; The second time period is the time period when the electrical equipment is turned on during the first time period; the set temperature is the temperature parameter set by the user when using the electrical equipment; the set temperature includes the maximum set temperature and the minimum set temperature; Based on the historical power load data of the electrical equipment, determining the power load baseline of the electrical equipment in the first future time period; Selecting the power load baseline of the electrical equipment in the second future time period from the power load baseline of the electrical equipment in the first future time period; Based on the difference between the power load baseline of the electrical equipment in the second future time period and the power load corresponding to the set temperature, adjusting the power load data of the electrical equipment; The adjusting the power load data of the electrical equipment based on the difference between the power load baseline of the electrical equipment in the second future time period and the power load corresponding to the set temperature includes: Determining a first difference between the power load baseline of the electrical equipment in the second future time period and the power load corresponding to the maximum set temperature, and a second difference between the power load baseline of the electrical equipment in the second future time period and the power load corresponding to the minimum set temperature; When the first difference is 0, taking the second difference as the load adjustment value; When the second difference is 0, taking 0 as the load adjustment value; When the first difference is greater than 0 and the second difference is greater than 0, determining the load adjustment value based on the first difference and the second difference; Based on the load adjustment value, adjusting the power load data of the electrical equipment.

2. The method for adjusting power load data according to claim 1, characterized in that The adjusting the power load data of the electrical equipment based on the load adjustment value includes: Based on the load adjustment value and the weight of the electrical equipment, determining the adjustment priority of the electrical equipment; When the load value to be adjusted is less than the load adjustment upper limit value, selecting at least one target electrical equipment based on the adjustment priority of the electrical equipment; the load adjustment upper limit value is the total load adjustment value in the first future time period; Based on the load adjustment value, adjusting the power load data of the target electrical equipment.

3. The method for adjusting power load data according to claim 1, characterized in that, The determining the power load baseline of the electrical equipment in the first future time period based on the historical power load data of the electrical equipment includes: When the adjustment time of the power load data is a legal working day, determining the power load baseline of the electrical equipment in the first future time period based on the historical power load data of multiple historical legal working days; the power load in the first time period of the historical legal working days all conforms to the power load corresponding to the user's electricity consumption behavior habits; When the adjustment time of the power load data is a non-statutory working day, based on the historical power load data of multiple consecutive historical non-statutory working days, determine the power load baseline of the electrical equipment in the future first time period.

4. The adjustment method of power load data according to claim 1, characterized in that The prediction of the turn-on information of the electrical equipment in the future first time period based on the data set of the electrical equipment includes: Extract feature data from the historical usage data in the data set, and perform cross-combinations of different features on the feature data; Extract effective information from the cross-combined features; the effective information is a feature or feature combination that is relevant to whether the electrical equipment is turned on; Based on the effective information, predict the turn-on information of the electrical equipment in the future first time period.

5. The method for adjusting power load data according to any one of claims 1 to 4, characterized in that The determination of the set temperature of the electrical equipment in the future second time period based on the turn-on information of the electrical equipment in the future first time period includes: Based on the turn-on information of the electrical equipment in the future first time period, determine the second time period; Extract the feature data of the second time period from the data set; Based on the feature data of the second time period, determine the set temperature of the electrical equipment in the future second time period.

6. An adjustment device for electric load data, characterized in that, It includes: A prediction module for predicting the turn-on information of the electrical equipment in the future first time period based on the data set of the electrical equipment; The data set includes the historical usage data of the electrical equipment; A set temperature determination module for determining the set temperature of the electrical equipment in the future second time period based on the turn-on information of the electrical equipment in the future first time period; the second time period is the time period when the electrical equipment is turned on in the first time period; the set temperature is the temperature parameter set by the user when using the electrical equipment; the set temperature includes the maximum set temperature and the minimum set temperature; A power load baseline determination module for determining the power load baseline of the electrical equipment in the future first time period based on the historical power load data of the electrical equipment; A selection module for selecting the power load baseline of the electrical equipment in the future second time period from the power load baseline of the electrical equipment in the future first time period; An adjustment module for adjusting the power load data of the electrical equipment based on the difference between the power load baseline of the electrical equipment in the future second time period and the power load corresponding to the set temperature; The adjustment module is further configured to determine a first difference between the power load baseline of the electrical equipment in the future second time period and the power load corresponding to the maximum set temperature, and a second difference between the power load baseline of the electrical equipment in the future second time period and the power load corresponding to the minimum set temperature; When the first difference is 0, use the second difference as the load adjustment value; When the second difference is 0, use 0 as the load adjustment value; When the first difference is greater than 0 and the second difference is greater than 0, determine the load adjustment value based on the first difference and the second difference; Based on the load adjustment value, adjust the power load data of the electrical equipment.

7. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for adjusting power load data according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements the method for adjusting power load data according to any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for adjusting power load data according to any one of claims 1 to 5.

Images