Temperature sensitive load prediction method and device, equipment and storage medium

Abstract

The invention discloses a temperature sensitive load prediction method and device, equipment and a storage medium. The method comprises the steps of obtaining a first electricity consumption set and a first temperature data set of a target area in a historical time period; determining reference electricity consumption according to the first electricity consumption set and the first temperature data set; acquiring a second electricity consumption set and a second temperature data set of the target area in the current time period; determining a first temperature-sensitive electricity consumption set according to the second electricity consumption set and the reference electricity consumption; determining a third temperature data set of the target area in the future time period; inputting the second temperature data set, the first temperature-sensitive electricity consumption set and the third temperature data set into a temperature-sensitive electricity consumption prediction model to obtain a target temperature-sensitive electricity consumption set in a future time period; and according to the third temperature data set and the target temperature-sensitive electricity consumption set, determining d target temperature-sensitive loads corresponding to the d sub future time periods. The method is beneficial for improving the accuracy of load prediction.

Description

Technical Field

[0001] The present application relates to the technical field of load forecasting, and in particular to a method, device, equipment and storage medium for temperature-sensitive load forecasting. Background Art

[0002] In modern urban distribution networks, supply and demand balance is an important link to ensure the safe and stable operation of the power system. With the acceleration of global climate change and urbanization, the impact of high temperatures in summer on power demand is becoming increasingly significant, especially the continued increase in temperature-sensitive loads, which is mainly reflected in the widespread use of air conditioners and refrigeration equipment. This significant increase in temperature-sensitive loads often leads to greater power supply pressure on the power grid during peak load periods, resulting in insufficient power supply or reduced power quality.

[0003] Load forecasting is the main means to solve the above problems. In traditional load forecasting methods, historical load data is usually analyzed to predict load data for future time periods. This method fails to fully consider the direct impact of high temperature weather on temperature-sensitive electricity consumption and temperature-sensitive loads, resulting in low accuracy of load forecasting. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the embodiments of the present application provide a temperature-sensitive load prediction method, device, equipment and storage medium. The benchmark power consumption is determined by the first power consumption set and the first temperature data set of the target area in the historical time period, and the first temperature-sensitive power consumption set of the target area in the current time period is determined according to the second power consumption set and the benchmark power consumption of the current time period. Then, the target temperature-sensitive power consumption set of the target area in the future time period is predicted by the second temperature data set of the current time period, the first temperature-sensitive power consumption set and the third temperature data set of the future time period, and the d target temperature-sensitive loads corresponding to the d sub-future time periods of the target area in the future time period are determined. The predicted target temperature-sensitive load takes into account the impact of temperature on the load, thereby improving the accuracy of load prediction.

[0005] In a first aspect, an embodiment of the present application provides a temperature-sensitive load prediction method, comprising:

[0006] Obtain a first power consumption set and a first temperature data set of a target area in a historical time period; the historical time period includes a working days; the first power consumption set includes a first power consumptions, and the a working days correspond to the a first power consumptions one by one; the first temperature data set includes t temperatures corresponding to the a working days, and each working day corresponds to at least one temperature; a is a positive integer, and t is an integer greater than a;

[0007] Determining a reference power consumption according to the first power consumption set and the first temperature data set;

[0008] Obtain a second power consumption set and a second temperature data set of the target area in a current time period; the current time period includes b working days before the current working day; the second power consumption set includes b second power consumptions, and the b working days correspond to the b second power consumptions one by one; the second temperature data set includes h temperatures corresponding to the b working days, and each working day corresponds to at least one temperature; b is a positive integer, and h is an integer greater than b;

[0009] Determine, according to the second power consumption set and the benchmark power consumption, a first temperature-sensitive power consumption set of the target area in the current time period; the first temperature-sensitive power consumption set includes b first temperature-sensitive power consumptions, and the b working days correspond one to one to the b first temperature-sensitive power consumptions;

[0010] Determine a third temperature data set of the target area in a future time period; the future time period includes c working days after the current working day; the third temperature data set includes y temperatures corresponding to the c working days, each working day corresponds to at least one temperature; c is a positive integer, and y is an integer greater than c;

[0011] Inputting the second temperature data set, the first temperature-sensitive power consumption set and the third temperature data set into a temperature-sensitive power consumption prediction model to obtain a target temperature-sensitive power consumption set for the target area in the future time period; the target temperature-sensitive power consumption set includes c target temperature-sensitive power consumptions, and the c working days correspond one to one to the c target temperature-sensitive power consumptions;

[0012] According to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period are determined; each working day in the future time period includes at least one sub-future time period, each sub-future time period corresponds to a target temperature-sensitive load, and d is an integer greater than c.

[0013] In a second aspect, an embodiment of the present application provides a temperature-sensitive load prediction device, comprising:

[0014] An acquisition unit, configured to acquire a first power consumption set and a first temperature data set of a target area in a historical time period; the historical time period includes a working days; the first power consumption set includes a first power consumptions, and the a working days correspond to the a first power consumptions one-to-one; the first temperature data set includes t temperatures corresponding to the a working days, and each working day corresponds to at least one temperature; a is a positive integer, and t is an integer greater than a;

[0015] a processing unit, configured to determine a reference power consumption according to the first power consumption set and the first temperature data set;

[0016] The acquisition unit is used to acquire a second power consumption set and a second temperature data set of the target area in a current time period; the current time period includes b working days before the current working day; the second power consumption set includes b second power consumptions, and the b working days correspond to the b second power consumptions one by one; the second temperature data set includes h temperatures corresponding to the b working days, and each working day corresponds to at least one temperature; b is a positive integer, and h is an integer greater than b;

[0017] The processing unit is used to determine a first temperature-sensitive power consumption set of the target area in the current time period according to the second power consumption set and the benchmark power consumption; the first temperature-sensitive power consumption set includes b first temperature-sensitive power consumptions, and the b working days correspond to the b first temperature-sensitive power consumptions one by one;

[0018] Determine a third temperature data set of the target area in a future time period; the future time period includes c working days after the current working day; the third temperature data set includes y temperatures corresponding to the c working days, each working day corresponds to at least one temperature; c is a positive integer, and y is an integer greater than c;

[0019] Inputting the second temperature data set, the first temperature-sensitive power consumption set and the third temperature data set into a temperature-sensitive power consumption prediction model to obtain a target temperature-sensitive power consumption set for the target area in the future time period; the target temperature-sensitive power consumption set includes c target temperature-sensitive power consumptions, and the c working days correspond one to one to the c target temperature-sensitive power consumptions;

[0020] According to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period are determined; each working day in the future time period includes at least one sub-future time period, each sub-future time period corresponds to a target temperature-sensitive load, and d is an integer greater than c.

[0021] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory, the processor being connected to the memory, the memory being used to store a computer program, and the processor being used to execute the computer program stored in the memory, so that the electronic device performs the method described in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the first aspect.

[0023] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program product is operable to cause a computer to execute the method described in the first aspect.

[0024] Implementing the embodiments of the present application has the following beneficial effects:

[0025] In an embodiment of the present application, firstly, a first power consumption set and a first temperature data set of the target area in a historical time period are obtained, and a baseline power consumption can be determined based on the first power consumption set and the first temperature data set. Then, a second power consumption set and a second temperature data set of the target area in the current time period are obtained, and a first temperature-sensitive power consumption set of the target area in the current time period can be determined based on the second power consumption set and the baseline power consumption. Further, a third temperature data set of the target area in a future time period is determined, so that the second temperature data set, the first temperature-sensitive power consumption set, and the third temperature data set are input into a temperature-sensitive power consumption prediction model to obtain a target temperature-sensitive power consumption set of the target area in the future time period. Finally, based on the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period can be determined. Therefore, the benchmark power consumption can be determined through the power consumption and temperature data of the target area in the historical time period, and the temperature-sensitive power consumption of the current time period can be determined based on the power consumption and the benchmark power consumption of the current time period. Therefore, the temperature-sensitive power consumption of the future time period can be predicted based on the temperature data and temperature-sensitive power consumption of the current time period, as well as the temperature data of the future time period, and then the temperature-sensitive load of the future time period can be determined. The predicted temperature-sensitive load takes into account the impact of temperature data on power consumption, thereby improving the accuracy of load prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 A schematic diagram of an application scenario of a temperature-sensitive load prediction method provided in an embodiment of the present application;

[0028] Figure 2 A schematic diagram of a flow chart of a temperature-sensitive load prediction method provided in an embodiment of the present application;

[0029] Figure 3 A schematic diagram of a temperature-sensitive load prediction device provided in an embodiment of the present application;

[0030] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0033] Reference to "embodiments" herein means that a particular feature, result, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0034] First, see Figure 1 , Figure 1 A schematic diagram of an application scenario of a temperature-sensitive load prediction method provided in an embodiment of the present application. The distribution network is a power grid directly connected to the power demand side in the power system, constructed by overhead lines, cables, distribution transformers, reactive power compensation devices and other related facilities, and is mainly used to distribute the electric energy in the transmission system to various power demand sides step by step through distribution equipment.

[0035] like Figure 1As shown, the distribution network is directly connected to the power demand side in the target area, and the power demand side in the target area may include residential power consumption and enterprise power consumption. The power-consuming equipment at the power demand side receives and consumes electric energy from the distribution network so that the electric equipment can work normally. The electric equipment may include: air conditioners, fans, water heaters, elevators, etc. Among them, the total power consumed by all the electric equipment in the target area is the load of the target area. The total amount of electric energy consumed by all the electric equipment in the target area in a time period is the power consumption of the target area in that time period.

[0036] In an embodiment of the present application, a smart meter is provided on the distribution network and the distribution line of the power demand end, for collecting the power consumption corresponding to the power demand end. The smart meter on the distribution line can be connected to the processing device for communication. Among them, the processing device can be a server for high-performance data processing and caching functions, including: application server, database server, virtual private server (Virtual Private Server, VPS), cloud server, etc., which is not limited in this application. The processing device can also be an integrated circuit, chip or processor for data processing, data transceiver and data storage functions, including: central processing unit (CPU), digital signal processor (DSP), microcontroller unit (MCU), etc., which is not limited in this application. The processing device can also be an electronic device integrated with a processor, a transceiver and a memory, such as a smart phone, a computer, etc., which is not limited in this application.

[0037] Among them, the processing device can obtain the power consumption of the power demand end of the target area in real time from the smart meters of the distribution network and the distribution lines of the target area, and store the power consumption and collection time of the target area in the memory. When performing load forecasting, the processing device can obtain the power consumption of the target area in the historical time period from the memory, and predict the power consumption of the target area in the future time period based on the power consumption of the target area in the historical time period. Thus, the load of the target area in the future time period is determined based on the power consumption of the target area in the future time period.

[0038] However, under different temperature conditions, there will be large differences in electricity consumption on the power demand side. For example, in hot summer weather, the electricity consumption in the target area will increase significantly due to the large-scale use of high-probability electrical equipment such as air conditioners and refrigeration equipment on the power demand side. The part of electricity consumption that increases significantly due to rising temperatures is called temperature-sensitive electricity consumption, and the load corresponding to temperature-sensitive electricity consumption is temperature-sensitive load. Therefore, load forecasting is inaccurate only by using electricity consumption in historical time periods. In order to avoid the problem of insufficient power supply or deterioration of power quality in the distribution network due to increased temperature-sensitive electricity consumption, it is necessary to predict temperature-sensitive electricity consumption and temperature-sensitive loads.

[0039] To this end, applied to the above scenario, in the temperature-sensitive load forecasting method provided in the present application, the processing device obtains a first power consumption set and a first temperature data set of the target area in a historical time period;

[0040] The processing device determines a reference power consumption according to the first power consumption set and the first temperature data set;

[0041] The processing device obtains a second power consumption set and a second temperature data set of the target area in the current time period;

[0042] The processing device determines a first temperature-sensitive power consumption set of the target area in the current time period according to the second power consumption set and the reference power consumption;

[0043] The processing device determines a third temperature data set for the target area at a future time period;

[0044] The processing device inputs the second temperature data set, the first temperature-sensitive power consumption set, and the third temperature data set into the temperature-sensitive power consumption prediction model to obtain a target temperature-sensitive power consumption set for the target area in the future time period;

[0045] The processing device determines, based on the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods in the target area in the future time period; each working day in the future time period includes at least one sub-future time period, each sub-future time period corresponds to a target temperature-sensitive load, and d is an integer greater than c.

[0046] It can be seen that, applied to the above scenario, the processing device can determine the benchmark power consumption based on the first power consumption set and the first temperature data set of the target area in the historical time period, and determine the first temperature-sensitive power consumption set of the target area in the current time period based on the second power consumption set and the benchmark power consumption of the current time period. Then, based on the second temperature data set, the first temperature-sensitive power consumption set of the target area in the current time period, and the third temperature data set of the target area in the future time period, the target temperature-sensitive power consumption set of the target area in the future time period is predicted, thereby determining the target temperature-sensitive load of the target area in the future time period. The predicted target temperature-sensitive load fully considers the impact of temperature on load, thereby improving the accuracy of load prediction.

[0047] See also Figure 2 , Figure 2 A flow chart of a temperature-sensitive load prediction method provided in an embodiment of the present application. The method is applied to the processing device in the above scenario, and the method includes but is not limited to the following steps:

[0048] 201: Obtain a first power consumption set and a first temperature data set of a target area in a historical time period.

[0049] In the embodiment of the present application, the historical time period includes a working days. The first power consumption set includes a first power consumptions, and a working day corresponds to a first power consumption in a one-to-one manner. The first temperature data set includes t temperatures corresponding to a working day, and each working day corresponds to at least one temperature. a is a positive integer, and t is an integer greater than a.

[0050] It should be noted that the first power consumption set and the first temperature data set of the historical time period are mainly used to determine the benchmark power consumption of the target area on each working day. Therefore, the historical time period should be a time period with mild temperature and no or little effect on power consumption. For example, the historical time period can include all working days in April. Among them, during holidays, due to residents going out and enterprises stopping production, holiday power consumption can be supplied normally, and there will be no problem of insufficient supply. Therefore, when predicting temperature-sensitive loads, only the power consumption and temperature data on working days are considered.

[0051] The processing device obtains from the memory the first power consumption corresponding to each working day in the historical time period of the target area and at least one temperature corresponding to each working day in the historical time period, and obtains a first power consumption set and a first temperature data set. The at least one temperature corresponding to each working day is at least one temperature of at least one historical time period corresponding to the working day, for example, the temperature from 10 a.m. to 11 a.m. and the temperature from 11 a.m. to 12 a.m. on a working day.

[0052] 202: Determine a reference power consumption according to the first power consumption set and the first temperature data set.

[0053] In the embodiment of the present application, the reference power consumption represents the average power consumption of the target area on each working day under the condition that the temperature is less than the first preset temperature. The first preset temperature is the highest temperature at which the power consumption of the target area is not affected by temperature, and the first preset temperature can be obtained in advance based on historical data analysis.

[0054] Exemplarily, determining the reference power consumption according to the first power consumption set and the first temperature data set may include:

[0055] Obtain the maximum temperature corresponding to each of the a working days from the t temperatures in the first temperature data set;

[0056] Determine a working day in which the highest temperature is less than or equal to the first preset temperature as the first working day, and obtain m first working days;

[0057] Determine the power consumption corresponding to each of the m first working days from the first power consumption set to obtain m third power consumptions;

[0058] The reference power consumption is determined according to the m third power consumptions.

[0059] In the embodiment of the present application, m is a positive integer less than or equal to a. The processing device can determine the baseline power consumption of the target area by using the power consumption on the working day when the maximum temperature is less than or equal to the first preset temperature.

[0060] Specifically, the processing device first obtains the maximum temperature corresponding to each working day from the t temperatures corresponding to the a working days in the first temperature data set. For example, the temperatures corresponding to working day d1 include: 23, 25, 26, 28, 25, 19, and the maximum temperature of working day d1 obtained by the processing device is 28. In this way, the processing device can obtain the maximum temperature corresponding to each working day in the a working days.

[0061] Then, the processing device obtains working days with a maximum temperature less than or equal to the first preset temperature from the a working days, obtaining m first working days. The power consumption corresponding to each working day in the m first working days is obtained from the first power consumption set, obtaining m third power consumptions.

[0062] Finally, the processing device determines the average power consumption of the m third power consumptions to obtain the benchmark power consumption, that is, the average power consumption of the target area on each working day without being affected by temperature.

[0063] It can be seen that the processing equipment can select m first working days with the highest temperature less than or equal to the first preset temperature from a working days, and determine the baseline power consumption of the target area based on the m third power consumptions corresponding to the m first working days. Based on the baseline power consumption, the power consumption of the target area that increases under the influence of temperature, that is, the temperature-sensitive power consumption, can be determined. Therefore, through the correspondence between the temperature-sensitive power consumption and temperature, the temperature-sensitive power consumption for the future time period can be predicted, thereby improving the accuracy of the temperature-sensitive power consumption prediction.

[0064] 203: Obtain a second power consumption set and a second temperature data set for the target area in the current time period.

[0065] In an embodiment of the present application, the current time period includes b working days before the current working day. The second power consumption set includes b second power consumptions, and the b working days correspond to the b second power consumptions one by one. The second temperature data set includes h temperatures corresponding to the b working days, and each working day corresponds to at least one temperature. b is a positive integer, and h is an integer greater than b. Optionally, b can be 10.

[0066] It should be noted that in order to improve the prediction accuracy of temperature-sensitive electricity consumption, the processing device needs to obtain the second electricity consumption set and the second temperature data set of the target area on b working days before the current working day, so as to predict the electricity consumption of the target area in the future time period based on the electricity consumption changes and temperature changes on the b working days before the current working day.

[0067] 204: Determine a first temperature-sensitive power consumption set of the target area in the current time period according to the second power consumption set and the reference power consumption.

[0068] In the embodiment of the present application, the first temperature-sensitive power consumption set includes b first temperature-sensitive power consumptions, and b working days correspond to the b first temperature-sensitive power consumptions one by one. The first temperature-sensitive power consumption is the difference between the second power consumption corresponding to the first temperature-sensitive power consumption and the reference power consumption. The processing device can determine the first temperature-sensitive power consumption set according to the b second power consumptions in the second power consumption set and the reference power consumption.

[0069] Exemplarily, determining the first temperature-sensitive power consumption set of the target area in the current time period according to the second power consumption set and the reference power consumption may include:

[0070] Determine that the difference between each second power consumption in the second power consumption set and the reference power consumption is the third temperature-sensitive power consumption, and obtain b third temperature-sensitive power consumptions;

[0071] adjusting the third temperature-sensitive power consumption that is less than the preset temperature-sensitive power consumption among the b third temperature-sensitive power consumptions to the preset temperature-sensitive power consumption, to obtain b fourth temperature-sensitive power consumptions;

[0072] Obtain the maximum temperature corresponding to each of the b working days from the h temperatures in the second temperature data set;

[0073] Determine the working day with the highest temperature less than or equal to the second preset temperature among b working days as the second working day, and adjust the fourth temperature-sensitive power consumption corresponding to the second working day among the b fourth temperature-sensitive power consumptions to the preset temperature-sensitive power consumption, to obtain a first temperature-sensitive power consumption set.

[0074] In the embodiment of the present application, b working days correspond one-to-one to b third temperature-sensitive power consumptions, and b working days correspond one-to-one to b fourth temperature-sensitive power consumptions.

[0075] Specifically, the processing device first determines the difference between each of the b second power consumptions in the second power consumption set and the reference power consumption, and obtains b third temperature-sensitive power consumptions.

[0076] It should be noted that the actual engineering significance of temperature-sensitive electricity consumption refers to the increase in electricity consumption caused by a significant increase in temperature. Temperature-sensitive electricity consumption should increase with increasing temperature and decrease with decreasing temperature. Therefore, temperature-sensitive electricity consumption will not have negative values.

[0077] Based on this, the processing device adjusts the third temperature-sensitive power consumption that is less than the preset temperature-sensitive power consumption among the b third temperature-sensitive power consumptions to the preset temperature-sensitive power consumption, and obtains b fourth temperature-sensitive power consumptions. Optionally, the preset temperature-sensitive power consumption may be 0. The preset temperature-sensitive power consumption may be 0, or may be pre-set according to the actual weather conditions in the target area.

[0078] It should be noted that when the maximum temperature of a working day is less than the preset temperature, the temperature-sensitive power consumption will also be reduced to 0. Therefore, the processing device also needs to adjust the fourth temperature-sensitive power consumption corresponding to the working day with the maximum temperature less than or equal to the second preset temperature to the preset temperature-sensitive power consumption. Specifically, the processing device first obtains the maximum temperature corresponding to each working day from the h temperatures corresponding to the b working days in the second temperature data set. Then, all second working days with the maximum temperature less than or equal to the second preset temperature are selected from the b working days, and the fourth temperature-sensitive power consumption corresponding to the second working day among the b fourth temperature-sensitive power consumptions is adjusted to the preset temperature-sensitive power consumption, and b first temperature-sensitive power consumptions are obtained, that is, a first temperature-sensitive power consumption set is obtained. Among them, the second preset temperature can be equal to the first preset temperature, and optionally, the second preset temperature can be 0.

[0079] It can be seen that the processing device can obtain b third temperature-sensitive power consumptions by determining the difference between each second power consumption in the second power consumption set and the benchmark power consumption. By adjusting the temperature-sensitive power consumption that is less than the preset temperature-sensitive power consumption among the b third temperature-sensitive power consumptions, b fourth temperature-sensitive power consumptions can be obtained. By adjusting the fourth temperature-sensitive power consumption corresponding to the working day with the highest temperature less than or equal to the second preset temperature, the first temperature-sensitive power consumption set can be obtained. Therefore, by correcting the temperature-sensitive power consumption, a more accurate first temperature-sensitive power consumption set can be obtained, thereby improving the accuracy of the prediction of temperature-sensitive power consumption and temperature-sensitive load.

[0080] 205: Determine a third temperature dataset of the target area in a future time period.

[0081] In the embodiment of the present application, the future time period includes c working days after the current working day. The third temperature data set includes y temperatures corresponding to c working days, and each working day corresponds to at least one temperature. c is a positive integer, and y is an integer greater than c. It can be understood that the prediction of temperature-sensitive power consumption in the present application is a short-term prediction, that is, the prediction starts from the next working day of the current working day.

[0082] Optionally, the processing device can obtain a third temperature data set of the target area in the future time period from the predicted temperature data released by the meteorological department. It should be noted that since the temperatures in the third temperature data set are all predicted temperatures, the prediction of temperature-sensitive power consumption based on the third temperature data set itself has errors. As long as the error is within a preset error range, it can help the distribution network to reasonably distribute electric energy and solve the problem of insufficient power supply caused by the increase of temperature-sensitive loads.

[0083] 206: Input the second temperature data set, the first temperature-sensitive power consumption set, and the third temperature data set into a temperature-sensitive power consumption prediction model to obtain a target temperature-sensitive power consumption set for the target area in a future time period.

[0084] In an embodiment of the present application, the target temperature-sensitive power consumption set includes c target temperature-sensitive power consumptions, and c working days correspond to c target temperature-sensitive power consumptions one by one. The temperature-sensitive power consumption prediction model is a model built based on a random forest algorithm. The processing device inputs the second temperature data set, the first temperature-sensitive power consumption set, and the third temperature data set into the temperature-sensitive power consumption prediction model, and predicts the target temperature-sensitive power consumption set of the target area in the future time period through the temperature-sensitive power consumption prediction model.

[0085] Exemplarily, before inputting the second temperature data set, the first temperature-sensitive power consumption set, and the third temperature data set into the temperature-sensitive power consumption prediction model, the following may also be included:

[0086] Get historical sample sets;

[0087] Divide the historical sample set into a training set and a test set;

[0088] Randomly sample the data in the training set to obtain i sample sets;

[0089] According to i sample sets, i decision trees are generated, and each sample set corresponds to a decision tree, specifically including: obtaining a target sample set, and generating a reference decision tree according to the target sample set; determining a target feature set corresponding to the target sample set; randomly selecting j features from the target feature set, and using the set consisting of j features as a feature subset of the reference decision tree; selecting a partition feature from the feature subset according to a preset partitioning standard, and partitioning the reference decision tree according to the partition feature to obtain a target decision tree;

[0090] According to i decision trees, the initial temperature-sensitive electricity consumption prediction model is determined;

[0091] The initial temperature-sensitive electricity consumption prediction model is tested using the test set to obtain the prediction error;

[0092] According to the prediction error and the initial temperature-sensitive electricity consumption prediction model, a temperature-sensitive electricity consumption prediction model is determined.

[0093] In an embodiment of the present application, the historical sample set includes multiple fourth temperature data sets and multiple second temperature-sensitive power consumption sets corresponding to multiple first sample time periods, and multiple fifth temperature data sets and multiple third temperature-sensitive power consumption sets corresponding to multiple second sample time periods. Multiple first sample time periods correspond to multiple second sample time periods one by one. Each first sample time period includes b working days before the sample working day corresponding to the first sample time period, and each second sample time period includes c working days after the sample working day of the first sample time period corresponding to the second sample time period. The fourth temperature data set includes h temperatures corresponding to b working days corresponding to the fourth temperature data set, and each working day corresponds to at least one temperature. The second temperature-sensitive power consumption set includes b second temperature-sensitive power consumptions corresponding to b working days corresponding to the second temperature-sensitive power consumption set, and b working days correspond to the b second temperature-sensitive power consumptions one by one. The fifth temperature data set includes y temperatures corresponding to c working days corresponding to the fifth temperature data set, and each working day corresponds to at least one temperature. The third temperature-sensitive power consumption set includes c target temperature-sensitive power consumptions corresponding to the third temperature-sensitive power consumption set, and the c target temperature-sensitive power consumptions correspond one-to-one to the c working days corresponding to the third temperature-sensitive power consumption set.

[0094] Where i and j are both positive integers. The target sample set is any sample set among the i sample sets. The target feature set includes all features corresponding to the target sample set. The target decision tree is the decision tree corresponding to the target sample set among the i decision trees.

[0095] Specifically, the processing device first needs to obtain a historical sample set, where the historical sample set can be sample data of the previous q years of the current forecast year, where q is equal to the number of first sample time periods and is equal to the number of second sample time periods. The multiple first sample time periods are the q time periods corresponding to the current time period in the previous q years, and the multiple second sample time periods are the q time periods corresponding to the future time period in the previous q years. The processing device divides the historical sample set into a training set and a test set, for example, the data corresponding to the previous year of the current forecast year in the historical sample set is used as a training set, and the other data is used as a training set.

[0096] Then, the processing device randomly samples the data in the training set, and the number of data sampled each time can be preset. For example, 5 sample data are extracted each time, and the dimension of each sample data can be 21. By randomly sampling the data in the training set, i sample sets can be obtained. i is the total number of samplings, and optionally, i can be 100. It should be noted that since the data in the i sample sets are all randomly sampled data, the sample sets can be diverse. By constructing a temperature-sensitive electricity consumption prediction model based on the i sample sets, the risk of overfitting of the temperature-sensitive electricity consumption prediction model can be reduced, and the accuracy of temperature-sensitive electricity consumption prediction can be improved.

[0097] Further, the processing device generates i decision trees based on i sample sets. Each decision tree generated can be used to predict temperature-sensitive electricity consumption. Finally, the prediction results of i decision trees are combined to output the final predicted temperature-sensitive electricity consumption. Taking the generation of a target decision tree based on a target sample set as an example, the processing device obtains the target sample set and generates a reference decision tree based on the target sample set. The root node of the reference decision tree is used to store the target sample set. Then, a target feature set consisting of all features corresponding to the target sample set is determined, wherein the type of feature extracted from each sample data in the target sample set can be preset. Further, the processing device randomly selects j features from the target feature set, and uses the set consisting of j features as a feature subset of the reference decision tree. j can be preset according to the actual prediction accuracy requirements. Optionally, j can be 122. Random selection of features can make the selected features diverse, so as to reduce the risk of overfitting of the temperature-sensitive electricity consumption prediction model and improve the accuracy of temperature-sensitive electricity consumption prediction. Next, the processing device selects partition features from the feature subset according to the preset partitioning criteria, and partitions the nodes of the reference decision tree according to the partitioning features to generate new leaf nodes on the reference decision tree, which are used to store the partitioning features. Among them, the preset partitioning criteria may include, for example, any of the following partitioning criteria: feature partitioning based on information gain, feature partitioning based on the Gini index, feature partitioning based on variance reduction, and so on. In this way, by regressively partitioning the reference decision tree features until the decision tree reaches the preset depth, a target decision tree corresponding to the target sample set can be obtained. Based on the above-mentioned method for generating the target decision tree, i decision trees can be generated.

[0098] Thus, the processing device will integrate the constructed i decision trees, connect the outputs of the i decision trees to the combiner, and obtain the initial temperature-sensitive power consumption prediction model. In the initial temperature-sensitive power consumption prediction model, each of the i decision trees can predict the temperature-sensitive power consumption and obtain the predicted temperature-sensitive power consumption corresponding to the decision tree. The combiner can determine the final output temperature-sensitive power consumption based on the i predicted temperature-sensitive power consumption corresponding to the i decision trees. For example, the average value of the i predicted temperature-sensitive power consumption can be used as the final output temperature-sensitive power consumption.

[0099] After obtaining the initial temperature-sensitive power consumption prediction model, the processing device can test the initial temperature-sensitive power consumption prediction model through the test set, and determine the prediction error based on the temperature-sensitive power consumption predicted by the initial temperature-sensitive power consumption prediction model and the actual temperature-sensitive power consumption in the test set. Optionally, the processing device can determine the mean absolute percentage error (MAPE) between the predicted temperature-sensitive power consumption and the actual temperature-sensitive power consumption, and use the mean absolute percentage error as the prediction error. When the prediction error is less than or equal to the preset error, the initial temperature-sensitive power consumption prediction model is used as the temperature-sensitive power consumption prediction model. When the prediction error is greater than the preset error, the initial temperature-sensitive power consumption prediction model continues to be trained until the prediction error is less than or equal to the preset error, and the temperature-sensitive power consumption prediction model is obtained.

[0100] It can be seen that by dividing the historical sample set into a training set and a test set, and randomly sampling the training set to obtain i sample sets, the sample set can be made diverse, the risk of overfitting of the temperature-sensitive electricity consumption prediction model can be reduced, and the accuracy of the prediction can be improved. Then, by random sampling and feature partitioning, i decision trees can be generated based on i sample sets, which can make the feature subsets diverse, reduce the risk of overfitting of the temperature-sensitive electricity consumption prediction model, and improve the accuracy of the prediction. According to the i decision trees, the initial temperature-sensitive electricity consumption prediction model can be determined. Furthermore, the initial temperature-sensitive electricity consumption prediction model can be tested with the test set to obtain the prediction error. Finally, based on the prediction error and the initial temperature-sensitive electricity consumption prediction model, the temperature-sensitive electricity consumption prediction model can be obtained. The accuracy of the temperature-sensitive electricity consumption prediction can be improved by using the temperature-sensitive electricity consumption prediction model to predict temperature-sensitive electricity consumption.

[0101] After obtaining the temperature-sensitive power consumption prediction model, the processing device may input the second temperature data set, the first temperature-sensitive power consumption set, and the third temperature data set into the temperature-sensitive power consumption prediction model to obtain the target temperature-sensitive power consumption set for the target area in the future time period. It is understandable that the temperature-sensitive power consumption prediction model can only predict one target temperature-sensitive power consumption at a time. Therefore, the temperature-sensitive power consumption prediction model needs to predict c times according to the time sequence of c working days to obtain c target temperature-sensitive power consumptions, that is, the target temperature-sensitive power consumption set.

[0102] 207: Determine, according to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period.

[0103] In the embodiment of the present application, each working day in the future time period includes at least one sub-future time period, each sub-future time period corresponds to a target temperature-sensitive load, and d is an integer greater than c. It can be understood that the above-mentioned predicted target temperature-sensitive power consumption set is the temperature-sensitive power consumption corresponding to each working day in the c working days, and the processing device also needs to determine the temperature-sensitive power consumption and temperature-sensitive load of different sub-future time periods under each working day to formulate a power distribution plan corresponding to each sub-future time period.

[0104] In an optional embodiment, before determining, according to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period, the following method may also be included:

[0105] Determine the average temperature of each of the c working days based on the third temperature data set;

[0106] If there are k consecutive working days among the c working days, and the k average temperatures corresponding to them are all greater than the third preset temperature, then the average temperature difference between any two adjacent working days among the k working days is determined according to the k average temperatures to obtain the k-1 average temperature difference;

[0107] According to the k-1 average temperature difference, k-1 proportional coefficients are determined;

[0108] According to k-1 proportionality coefficients, determine the k-1 temperature-sensitive electricity consumption growth;

[0109] According to the increase in k-1 temperature-sensitive electricity consumption, k-1 target temperature-sensitive electricity consumption corresponding to k-1 working days among k working days is adjusted to obtain the adjusted k-1 target temperature-sensitive electricity consumption.

[0110] In the embodiment of the present application, each working day corresponds to an average temperature. k is an integer greater than x and less than or equal to c, x is a positive integer less than c, and x represents the minimum number of working days for continuous high temperature to cause a change in the target temperature-sensitive power consumption of the target area, which can be preset according to actual test results.

[0111] Specifically, the processing device first obtains all temperatures corresponding to each of the c working days from the third temperature data set, and then determines the average temperature of all temperatures of each working day.

[0112] If there are k consecutive working days with k average temperatures greater than the third preset temperature among c working days, for example, the average temperature of four consecutive working days on Monday, Tuesday, Wednesday and Thursday is greater than the third preset temperature, then the processing device will determine the difference between any two adjacent average temperatures among the k average temperatures in sequence according to the time sequence of the k working days, that is, the average temperature difference between any two adjacent working days among the k working days, thereby obtaining the k-1 average temperature difference. The third preset temperature represents the highest average temperature that causes the change in power consumption in the target area, and can be preset according to the actual test results.

[0113] It can be understood that when k average temperatures for k consecutive working days are all greater than the third preset temperature, the impact of temperature on the power consumption of the target area will continue to increase.

[0114] Therefore, the processing device determines k-1 proportionality coefficients based on the k-1 average temperature difference values, and one proportionality coefficient is determined for each average temperature difference value. The proportionality coefficient is used to represent the growth ratio of temperature-sensitive power consumption with temperature. The mapping relationship between the average temperature difference value and the proportionality coefficient can be preset according to the actual test results. The processing device obtains the k-1 temperature-sensitive power consumption growth amount by multiplying each proportionality coefficient in the k-1 proportionality coefficients by the target temperature-sensitive power consumption of the working day corresponding to the proportionality coefficient as the temperature-sensitive power consumption growth amount.

[0115] Finally, the processing device sums each temperature-sensitive power consumption increase in the k-1 temperature-sensitive power consumption increase with the target temperature-sensitive power consumption of the working day corresponding to the temperature-sensitive power consumption increase, obtains the adjusted k-1 target temperature-sensitive power consumption, and uses the adjusted k-1 target temperature-sensitive power consumption as the k-1 target temperature-sensitive power consumption.

[0116] Therefore, by determining that the k average temperatures corresponding to k consecutive working days among c working days are all greater than the third preset temperature, the processing device will determine k-1 proportional coefficients according to the average temperature difference between any two adjacent working days among the k working days, and determine the k-1 temperature-sensitive power consumption increase according to the k-1 proportional coefficients, so as to adjust the k-1 target temperature-sensitive power consumption and obtain the adjusted k-1 target temperature-sensitive power consumption. The adjusted target temperature-sensitive power consumption takes into account the impact of continuous high temperature on the temperature-sensitive power consumption, so that the adjusted target temperature-sensitive power consumption is closer to the actual temperature-sensitive power consumption, thereby improving the accuracy of the temperature-sensitive power consumption prediction.

[0117] Exemplarily, determining, according to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period may include:

[0118] Obtaining d temperatures whose temperatures are greater than the first preset temperature from the y temperatures in the third temperature data set;

[0119] Obtain d sub-future time periods corresponding to the d temperatures from the future time period;

[0120] Determine the difference between each of the d temperatures and the first preset temperature to obtain d temperature differences;

[0121] According to the d temperature differences, determine the total temperature difference corresponding to each of the c working days, and obtain c total temperature differences;

[0122] Determine d second temperature-sensitive power consumptions according to the d temperature differences, the c total temperature differences and the target temperature-sensitive power consumption set;

[0123] According to the d second temperature-sensitive power consumptions and the d sub-future time periods, d target temperature-sensitive loads are determined.

[0124] In the embodiment of the present application, each of the c working days corresponds to at least one of the d temperatures. Each of the c working days corresponds to at least one of the d temperature differences. The d sub-future time periods correspond one-to-one to the d second temperature-sensitive power consumptions.

[0125] Specifically, the processing device first obtains d temperatures greater than the first preset temperature from the y temperatures of the third temperature data set. The d temperatures represent the temperatures corresponding to different time periods on each of the c working days. The processing device can divide the future time period into d sub-future time periods corresponding to the d temperatures based on the d temperatures. Each sub-future time period is a time period on the working day corresponding to the sub-future time period. For example, on a working day, the temperature from 10 to 11 o'clock and the temperature from 11 to 12 o'clock are both greater than the first preset temperature, and the processing device will use 10 to 11 o'clock as a sub-future time period and 11 to 12 o'clock as a sub-future time period.

[0126] Then, the processing device determines the difference between each of the d temperatures and the first preset temperature, and obtains d temperature differences. According to all the temperature differences corresponding to each of the c working days, the total temperature difference corresponding to each of the c working days is determined, and c total temperature differences are obtained. The processing device uses the ratio of each of the d temperature differences to the total temperature difference corresponding to the temperature difference as the distribution density, and obtains d distribution densities. Each of the c working days corresponds to at least one of the d distribution densities. Next, the processing uses the product of the d distribution densities and the target temperature-sensitive power consumption of the working day corresponding to the distribution density as the second temperature-sensitive power consumption, and obtains d second temperature-sensitive power consumptions.

[0127] Finally, the processing device obtains the duration of each of the d future sub-time periods, and uses the ratio of each of the d second temperature-sensitive power consumptions to the duration of the sub-time period corresponding to the second temperature-sensitive power consumption as the temperature-sensitive load of the sub-time period, to obtain d target temperature-sensitive loads.

[0128] Therefore, by adjusting the temperature-sensitive power consumption of the sub-future time periods in the future time period whose temperature is greater than the first preset temperature, the target temperature-sensitive power consumption of each sub-future time period is obtained, and the target temperature-sensitive load is determined, thereby fully considering the impact of temperature on temperature-sensitive power consumption and temperature-sensitive load, thereby improving the accuracy of temperature-sensitive load prediction.

[0129] Exemplarily, the method may further include:

[0130] Acquire a residential electricity consumption set and an enterprise electricity consumption set of a target area in a historical time period according to the first electricity consumption set;

[0131] Determine f historical residential electricity consumption time periods corresponding to f residential electricity consumption, and determine g historical enterprise electricity consumption time periods corresponding to g enterprise electricity consumption;

[0132] Obtain d historical residential electricity consumption time periods corresponding to d sub-future time periods from f historical residential electricity consumption time periods, and obtain d residential electricity consumption corresponding to d historical residential electricity consumption time periods from f residential electricity consumption;

[0133] Obtain d historical enterprise electricity consumption time periods corresponding to d sub-future time periods from g historical enterprise electricity consumption time periods, and obtain d enterprise electricity consumption corresponding to d historical enterprise electricity consumption time periods from g enterprise electricity consumption;

[0134] According to the electricity consumption of d residents and d enterprises, determine the electricity consumption weights of d residents and d enterprises;

[0135] According to d residential electricity consumption weights, d enterprise electricity consumption weights and d target temperature-sensitive loads, d residential temperature-sensitive loads and d enterprise temperature-sensitive loads corresponding to d sub-future time periods are determined.

[0136] In the embodiment of the present application, the residential electricity consumption set includes f residential electricity consumptions and g enterprise electricity consumptions corresponding to a working days. Each working day corresponds to at least one residential electricity consumption and at least one enterprise electricity consumption. f and g are positive integers greater than a. Each residential electricity consumption corresponds to a historical residential electricity consumption time period, and each enterprise electricity consumption corresponds to a historical enterprise electricity consumption time period.

[0137] It should be noted that the electricity demand side in the target area includes residential electricity and corporate electricity, wherein residential electricity and corporate electricity correspond to different electricity consumption time periods. Therefore, the processing equipment will determine d residential temperature-sensitive loads and d corporate temperature-sensitive loads corresponding to d sub-future time periods.

[0138] Specifically, the processing device first obtains the residential power consumption set and the enterprise power consumption set of the target area in the historical time period according to the first power consumption set. The residential power consumption set is the set of power consumption collected by the smart meters on the residential power lines, and the enterprise power consumption set is the set of power consumption collected by the smart meters on the enterprise power lines. The residential power consumption set and the enterprise power consumption set are both stored in the memory. The processing device can obtain the residential power consumption set and the enterprise power consumption set of the historical time period according to the first power consumption set.

[0139] Then, determine the f historical residential electricity consumption time periods corresponding to the f residential electricity consumption in the residential electricity consumption set, and determine the g historical enterprise electricity consumption time periods corresponding to the g enterprise electricity consumption in the enterprise electricity consumption set. Further, obtain d historical residential electricity consumption time periods corresponding to d sub-future time periods from the f historical residential electricity consumption time periods, and obtain d residential electricity consumption corresponding to d historical residential electricity consumption time periods from the f residential electricity consumption. Obtain d historical enterprise electricity consumption time periods corresponding to d sub-future time periods from the g historical enterprise electricity consumption time periods, and obtain d enterprise electricity consumption corresponding to d historical enterprise electricity consumption time periods from the g enterprise electricity consumption.

[0140] Further, the processing device determines that the sum of the residential electricity consumption and the enterprise electricity consumption corresponding to each of the d sub-future time periods is the total electricity consumption corresponding to the sub-future time period. The ratio of the residential electricity consumption corresponding to each of the d sub-future time periods to the total electricity consumption corresponding to the sub-future time period is used as the residential electricity weight corresponding to the residential electricity consumption, and d residential electricity weights are obtained. The ratio of the enterprise electricity consumption corresponding to each of the d sub-future time periods to the total electricity consumption corresponding to the sub-future time period is used as the enterprise electricity weight corresponding to the enterprise electricity consumption, and d enterprise electricity weights are obtained.

[0141] Finally, the processing device determines that the product of each residential electricity weight among the d residential electricity weights and the target temperature-sensitive load of the sub-future time period corresponding to the residential electricity weight is the residential temperature-sensitive load corresponding to the sub-future time period, and obtains d residential temperature-sensitive loads. The processing device determines that the product of each enterprise electricity weight among the d enterprise electricity weights and the target temperature-sensitive load of the sub-future time period corresponding to the enterprise electricity weight is the enterprise temperature-sensitive load corresponding to the sub-future time period, and obtains d enterprise temperature-sensitive loads.

[0142] Therefore, the residential electricity consumption weight and the enterprise electricity consumption weight are determined through the residential electricity consumption and the enterprise electricity consumption in the historical time period, and the residential temperature-sensitive load and the enterprise temperature-sensitive load are determined according to the residential electricity consumption weight and the enterprise electricity consumption weight. The changes of the residential temperature-sensitive load and the enterprise temperature-sensitive load in the future time period can be predicted, so that the power system can better distribute electricity.

[0143] In summary, in an embodiment of the present application, firstly, a first power consumption set and a first temperature data set of the target area in a historical time period are obtained, and a benchmark power consumption can be determined based on the first power consumption set and the first temperature data set. Then, a second power consumption set and a second temperature data set of the target area in the current time period are obtained, and a first temperature-sensitive power consumption set of the target area in the current time period can be determined based on the second power consumption set and the benchmark power consumption. Further, a third temperature data set of the target area in a future time period is determined, so that the second temperature data set, the first temperature-sensitive power consumption set and the third temperature data set are input into a temperature-sensitive power consumption prediction model to obtain a target temperature-sensitive power consumption set of the target area in the future time period. Finally, based on the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period can be determined. Therefore, the benchmark power consumption can be determined through the power consumption and temperature data of the target area in the historical time period, and the temperature-sensitive power consumption of the current time period can be determined based on the power consumption and the benchmark power consumption of the current time period. Therefore, the temperature-sensitive power consumption of the future time period can be predicted based on the temperature data and temperature-sensitive power consumption of the current time period, as well as the temperature data of the future time period, and then the temperature-sensitive load of the future time period can be determined. The predicted temperature-sensitive load takes into account the impact of temperature data on power consumption, thereby improving the accuracy of load prediction.

[0144] See also Figure 3 , Figure 3 A schematic diagram of a temperature-sensitive load prediction device provided in an embodiment of the present application. The temperature-sensitive load prediction device 300 may be a processing device of any of the above embodiments. The temperature-sensitive load prediction device 300 includes an acquisition unit 301 and a processing unit 302 .

[0145] The acquisition unit 301 is used to acquire a first power consumption set and a first temperature data set of a target area in a historical time period; the historical time period includes a working days; the first power consumption set includes a first power consumptions, and a working days correspond to a first power consumptions one by one; the first temperature data set includes t temperatures corresponding to a working days, and each working day corresponds to at least one temperature; a is a positive integer, and t is an integer greater than a;

[0146] The processing unit 302 is used to determine a reference power consumption according to the first power consumption set and the first temperature data set;

[0147] The acquisition unit 301 is used to acquire a second power consumption set and a second temperature data set of a target area in a current time period; the current time period includes b working days before the current working day; the second power consumption set includes b second power consumptions, and the b working days correspond to the b second power consumptions one by one; the second temperature data set includes h temperatures corresponding to the b working days, and each working day corresponds to at least one temperature; b is a positive integer, and h is an integer greater than b;

[0148] The processing unit 302 is used to determine a first temperature-sensitive power consumption set of the target area in the current time period according to the second power consumption set and the reference power consumption; the first temperature-sensitive power consumption set includes b first temperature-sensitive power consumptions, and b working days correspond to b first temperature-sensitive power consumptions one by one;

[0149] Determine a third temperature data set of the target area in a future time period; the future time period includes c working days after the current working day; the third temperature data set includes y temperatures corresponding to the c working days, and each working day corresponds to at least one temperature; c is a positive integer, and y is an integer greater than c;

[0150] The second temperature data set, the first temperature-sensitive power consumption set and the third temperature data set are input into the temperature-sensitive power consumption prediction model to obtain a target temperature-sensitive power consumption set for the target area in the future time period; the target temperature-sensitive power consumption set includes c target temperature-sensitive power consumptions, and c working days correspond to c target temperature-sensitive power consumptions one by one;

[0151] According to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods in the target area in the future time period are determined; each working day in the future time period includes at least one sub-future time period, each sub-future time period corresponds to a target temperature-sensitive load, and d is an integer greater than c.

[0152] In a possible embodiment, in determining, according to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period, the processing unit 302 is specifically configured to:

[0153] Obtaining d temperatures greater than the first preset temperature from the y temperatures in the third temperature data set; each of the c working days corresponds to at least one temperature among the d temperatures;

[0154] Obtain d sub-future time periods corresponding to the d temperatures from the future time period;

[0155] Determine the difference between each temperature in the d temperatures and the first preset temperature to obtain d temperature differences; each working day in the c working days corresponds to at least one temperature difference in the d temperature differences;

[0156] According to the d temperature differences, determine the total temperature difference corresponding to each of the c working days, and obtain c total temperature differences;

[0157] According to the d temperature differences, the c total temperature differences and the target temperature-sensitive power consumption set, d second temperature-sensitive power consumptions are determined; the d sub-future time periods correspond one to one with the d second temperature-sensitive power consumptions;

[0158] According to the d second temperature-sensitive power consumptions and the d sub-future time periods, d target temperature-sensitive loads are determined.

[0159] In a possible embodiment, in determining the reference power consumption according to the first power consumption set and the first temperature data set, the processing unit 302 is specifically configured to:

[0160] Obtain the maximum temperature corresponding to each of the a working days from the t temperatures in the first temperature data set;

[0161] Determine a working day whose maximum temperature is less than or equal to the first preset temperature among a working days as the first working day, and obtain m first working days, where m is a positive integer less than or equal to a;

[0162] Determine the power consumption corresponding to each of the m first working days from the first power consumption set to obtain m third power consumptions;

[0163] The reference power consumption is determined according to the m third power consumptions.

[0164] In a possible embodiment, in determining the first temperature-sensitive power consumption set of the target area in the current time period according to the second power consumption set and the reference power consumption, the processing unit 302 is specifically configured to:

[0165] Determine that the difference between each second power consumption and the reference power consumption in the second power consumption set is the third temperature-sensitive power consumption, and obtain b third temperature-sensitive power consumptions; the b working days correspond to the b third temperature-sensitive power consumptions one by one;

[0166] The third temperature-sensitive power consumption that is less than the preset temperature-sensitive power consumption among the b third temperature-sensitive power consumptions is adjusted to the preset temperature-sensitive power consumption, so as to obtain b fourth temperature-sensitive power consumptions; the b working days correspond to the b fourth temperature-sensitive power consumptions one by one;

[0167] Obtain the maximum temperature corresponding to each of the b working days from the h temperatures in the second temperature data set;

[0168] Determine the working day with the highest temperature less than or equal to the second preset temperature among b working days as the second working day, and adjust the fourth temperature-sensitive power consumption corresponding to the second working day among the b fourth temperature-sensitive power consumptions to the preset temperature-sensitive power consumption, to obtain a first temperature-sensitive power consumption set.

[0169] In a possible embodiment, before inputting the second temperature data set, the first temperature-sensitive power consumption set, and the third temperature data set into the temperature-sensitive power consumption prediction model, the processing unit 302 is further configured to:

[0170] Obtain a historical sample set; the historical sample set includes a plurality of fourth temperature data sets and a plurality of second temperature-sensitive power consumption sets corresponding to a plurality of first sample time periods, and a plurality of fifth temperature data sets and a plurality of third temperature-sensitive power consumption sets corresponding to a plurality of second sample time periods; the plurality of first sample time periods correspond one-to-one to the plurality of second sample time periods; each first sample time period includes b working days before a sample working day corresponding to the first sample time period, and each second sample time period includes c working days after a sample working day of the first sample time period corresponding to the second sample time period;

[0171] Divide the historical sample set into a training set and a test set;

[0172] Randomly sample the data in the training set to obtain i sample sets; i is a positive integer;

[0173] According to i sample sets, i decision trees are generated, and each sample set corresponds to a decision tree, specifically including: obtaining a target sample set, and generating a reference decision tree according to the target sample set, and the target sample set is any sample set in the i sample sets; determining a target feature set corresponding to the target sample set, and the target feature set includes all features corresponding to the target sample set; randomly selecting j features from the target feature set, and taking the set composed of j features as a feature subset of the reference decision tree, and j is a positive integer; according to a preset partitioning standard, selecting partitioning features from the feature subset, and partitioning the reference decision tree according to the partitioning features to obtain a target decision tree, and the target decision tree is a decision tree corresponding to the target sample set in the i decision trees;

[0174] According to i decision trees, the initial temperature-sensitive electricity consumption prediction model is determined;

[0175] The initial temperature-sensitive electricity consumption prediction model is tested using the test set to obtain the prediction error;

[0176] According to the prediction error and the initial temperature-sensitive electricity consumption prediction model, a temperature-sensitive electricity consumption prediction model is determined.

[0177] In a possible embodiment, before determining, according to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period, the processing unit 302 is further configured to:

[0178] Determine the average temperature of each of the c working days based on the third temperature data set;

[0179] If there are k consecutive working days among c working days, and the k average temperatures corresponding to them are all greater than the third preset temperature, then the average temperature difference between any two adjacent working days among the k working days is determined according to the k average temperatures to obtain a k-1 average temperature difference; each working day corresponds to an average temperature; k is an integer greater than x and less than or equal to c, and x is a positive integer less than c;

[0180] According to the k-1 average temperature difference, k-1 proportional coefficients are determined;

[0181] According to k-1 proportionality coefficients, determine the k-1 temperature-sensitive electricity consumption growth;

[0182] According to the increase in k-1 temperature-sensitive electricity consumption, k-1 target temperature-sensitive electricity consumption corresponding to k-1 working days among k working days is adjusted to obtain the adjusted k-1 target temperature-sensitive electricity consumption.

[0183] In a possible embodiment, the processing unit 302 is further configured to:

[0184] Obtain a residential electricity consumption set and an enterprise electricity consumption set of the target area in a historical time period according to the first electricity consumption set; the residential electricity consumption set includes f residential electricity consumptions and g enterprise electricity consumptions corresponding to a working days; each working day corresponds to at least one residential electricity consumption and at least one enterprise electricity consumption; f and g are positive integers greater than a;

[0185] Determine f historical residential electricity consumption time periods corresponding to f residential electricity consumption, and determine g historical enterprise electricity consumption time periods corresponding to g enterprise electricity consumption; each residential electricity consumption corresponds to a historical residential electricity consumption time period, and each enterprise electricity consumption corresponds to a historical enterprise electricity consumption time period;

[0186] Obtain d historical residential electricity consumption time periods corresponding to d sub-future time periods from f historical residential electricity consumption time periods, and obtain d residential electricity consumption corresponding to d historical residential electricity consumption time periods from f residential electricity consumption;

[0187] Obtain d historical enterprise electricity consumption time periods corresponding to d sub-future time periods from g historical enterprise electricity consumption time periods, and obtain d enterprise electricity consumption corresponding to d historical enterprise electricity consumption time periods from g enterprise electricity consumption;

[0188] According to the electricity consumption of d residents and d enterprises, determine the electricity consumption weights of d residents and d enterprises;

[0189] According to d residential electricity consumption weights, d enterprise electricity consumption weights and d target temperature-sensitive loads, d residential temperature-sensitive loads and d enterprise temperature-sensitive loads corresponding to d sub-future time periods are determined.

[0190] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown, the electronic device 400 includes a transceiver 401, a processor 402 and a memory 403. They are connected via a bus 404. The memory 403 is used to store computer programs and data, and can transmit the data stored in the memory 403 to the processor 402. The electronic device 400 can be a temperature-sensitive load prediction device 300. The electronic device 400 can also be a processing device of any of the above embodiments.

[0191] The processor 402 is used to read the computer program in the memory 403 and perform the following operations:

[0192] Acquire a first power consumption set and a first temperature data set of a target area in a historical time period;

[0193] Determining a reference power consumption according to the first power consumption set and the first temperature data set;

[0194] Obtain a second power consumption set and a second temperature data set for the target area in the current time period;

[0195] Determine a first temperature-sensitive power consumption set for the target area in the current time period according to the second power consumption set and the benchmark power consumption;

[0196] determining a third temperature data set for the target area at a future time period;

[0197] Inputting the second temperature data set, the first temperature-sensitive power consumption set and the third temperature data set into the temperature-sensitive power consumption prediction model to obtain a target temperature-sensitive power consumption set for the target area in the future time period;

[0198] According to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods in the target area in the future time period are determined.

[0199] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0200] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any one of the methods described in the above method embodiments.

[0201] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any one of the methods recorded in the above method embodiments.

[0202] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, 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 optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0203] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0204] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0205] 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 on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0206] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software program module.

[0207] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0208] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0209] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, 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 application.

Claims

1. A temperature sensitive load forecasting method, characterized in that: include: Acquire a first power consumption set and a first temperature data set of a target area in a historical time period; the historical time period includes a working days; the first power consumption set includes a first power consumptions, and the a working days correspond to the a first power consumptions one by one; the first temperature data set includes t temperatures corresponding to the a working days, and each working day corresponds to at least one temperature; a is a positive integer, and t is an integer greater than a; Determining a reference power consumption according to the first power consumption set and the first temperature data set; Obtain a second power consumption set and a second temperature data set of the target area in a current time period; the current time period includes b working days before the current working day; the second power consumption set includes b second power consumptions, and the b working days correspond to the b second power consumptions one by one; the second temperature data set includes h temperatures corresponding to the b working days, and each working day corresponds to at least one temperature; b is a positive integer, and h is an integer greater than b; Determine a first temperature-sensitive power consumption set for the target area in the current time period according to the second power consumption set and the reference power consumption; The first temperature-sensitive power consumption set includes b first temperature-sensitive power consumptions, and the b working days correspond one-to-one to the b first temperature-sensitive power consumptions; Determine a third temperature data set of the target area in a future time period; the future time period includes c working days after the current working day; the third temperature data set includes y temperatures corresponding to the c working days, each working day corresponds to at least one temperature; c is a positive integer, and y is an integer greater than c; Inputting the second temperature data set, the first temperature-sensitive power consumption set and the third temperature data set into a temperature-sensitive power consumption prediction model to obtain a target temperature-sensitive power consumption set for the target area in the future time period; The target temperature-sensitive power consumption set includes c target temperature-sensitive power consumptions, and the c working days correspond one to one with the c target temperature-sensitive power consumptions; According to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period are determined; each working day in the future time period includes at least one sub-future time period, each sub-future time period corresponds to a target temperature-sensitive load, and d is an integer greater than c.

2. The method according to claim 1, characterized in that The determining, according to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period includes: Obtaining d temperatures greater than the first preset temperature from the y temperatures in the third temperature data set; each of the c working days corresponds to at least one temperature among the d temperatures; Acquire d sub-future time periods corresponding to the d temperatures from the future time period; Determine the difference between each of the d temperatures and the first preset temperature to obtain d temperature differences; each of the c working days corresponds to at least one temperature difference among the d temperature differences; According to the d temperature differences, determining the total temperature difference corresponding to each of the c working days, to obtain c total temperature differences; Determine d second temperature-sensitive power consumptions according to the d temperature differences, the c total temperature differences and the target temperature-sensitive power consumption set; the d sub-future time periods correspond one-to-one to the d second temperature-sensitive power consumptions; The d target temperature-sensitive loads are determined according to the d second temperature-sensitive power consumptions and the d sub-future time periods.

3. The method according to claim 2, characterized in that The determining, according to the first power consumption set and the first temperature data set, a reference power consumption, includes: Obtaining the maximum temperature corresponding to each of the a working days from the t temperatures in the first temperature data set; Determine a working day in the a working days whose maximum temperature is less than or equal to the first preset temperature as the first working day, and obtain m first working days, where m is a positive integer less than or equal to a; Determine the power consumption corresponding to each of the m first working days from the first power consumption set to obtain m third power consumptions; The reference power consumption is determined according to the m third power consumptions.

4. The method according to any one of claims 1 to 3, characterized in that: The determining, according to the second power consumption set and the reference power consumption, a first temperature-sensitive power consumption set of the target area in the current time period includes: Determine that the difference between each second power consumption in the second power consumption set and the reference power consumption is a third temperature-sensitive power consumption, and obtain b third temperature-sensitive power consumptions; the b working days correspond one to one with the b third temperature-sensitive power consumptions; The third temperature-sensitive power consumption that is less than the preset temperature-sensitive power consumption among the b third temperature-sensitive power consumptions is adjusted to the preset temperature-sensitive power consumption, to obtain b fourth temperature-sensitive power consumptions; the b working days correspond one to one with the b fourth temperature-sensitive power consumptions; Obtaining the maximum temperature corresponding to each of the b working days from the h temperatures in the second temperature data set; Determine the working day among the b working days whose maximum temperature is less than or equal to the second preset temperature as the second working day, and adjust the fourth temperature-sensitive power consumption corresponding to the second working day among the b fourth temperature-sensitive power consumptions to the preset temperature-sensitive power consumption, to obtain the first temperature-sensitive power consumption set.

5. The method according to any one of claims 1 to 3, characterized in that: Before inputting the second temperature data set, the first temperature-sensitive power consumption set and the third temperature data set into the temperature-sensitive power consumption prediction model, the method further includes: Acquire a historical sample set; the historical sample set includes a plurality of fourth temperature data sets and a plurality of second temperature-sensitive power consumption sets corresponding to a plurality of first sample time periods, and a plurality of fifth temperature data sets and a plurality of third temperature-sensitive power consumption sets corresponding to a plurality of second sample time periods; the plurality of first sample time periods correspond one-to-one to the plurality of second sample time periods; each first sample time period includes b working days before a sample working day corresponding to the first sample time period, and each second sample time period includes c working days after a sample working day of the first sample time period corresponding to the second sample time period; Dividing the historical sample set into a training set and a test set; Randomly sample the data in the training set to obtain i sample sets; i is a positive integer; According to the i sample sets, i decision trees are generated, each sample set corresponds to a decision tree, specifically including: obtaining a target sample set, and generating a reference decision tree according to the target sample set, wherein the target sample set is any sample set among the i sample sets; determining a target feature set corresponding to the target sample set, wherein the target feature set includes all features corresponding to the target sample set; randomly selecting j features from the target feature set, and taking the set consisting of the j features as a feature subset of the reference decision tree, where j is a positive integer; selecting a partition feature from the feature subset according to a preset partitioning standard, and partitioning the reference decision tree according to the partition feature to obtain a target decision tree, wherein the target decision tree is a decision tree among the i decision trees corresponding to the target sample set; Determine an initial temperature-sensitive power consumption prediction model according to the i decision trees; Testing the initial temperature-sensitive power consumption prediction model using the test set to obtain a prediction error; The temperature-sensitive power consumption prediction model is determined according to the prediction error and the initial temperature-sensitive power consumption prediction model.

6. The method according to any one of claims 1 to 3, characterized in that: Before determining, according to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period, the method further includes: Determining the average temperature of each of the c working days according to the third temperature data set; If there are k consecutive working days among the c working days, and the k average temperatures corresponding to them are all greater than the third preset temperature, then the average temperature difference between any two adjacent working days among the k working days is determined according to the k average temperatures to obtain a k-1 average temperature difference; each working day corresponds to an average temperature; k is an integer greater than x and less than or equal to c, and x is a positive integer less than c; Determining k-1 proportionality coefficients according to the k-1 average temperature differences; Determine k-1 temperature-sensitive electricity consumption growth amounts according to the k-1 proportionality coefficients; According to the k-1 temperature-sensitive power consumption increase, the k-1 target temperature-sensitive power consumption corresponding to the k-1 working day among the k working days is adjusted to obtain the adjusted k-1 target temperature-sensitive power consumption.

7. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: Obtaining a residential electricity consumption set and an enterprise electricity consumption set of the target area in the historical time period according to the first electricity consumption set; the residential electricity consumption set includes f residential electricity consumptions and g enterprise electricity consumptions corresponding to the a working days; each working day corresponds to at least one residential electricity consumption and at least one enterprise electricity consumption; f and g are positive integers greater than a; Determine f historical residential electricity consumption time periods corresponding to the f residential electricity consumptions, and determine g historical enterprise electricity consumption time periods corresponding to the g enterprise electricity consumptions; each residential electricity consumption corresponds to a historical residential electricity consumption time period, and each enterprise electricity consumption corresponds to a historical enterprise electricity consumption time period; Obtaining d historical residential electricity consumption time periods corresponding to the d sub-future time periods from the f historical residential electricity consumption time periods, and obtaining d residential electricity consumption corresponding to the d historical residential electricity consumption time periods from the f residential electricity consumption; Obtaining d historical enterprise electricity consumption time periods corresponding to the d future sub-time periods from the g historical enterprise electricity consumption time periods, and obtaining d enterprise electricity consumption corresponding to the d historical enterprise electricity consumption time periods from the g enterprise electricity consumption; Determining d residential electricity consumption weights and d enterprise electricity consumption weights according to the d residential electricity consumption and the d enterprise electricity consumption; According to the d residential electricity consumption weights, the d enterprise electricity consumption weights and the d target temperature-sensitive loads, the d residential temperature-sensitive loads and d enterprise temperature-sensitive loads corresponding to the d sub-future time periods are determined.

8. A temperature sensitive load prediction device, characterized in that: include: an acquisition unit, configured to acquire a first power consumption set and a first temperature data set of a target area in a historical time period; the historical time period includes a working days; the first power consumption set includes a first power consumptions, and the a working days correspond to the a first power consumptions one-to-one; the first temperature data set includes t temperatures corresponding to the a working days, and each working day corresponds to at least one temperature; a is a positive integer, and t is an integer greater than a; a processing unit, configured to determine a reference power consumption according to the first power consumption set and the first temperature data set; The acquisition unit is used to acquire a second power consumption set and a second temperature data set of the target area in a current time period; the current time period includes b working days before the current working day; the second power consumption set includes b second power consumptions, and the b working days correspond to the b second power consumptions one by one; the second temperature data set includes h temperatures corresponding to the b working days, and each working day corresponds to at least one temperature; b is a positive integer, and h is an integer greater than b; The processing unit is configured to determine a first temperature-sensitive power consumption set of the target area in the current time period according to the second power consumption set and the reference power consumption; The first temperature-sensitive power consumption set includes b first temperature-sensitive power consumptions, and the b working days correspond one-to-one to the b first temperature-sensitive power consumptions; Determine a third temperature data set of the target area in a future time period; the future time period includes c working days after the current working day; the third temperature data set includes y temperatures corresponding to the c working days, each working day corresponds to at least one temperature; c is a positive integer, and y is an integer greater than c; Inputting the second temperature data set, the first temperature-sensitive power consumption set and the third temperature data set into a temperature-sensitive power consumption prediction model to obtain a target temperature-sensitive power consumption set for the target area in the future time period; The target temperature-sensitive power consumption set includes c target temperature-sensitive power consumptions, and the c working days correspond one to one with the c target temperature-sensitive power consumptions; According to the third temperature data set and the target temperature-sensitive power consumption set, d target temperature-sensitive loads corresponding to d sub-future time periods of the target area in the future time period are determined; each working day in the future time period includes at least one sub-future time period, each sub-future time period corresponds to a target temperature-sensitive load, and d is an integer greater than c.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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