Method and device for predicting power grid load, electronic equipment and storage medium

Abstract

The invention provides a power grid load prediction method and device, electronic equipment and a storage medium. According to the method and the device, when the future load of the power grid in the future time period is predicted, a plurality of factors which can influence the future load of the power grid in the future time period are considered, for example, the future load of the power grid in the future time period is predicted; the historical load, the current load, the historical extra power event, the current extra power event, the future extra power event, the historical weather data, the current weather data, the future weather data, the historical calendar data, the current calendar data, the future calendar data, the historical power consumption price, the current power consumption price, the future power consumption price and the like are comprehensively considered; in this way, the accuracy of predicting the future load of the power grid in the future time period can be improved, and secondly, no artificial participation exists in the process of predicting the future load of the power grid in the future time period, so that the labor cost is reduced, and the problem of low prediction accuracy caused by the artificial experience problem is avoided.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, electronic device, and storage medium for predicting power grid load. Background Art

[0002] With the continuous growth of energy demand and the increasing emphasis on environmental protection, the stability and efficiency of the power grid are becoming increasingly important. Traditional power systems rely primarily on fossil fuels for power generation, which not only causes serious pollution but also suffers from low energy utilization and insufficient power generation during peak periods, leading to power shortages.

[0003] Therefore, the demand for peak shaving and valley filling has been put forward.

[0004] Peak shaving and valley filling refers to the practice of energy storage systems storing excess energy during periods of low grid load and then releasing this energy during peak load periods. This approach balances grid loads, reduces power waste, improves grid stability and efficiency, and increases energy utilization, resulting in significant economic benefits. Summary of the Invention

[0005] The present application provides a method, device, electronic device, and storage medium for predicting power grid load.

[0006] In a first aspect, the present application provides a method for predicting power grid load, comprising:

[0007] Obtain the historical load of the power grid in the historical time period before the current moment, and obtain the current load of the power grid in the current time period at the current moment;

[0008] Obtain historical additional electricity-related events within a historical time period within a power consumption area supplied by the power grid, obtain current additional electricity-related events within a current time period within the power consumption area, and obtain future additional electricity-related events within a future time period within the power consumption area;

[0009] Acquire historical weather data of the power consumption area in a historical time period, acquire current weather data of the power consumption area in a current time period, and acquire future weather data of the power consumption area in a future time period;

[0010] Get historical calendar data for historical time periods, get current calendar data for the current time period, and get future calendar data for future time periods;

[0011] Obtaining historical electricity prices within a power consumption area supplied by the power grid during historical time periods, obtaining current electricity prices within the power consumption area during a current time period, and obtaining future electricity prices within the power consumption area during future time periods;

[0012] The future load of the power grid in a future time period is predicted based on historical load, current load, historical additional power-related events, current additional power-related events, future additional power-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices, and future electricity prices.

[0013] In a second aspect, the present application provides a device for predicting power grid load, comprising:

[0014] The first acquisition module is used to obtain the historical load of the power grid in the historical time period before the current moment, and obtain the current load of the power grid in the current time period at the current moment;

[0015] A second acquisition module is configured to acquire historical additional electricity-related events within a historical time period within a power consumption area supplied by the power grid, acquire current additional electricity-related events within a current time period within the power consumption area, and acquire future additional electricity-related events within a future time period within the power consumption area;

[0016] A third acquisition module is used to obtain historical weather data of the power consumption area in a historical time period, obtain current weather data of the power consumption area in a current time period, and obtain future weather data of the power consumption area in a future time period;

[0017] A fourth acquisition module is used to acquire historical calendar data of historical time periods, current calendar data of current time periods, and future calendar data of future time periods;

[0018] A fifth acquisition module is configured to acquire historical electricity prices within a power consumption area supplied by the power grid during historical time periods, acquire current electricity prices within the power consumption area during a current time period, and acquire future electricity prices within the power consumption area during future time periods;

[0019] The prediction module is used to predict the future load of the power grid in a future time period based on historical load, current load, historical additional power-related events, current additional power-related events, future additional power-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices.

[0020] In a third aspect, the present application shows an electronic device, which includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the method described in any of the above aspects.

[0021] In a fourth aspect, the present application shows a non-temporary computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute the method described in any of the above aspects.

[0022] In a fifth aspect, the present application illustrates a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method as described in any one of the above aspects.

[0023] The technical solution provided by this application may have the following beneficial effects:

[0024] In this application, the historical load of the power grid in the historical time period before the current moment is obtained, and the current load of the power grid in the current time period at the current moment is obtained; the historical additional electricity-related events in the historical time period within the power consumption area supplied by the power grid are obtained, the current additional electricity-related events in the current time period within the power consumption area are obtained, and the future additional electricity-related events in the future time period within the power consumption area are obtained; the historical weather data of the power consumption area in the historical time period is obtained, the current weather data of the power consumption area in the current time period is obtained, and the future weather data of the power consumption area in the future time period is obtained; the historical calendar data of the historical time period is obtained, and the current time period is obtained. Current calendar data, obtain future calendar data for future time periods; obtain historical electricity prices in historical time periods within the power consumption area supplied by the power grid, obtain current electricity prices in the current time period within the power consumption area, and obtain future electricity prices in future time periods within the power consumption area; predict the future load of the power grid in the future time period based on historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices.

[0025] Through the present application, when predicting the future load of the power grid in a future time period, multiple factors that may affect the future load of the power grid in a future time period are taken into account, such as historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices, etc. The factors considered are more comprehensive, which can improve the accuracy of the predicted future load of the power grid in a future time period. Secondly, there can be no human participation in the process of predicting the future load of the power grid in a future time period, which reduces labor costs and avoids the problem of low prediction accuracy caused by human experience problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flowchart of the steps of a method for predicting power grid load in the present application.

[0027] Figure 2 This is a structural block diagram of a device for predicting power grid load in the present application.

[0028] Figure 3 This is a block diagram of an electronic device of the present application.

[0029] Figure 4 This is a block diagram of an electronic device of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0031] The optimal charging and discharging strategy for an energy storage system depends not only on the current load conditions of the power grid, but also on the forecast of the future load of the power grid. By integrating the forecast of the future load of the power grid into the charging and discharging strategy of the energy storage system, more precise regulation of the load of the power grid can be achieved.

[0032] However, the current prediction of future load on the power grid is made manually based on experience, and the accuracy is low.

[0033] Therefore, in order to solve the above problems, the technical solution of the present application is proposed.

[0034] Specifically, refer to Figure 1 , shows a flowchart of the steps of a method for predicting power grid load of the present application, the method is applied to an electronic device, and the electronic device may include a terminal or a server, wherein the method includes:

[0035] In step S101 , the historical load of the power grid in the historical time period before the current moment is obtained, and the current load of the power grid in the current time period at the current moment is obtained.

[0036] Grid load refers to the total power consumed by all electricity users (including industrial, commercial, and residential users) in the grid. The size of the grid load directly reflects the grid's electricity demand and is an important basis for grid operation and scheduling.

[0037] This application divides time into time periods, and the length of each time period can be the same. For example, the length of the time period includes 1 hour, 2 hours, 3 hours, 6 hours, 12 hours, 24 hours, 36 hours, 48 ​​hours, 72 hours, 7 days, 14 days or one month, etc. In two adjacent time periods, the end time of the previous time period is adjacent to the start time of the next time period.

[0038] The time period of the current moment is the current time period, the time period adjacent to and after the current time period is the future time period, and the time period adjacent to and before the current time period is the historical time period.

[0039] The historical load of the power grid in the historical time period before the current moment and the current load of the power grid in the current time period at the current moment are used to help the power grid load forecasting model learn the periodic laws (such as hourly periodic laws, daily periodic laws, weekly periodic laws and / or monthly periodic laws, etc.) and trends (especially the recent periodic laws and trends of the power grid load) of the power grid.

[0040] The power grid system records and updates the load of the power grid in each time period in real time. In this way, the historical load of the power grid in the historical time period before the current moment and the current load of the power grid in the current time period at the current moment can be directly obtained through the power grid system.

[0041] In step S102, historical additional electricity-related events in historical time periods within the power consumption area supplied by the power grid are obtained, current additional electricity-related events in the current time period within the power consumption area are obtained, and future additional electricity-related events in the future time period within the power consumption area are obtained.

[0042] The electricity consumption areas supplied by the power grid may include residential areas, streets, industrial parks, township administrative areas or district and county administrative areas, etc.

[0043] Additional electricity-related events include events that require a large amount of electricity consumption in a short period of time or events that require no electricity consumption in a short period of time.

[0044] Events that require large amounts of electricity in the short term include sports events, concerts, or other large gatherings, which increase the load on the power grid in a short period of time.

[0045] Events that require no electricity use in the short term include: power outages for maintenance or power outages caused by faults, which result in a short-term reduction in grid load (because electricity is not available). They also include: factory shutdowns or power pole collapses, which result in a short-term reduction in grid load.

[0046] The historical additional electricity-related events in the historical time period within the power consumption area supplied by the power grid and the current additional electricity-related events in the current time period within the power consumption area are used to help the power grid load forecasting model learn the correlation between the additional electricity-related events in the power consumption area and the power grid load in the power consumption area.

[0047] The historical additional electricity-related events in the historical time period within the electricity consumption area supplied by the power grid and the current additional electricity-related events in the current time period within the electricity consumption area can be collected based on information such as news or notifications.

[0048] In step S103, historical weather data of the power consumption area in a historical time period is obtained, current weather data of the power consumption area in a current time period is obtained, and future weather data of the power consumption area in a future time period is obtained.

[0049] Weather data includes: temperature, humidity, wind speed, wind direction, rainfall, snowfall, and sunshine intensity.

[0050] The meteorological system (e.g., a meteorological bureau, a meteorological data API, or a meteorological monitoring station of a power grid system, including OpenWeatherMap) records weather data for each power consumption area in real time during each time period. Historical weather data for a power consumption area during a historical time period, current weather data for a power consumption area during a current time period, and future weather data for a power consumption area during a future time period can be obtained through the meteorological system. API stands for Application Programming Interface.

[0051] Temperature has a significant impact on the grid load. For example, high temperatures (such as high temperatures in summer) may lead to an increase in the number of users using air conditioning for cooling, resulting in an increase in the cooling load of the air conditioner, and thus an increase in the grid load. Alternatively, low temperatures (such as low temperatures in winter) may lead to an increase in the number of users using air conditioning for heating, resulting in an increase in the heating load of the air conditioner, and thus an increase in the grid load.

[0052] Humidity, light, wind speed and wind direction may all affect the electricity demand of industrial and commercial users in the electricity consumption area, and may also affect the electricity consumption of users using new energy sources (such as solar energy and wind energy). For example, if humidity, light, wind speed or wind direction causes the new energy generation equipment (such as wind power generation or photovoltaic power generation, etc.) of users of new energy sources (such as solar energy and wind energy) to be unable to provide sufficient electricity, they will need to use the electricity of the power grid to make up for the demand, thereby increasing the load on the power grid. Or, if humidity, light, wind speed or wind direction causes the new energy generation equipment of users of new energy sources (such as solar energy and wind energy) to be able to provide sufficient electricity, they will not use the electricity of the power grid, thereby reducing the load on the power grid.

[0053] In addition, rain or snowfall may cause sudden changes in grid load.

[0054] The historical weather data of the power consumption area in the historical time period and the current weather data of the power consumption area in the current time period are used to help the power grid load prediction model learn the correlation between the weather data of the power consumption area and the power grid load of the power consumption area.

[0055] In step S104, historical calendar data of historical time periods are obtained, current calendar data of current time periods are obtained, and future calendar data of future time periods are obtained.

[0056] The calendar data of a time period indicates whether the time period is Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday.

[0057] Alternatively, the calendar data of a time period is used to indicate whether the time period is a working day or a holiday.

[0058] Through historical statistics, it is found that the impact of working days and non-working days on the load of the power grid is usually different. On working days, the power grid has specific load characteristics, and on non-working days, the power grid also has specific load characteristics. For example, the load of the power grid on holidays is often lower than that on working days.

[0059] Alternatively, the load characteristics of the power grid on Monday, Tuesday, Wednesday, Thursday, Friday, Saturday and Sunday are also different.

[0060] The historical calendar data of the historical time period and the current calendar data of the current time period are used to help the power grid load forecasting model learn the correlation between the calendar data and the power grid load.

[0061] In step S105 , historical electricity prices in historical time periods within the power consumption area supplied by the power grid are obtained, current electricity prices in the current time period within the power consumption area are obtained, and future electricity prices in the future time periods within the power consumption area are obtained.

[0062] In step S106, the future load of the power grid in a future time period is predicted based on the historical load, the current load, the historical additional electricity-related events, the current additional electricity-related events, the future additional electricity-related events, the historical weather data, the current weather data, the future weather data, the historical calendar data, the current calendar data, the future calendar data, the historical electricity prices, the current electricity prices and the future electricity prices.

[0063] In the present application, a power grid load prediction model is trained in advance. Thus, in this step, historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices can all be input into the power grid load prediction model, so that the power grid load prediction model processes the historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices (for example, collaborative processing, etc.) to obtain the future load of the power grid in the future time period, and output the future load of the power grid in the future time period. The electronic device can obtain the future load of the power grid in the future time period output by the power grid load prediction model.

[0064] Grid load forecasting models can include: random forest model, XGBoost (eXtreme Gradient Boosting), LSTM (Long Short Term Memory) or Transformer (a deep learning architecture based on the self-attention mechanism, originally used for natural language processing tasks such as machine translation), etc.

[0065] In this application, the historical load of the power grid in the historical time period before the current moment is obtained, and the current load of the power grid in the current time period at the current moment is obtained; the historical additional electricity-related events in the historical time period within the power consumption area supplied by the power grid are obtained, the current additional electricity-related events in the current time period within the power consumption area are obtained, and the future additional electricity-related events in the future time period within the power consumption area are obtained; the historical weather data of the power consumption area in the historical time period is obtained, the current weather data of the power consumption area in the current time period is obtained, and the future weather data of the power consumption area in the future time period is obtained; the historical calendar data of the historical time period is obtained, and the current time period is obtained. Current calendar data, obtain future calendar data for future time periods; obtain historical electricity prices in historical time periods within the power consumption area supplied by the power grid, obtain current electricity prices in the current time period within the power consumption area, and obtain future electricity prices in future time periods within the power consumption area; predict the future load of the power grid in the future time period based on historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices.

[0066] Through the present application, when predicting the future load of the power grid in a future time period, multiple factors that may affect the future load of the power grid in a future time period are taken into account, such as historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices, etc. The factors considered are more comprehensive, which can improve the accuracy of the predicted future load of the power grid in a future time period. Secondly, there can be no human participation in the process of predicting the future load of the power grid in a future time period, which reduces labor costs and avoids the problem of low prediction accuracy caused by human experience problems.

[0067] In one embodiment of the present application, after historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices are input into the power grid load forecasting model, the feature extraction network in the power grid load forecasting model can respectively extract the feature vectors corresponding to the historical load, the feature vectors corresponding to the current load, the feature vectors corresponding to the historical additional electricity-related events, the feature vectors corresponding to the current additional electricity-related events, the feature vectors corresponding to the future additional electricity-related events, the feature vectors corresponding to the historical weather data, the feature vectors corresponding to the current weather data, the feature vectors corresponding to the future weather data, the feature vectors corresponding to the historical calendar data, the feature vectors corresponding to the current calendar data, the feature vectors corresponding to the future calendar data, the feature vectors corresponding to the historical electricity prices, the feature vectors corresponding to the current electricity prices and the feature vectors corresponding to the future electricity prices.

[0068] Among them, the feature extraction network can include an encoding layer and a multi-head self-attention layer. For the historical load, the encoding layer can be used to encode the historical load (such as one-hot encoding, etc.) to obtain a sparse vector corresponding to the historical load, and then the multi-head self-attention layer is used to perform multi-head self-attention weighting on the sparse vector corresponding to the historical load to obtain a feature vector corresponding to the historical load.

[0069] The same applies to current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices, and future electricity prices, which will not be elaborated here.

[0070] The coding layer can include one-hot etc.

[0071] The multi-head self-attention layer may include a Multi-head Self-attenti on Layer, etc.

[0072] Then, the characteristic vector corresponding to the historical load, the characteristic vector corresponding to the current load, the characteristic vector corresponding to the historical additional electricity-related events, the characteristic vector corresponding to the current additional electricity-related events, the characteristic vector corresponding to the future additional electricity-related events, the characteristic vector corresponding to the historical weather data, the characteristic vector corresponding to the current weather data, the characteristic vector corresponding to the future weather data, the characteristic vector corresponding to the historical calendar data, the characteristic vector corresponding to the current calendar data, the characteristic vector corresponding to the future calendar data, the characteristic vector corresponding to the historical electricity price, the characteristic vector corresponding to the current electricity price, and the characteristic vector corresponding to the future electricity price are input into the aggregation network in the power grid load forecasting model, so that the aggregation network aggregates the characteristic vector corresponding to the historical load, the characteristic vector corresponding to the current load, the characteristic vector corresponding to the historical additional electricity-related events, the characteristic vector corresponding to the current additional electricity-related events, the characteristic vector corresponding to the future additional electricity-related events, the characteristic vector corresponding to the historical weather data, the characteristic vector corresponding to the current weather data, the characteristic vector corresponding to the future weather data, the characteristic vector corresponding to the historical calendar data, the characteristic vector corresponding to the current calendar data, the characteristic vector corresponding to the future calendar data, the characteristic vector corresponding to the historical electricity price, the characteristic vector corresponding to the current electricity price, and the characteristic vector corresponding to the future electricity price into an aggregation vector.

[0073] For example, the characteristic vector corresponding to historical load, the characteristic vector corresponding to current load, the characteristic vector corresponding to historical additional electricity-related events, the characteristic vector corresponding to current additional electricity-related events, the characteristic vector corresponding to future additional electricity-related events, the characteristic vector corresponding to historical weather data, the characteristic vector corresponding to current weather data, the characteristic vector corresponding to future weather data, the characteristic vector corresponding to historical calendar data, the characteristic vector corresponding to current calendar data, the characteristic vector corresponding to future calendar data, the characteristic vector corresponding to historical electricity prices, the characteristic vector corresponding to current electricity prices, and the characteristic vector corresponding to future electricity prices can be concatenated end to end in sequence to obtain an aggregated vector.

[0074] Alternatively, if the dimensions of the feature vectors mentioned above are the same, the feature vectors corresponding to historical load, the feature vectors corresponding to current load, the feature vectors corresponding to historical additional electricity-related events, the feature vectors corresponding to current additional electricity-related events, the feature vectors corresponding to future additional electricity-related events, the feature vectors corresponding to historical weather data, the feature vectors corresponding to current weather data, the feature vectors corresponding to future weather data, the feature vectors corresponding to historical calendar data, the feature vectors corresponding to current calendar data, the feature vectors corresponding to future calendar data, the feature vectors corresponding to historical electricity prices, the feature vectors corresponding to current electricity prices, and the feature vectors corresponding to future electricity prices can be average pooled or maximum pooled to obtain an aggregated vector.

[0075] The aggregated vector is then processed using the forecasting network in the power grid load forecasting model to obtain the future load of the power grid in the future time period.

[0076] Alternatively, in one embodiment of the present application, when predicting the future load of the power grid in a future time period based on historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices, historical interaction relationship data between historical additional electricity-related events and historical loads can be obtained (through the load of the power grid in the same time period and the additional electricity-related events in the electricity consumption area supplied by the power grid, in the process of training the power grid load prediction model, the power grid load prediction model can learn the correlation between the load of the power grid in the same time period and the additional electricity-related events in the electricity consumption area supplied by the power grid, or in other words, learn the impact of the additional electricity-related events in the electricity consumption area supplied by the power grid in the same time period on the load of the power grid. In this way, when using the power grid load prediction model to predict the future load of the power grid in the future time period, using the historical interaction relationship data between historical additional electricity-related events and historical loads can improve the accuracy of predicting the future load of the power grid in the future time period).

[0077] Obtain historical interactive relationship data between historical weather data and historical loads (through the load of the power grid in the same time period and the weather data in the power consumption area supplied by the power grid, in the process of training the power grid load prediction model, the power grid load prediction model can learn the correlation between the load of the power grid in the same time period and the weather data in the power consumption area supplied by the power grid, or in other words, learn the impact of the weather data in the power consumption area supplied by the power grid in the same time period on the load of the power grid. In this way, when using the power grid load prediction model to predict the future load of the power grid in the future time period, using the historical interactive relationship data between historical weather data and historical loads can improve the accuracy of predicting the future load of the power grid in the future time period).

[0078] Obtain historical interaction relationship data between historical calendar data and historical loads. (Through the load and calendar data of the power grid in the same time period, during the training of the power grid load forecasting model, the power grid load forecasting model can learn the correlation between the load of the power grid in the same time period and the calendar data, or in other words, learn the impact of the calendar data in the same time period on the load of the power grid. In this way, when using the power grid load forecasting model to predict the future load of the power grid in the future time period, the historical interaction relationship data between the historical calendar data and the historical load can be used to improve the accuracy of predicting the future load of the power grid in the future time period).

[0079] Obtain historical interaction data between historical electricity prices and historical loads. (By using the load and electricity prices of the power grid in the same time period, during the training of the power grid load forecasting model, the power grid load forecasting model can learn the correlation between the load and electricity prices of the power grid in the same time period, or in other words, learn the impact of electricity prices in the same time period on the load of the power grid. In this way, when using the power grid load forecasting model to predict the future load of the power grid in the future time period, using the historical interaction data between historical electricity prices and historical loads can improve the accuracy of predicting the future load of the power grid in the future time period).

[0080] Obtain historical interactive relationship data among historical weather data, historical calendar data, and historical load. (Through the load of the power grid in the same time period, calendar data, and weather data in the power consumption area supplied by the power grid, in the process of training the power grid load forecasting model, the power grid load forecasting model can learn the correlation between the load of the power grid in the same time period, calendar data, and weather data in the power consumption area supplied by the power grid, or in other words, learn the impact of the weather data and calendar data in the power consumption area supplied by the power grid in the same time period on the load of the power grid. In this way, when using the power grid load forecasting model to predict the future load of the power grid in the future time period, using the historical interactive relationship data among historical weather data, historical calendar data, and historical load can improve the accuracy of predicting the future load of the power grid in the future time period).

[0081] Obtain historical interactive relationship data among historical weather data, historical additional electricity-related events, and historical loads (through the load of the power grid in the same time period, the additional electricity-related events in the power consumption area supplied by the power grid, and the weather data in the power consumption area supplied by the power grid, so that in the process of training the power grid load forecasting model, the power grid load forecasting model can learn the correlation between the load of the power grid in the same time period, the additional electricity-related events in the power consumption area supplied by the power grid, and the weather data in the power consumption area supplied by the power grid, or in other words, learn the impact of the weather data in the power consumption area supplied by the power grid in the same time period and the additional electricity-related events in the power consumption area supplied by the power grid on the load of the power grid. In this way, when using the power grid load forecasting model to predict the future load of the power grid in the future time period, using the historical interactive relationship data among historical weather data, historical additional electricity-related events, and historical loads can improve the accuracy of predicting the future load of the power grid in the future time period).

[0082] Obtain historical interactive relationship data among historical calendar data, historical additional electricity-related events, and historical load (through the load of the power grid in the same time period, additional electricity-related events in the power consumption area supplied by the power grid, and calendar data, so that in the process of training the power grid load forecasting model, the power grid load forecasting model can learn the correlation between the load of the power grid in the same time period, additional electricity-related events in the power consumption area supplied by the power grid, and calendar data, or in other words, learn the impact of the additional electricity-related events in the power consumption area supplied by the power grid in the same time period and calendar data on the load of the power grid. In this way, when using the power grid load forecasting model to predict the future load of the power grid in the future time period, using the historical interactive relationship data among historical calendar data, historical additional electricity-related events, and historical load can improve the accuracy of predicting the future load of the power grid in the future time period).

[0083] And, obtain the current interactive relationship data between the current additional electricity-related event and the current load. Obtain the current interactive relationship data between the current weather data and the current load. Obtain the current interactive relationship data between the current calendar data and the current load. Obtain the current interactive relationship data between the current electricity price and the current load. Obtain the current interactive relationship data between the current weather data, the current calendar data and the current load. Obtain the current interactive relationship data between the current weather data, the current additional electricity-related event and the current load. Obtain the current interactive relationship data between the current calendar data, the current additional electricity-related event and the current load. The specific effects can be found in the aforementioned example description and will not be described in detail here.

[0084] In one example, after a feature extraction network in a power grid load forecasting model extracts feature vectors corresponding to historical loads, current loads, historical additional power-related events, current additional power-related events, future additional power-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices, and future electricity prices, a feature interaction network in the power grid load forecasting model can be used to obtain historical interaction relationship data between historical additional power-related events and historical loads, historical weather data and historical loads, historical calendar data and historical loads, historical electricity prices and historical loads, historical weather data, historical calendar data, and historical loads, and historical weather data, historical additional power-related events, and historical loads. Obtain historical interactive relationship data among historical calendar data, historical additional power-related events, and historical load.

[0085] For example, the feature interaction network calculates the product between the feature vector corresponding to the historical additional power-related events and the feature vector corresponding to the historical load, and uses it as the historical interaction relationship data between the historical additional power-related events and the historical load.

[0086] The feature interaction network calculates the product between the feature vector corresponding to the historical weather data and the feature vector corresponding to the historical load, and uses it as the historical interaction relationship data between the historical weather data and the historical load.

[0087] The feature interaction network calculates the product between the feature vector corresponding to the historical calendar data and the feature vector corresponding to the historical load, and uses it as the historical interaction relationship data between the historical calendar data and the historical load.

[0088] The feature interaction network calculates the product between the feature vector corresponding to the historical electricity price and the feature vector corresponding to the historical load, and uses it as the historical interaction relationship data between the historical electricity price and the historical load.

[0089] The feature interaction network calculates the product of the feature vector corresponding to the historical weather data, the feature vector corresponding to the historical calendar data, and the feature vector corresponding to the historical load, and uses the product as the historical interaction relationship data among the historical weather data, the historical calendar data, and the historical load.

[0090] The feature interaction network calculates the product of the feature vectors corresponding to historical weather data, the feature vectors corresponding to historical additional power-related events, and the feature vectors corresponding to historical loads, and uses them as the historical interaction relationship data among the historical weather data, the historical additional power-related events, and the historical loads.

[0091] The feature interaction network calculates the product of the feature vectors corresponding to the historical calendar data, the feature vectors corresponding to the historical additional power-related events, and the feature vectors corresponding to the historical load, and uses them as the historical interaction relationship data among the historical calendar data, the historical additional power-related events, and the historical load.

[0092] The characteristic interaction network in the power grid load forecasting model can be used to obtain the current interaction relationship data between the current additional power-related events and the current load. The current interaction relationship data between the current weather data and the current load can be obtained. The current interaction relationship data between the current calendar data and the current load can be obtained. The current interaction relationship data between the current electricity price and the current load can be obtained. The current interaction relationship data between the current weather data, the current calendar data, and the current load can be obtained. The current interaction relationship data between the current weather data, the current additional power-related events, and the current load can be obtained. The current interaction relationship data between the current calendar data, the current additional power-related events, and the current load can be obtained.

[0093] For example, the feature interaction network calculates the product between the feature vector corresponding to the current additional power-related event and the feature vector corresponding to the current load, and uses it as the current interaction relationship data between the current additional power-related event and the current load.

[0094] The feature interaction network calculates the product between the feature vector corresponding to the current weather data and the feature vector corresponding to the current load, and uses the product as the current interaction relationship data between the current weather data and the current load.

[0095] The feature interaction network calculates the product between the feature vector corresponding to the current calendar data and the feature vector corresponding to the current load, and uses the product as the current interaction relationship data between the current calendar data and the current load.

[0096] The feature interaction network calculates the product between the feature vector corresponding to the current electricity price and the feature vector corresponding to the current load, and uses it as the current interaction relationship data between the current electricity price and the current load.

[0097] The feature interaction network calculates the product of the feature vector corresponding to the current weather data, the feature vector corresponding to the current calendar data, and the feature vector corresponding to the current load, and uses the product as the current interaction relationship data among the current weather data, the current calendar data, and the current load.

[0098] The feature interaction network calculates the product of the feature vector corresponding to the current weather data, the feature vector corresponding to the current additional power-related event, and the feature vector corresponding to the current load, and uses it as the current interaction relationship data among the current weather data, the current additional power-related event, and the current load.

[0099] The feature interaction network calculates the product of the feature vector corresponding to the current calendar data, the feature vector corresponding to the current additional power-related event, and the feature vector corresponding to the current load, and uses it as the current interaction relationship data among the current calendar data, the current additional power-related event, and the current load.

[0100] The product mentioned above is also an eigenvector.

[0101] The dimensions of the aforementioned feature vectors are the same.

[0102] Afterwards, the future load of the power grid in the future time period can be predicted based on future additional electricity-related events, future weather data, future calendar data, future electricity prices, various historical interaction relationship data (all historical interaction relationship data obtained above) and various current interaction relationship data (all current interaction relationship data obtained above).

[0103] For example, the feature vectors corresponding to future additional electricity-related events, the feature vectors corresponding to future weather data, the feature vectors corresponding to future calendar data, the feature vectors corresponding to future electricity prices, the feature vectors corresponding to various historical interaction relationship data, and the feature vectors corresponding to various current interaction relationship data are aggregated into an aggregate vector, and then the aggregate vector is processed using a prediction network to obtain the future load of the power grid in the future time period.

[0104] In another embodiment of the present application, when predicting the future load of the power grid in a future time period based on future additional electricity-related events, future weather data, future calendar data, future electricity prices, various historical interaction relationship data, and various current interaction relationship data, the attention weights corresponding to each historical interaction relationship data can be obtained, and the corresponding historical interaction relationship data can be weighted according to the attention weights corresponding to each historical interaction relationship data to obtain the historical weighted interaction relationship data corresponding to each historical interaction relationship data.

[0105] For example, the “historical interaction relationship data between historical additional electricity-related events and historical loads” is weighted according to the attention weight corresponding to the “historical interaction relationship data between historical additional electricity-related events and historical loads” to obtain the “historical weighted interaction relationship data between historical additional electricity-related events and historical loads”.

[0106] The “historical interaction relationship data between historical weather data and historical load” is weighted according to the attention weight corresponding to the “historical interaction relationship data between historical weather data and historical load” to obtain the “historical weighted interaction relationship data between historical weather data and historical load”.

[0107] The “historical interaction relationship data between historical calendar data and historical loads” is weighted according to the attention weight corresponding to the “historical interaction relationship data between historical calendar data and historical loads” to obtain the “historical weighted interaction relationship data between historical calendar data and historical loads”.

[0108] The “historical interaction relationship data between historical electricity prices and historical loads” is weighted according to the attention weight corresponding to the “historical interaction relationship data between historical electricity prices and historical loads” to obtain the “historical weighted interaction relationship data between historical electricity prices and historical loads”.

[0109] According to the attention weight corresponding to the "historical interaction relationship data among historical weather data, historical calendar data and historical load", the "historical interaction relationship data among historical weather data, historical calendar data and historical load" is weighted to obtain the "historical weighted interaction relationship data among historical weather data, historical calendar data and historical load".

[0110] According to the attention weight corresponding to the "historical interactive relationship data among historical weather data, historical additional power-related events and historical load", the "historical interactive relationship data among historical weather data, historical additional power-related events and historical load" are weighted to obtain the "historical weighted interactive relationship data among historical weather data, historical additional power-related events and historical load".

[0111] According to the attention weight corresponding to the "historical interaction relationship data among historical calendar data, historical additional power-related events and historical load", the "historical interaction relationship data among historical calendar data, historical additional power-related events and historical load" are weighted to obtain the "historical weighted interaction relationship data among historical calendar data, historical additional power-related events and historical load".

[0112] Obtain the attention weights corresponding to each current interaction relationship data. Weight the corresponding current interaction relationship data according to the attention weights corresponding to each current interaction relationship data to obtain the current weighted interaction relationship data corresponding to each current interaction relationship data.

[0113] For example, the "current interaction relationship data between the current additional electricity-related events and the current load" is weighted according to the attention weight corresponding to the "current interaction relationship data between the current additional electricity-related events and the current load" to obtain the "current weighted interaction relationship data between the current additional electricity-related events and the current load".

[0114] The “current interaction relationship data between the current weather data and the current load” is weighted according to the attention weight corresponding to the “current interaction relationship data between the current weather data and the current load” to obtain the “current weighted interaction relationship data between the current weather data and the current load”.

[0115] The “current interaction relationship data between the current calendar data and the current load” is weighted according to the attention weight corresponding to the “current interaction relationship data between the current calendar data and the current load” to obtain the “current weighted interaction relationship data between the current calendar data and the current load”.

[0116] The “current interactive relationship data between the current electricity price and the current load” is weighted according to the attention weight corresponding to the “current interactive relationship data between the current electricity price and the current load” to obtain the “current weighted interactive relationship data between the current electricity price and the current load”.

[0117] According to the attention weight corresponding to the "current interactive relationship data among the current weather data, the current calendar data and the current load", the "current interactive relationship data among the current weather data, the current calendar data and the current load" is weighted to obtain the "current weighted interactive relationship data among the current weather data, the current calendar data and the current load".

[0118] According to the attention weight corresponding to the "current interactive relationship data among the current weather data, the current additional electricity-related events and the current load", the "current interactive relationship data among the current weather data, the current additional electricity-related events and the current load" are weighted to obtain the "current weighted interactive relationship data among the current weather data, the current additional electricity-related events and the current load".

[0119] According to the attention weight corresponding to the "current interactive relationship data among the current calendar data, the current additional electricity-related events and the current load", the "current interactive relationship data among the current calendar data, the current additional electricity-related events and the current load" are weighted to obtain the "current weighted interactive relationship data among the current calendar data, the current additional electricity-related events and the current load".

[0120] Based on future additional electricity-related events, future weather data, future calendar data, future electricity prices, various historical weighted interaction relationship data (all the historical weighted interaction relationship data mentioned above) and various current weighted interaction relationship data (all the current weighted interaction relationship data mentioned above), the future load of the power grid in the future time period is predicted.

[0121] For example, future additional electricity-related events, future weather data, future calendar data, future user prices, various historical weighted interaction relationship data, and various current weighted interaction relationship data are aggregated to obtain aggregated data. The future load of the power grid in the future time period is predicted based on the aggregated data.

[0122] In this application, the power grid load forecasting model also includes an attention network, which can be used to obtain the attention weights corresponding to the feature vectors corresponding to each historical interaction relationship data.

[0123] For example, for any historical interaction relationship data, the feature vector corresponding to the historical interaction relationship data can be weighted according to the attention weight corresponding to the feature vector corresponding to the historical interaction relationship data to obtain a weighted feature vector corresponding to the historical interaction relationship data. The weighted feature vector corresponding to the historical interaction relationship data is used as input into the prediction network. The same is true for each other historical interaction relationship data. The same is true for each current interaction relationship data.

[0124] For example, the feature vectors corresponding to future additional electricity-related events, the feature vectors corresponding to future weather data, the feature vectors corresponding to future calendar data, the feature vectors corresponding to future electricity prices, the feature vectors corresponding to each historical weighted interaction relationship data, and the feature vectors corresponding to each current weighted interaction relationship data are aggregated to obtain an aggregated vector, and then the aggregated vector is processed using a prediction network to obtain the future load of the power grid in the future time period.

[0125] The prediction network includes a logistic regression function, a normalized exponential function, a fully connected layer or an activation function, such as sigmoid, Softmax or ReLU.

[0126] The prediction network can first use MLP (Multilayer Perceptron) to process the aggregate vector to obtain an intermediate vector, then use the activation function tanh to process the intermediate vector to obtain an activation vector, and then use Softmax, Sigmoid or ReLU to process the activation vector to obtain the future load of the power grid in the future time period.

[0127] For two eigenvectors, if the dimensions of the two eigenvectors are the same, calculating the product between the two eigenvectors includes calculating a vector product or a cross product between the two eigenvectors.

[0128] Among them, the attention mechanism can be used to obtain the contribution of each historical interaction relationship data and each current interaction relationship data to the future load of the power grid in the future time period.

[0129] For example, different attention weights corresponding to each historical interaction relationship data can be obtained, and different attention weights corresponding to each current interaction relationship data can be obtained. For example, the weight of historical interaction relationship data with a greater degree of contribution is appropriately higher, so that historical interaction relationship data with more positive influences contribute more to the future load of the power grid in the future time period; the weight of historical interaction relationship data with less positive influences is appropriately lower, so that historical interaction relationship data with less positive influences contribute less to the future load of the power grid in the future time period.

[0130] In addition, different attention weights corresponding to each current interaction relationship data can be obtained. For example, the weight of the current interaction relationship data with a greater contribution degree is appropriately higher, so that the current interaction relationship data with more positive influence contributes more to the future load of the power grid in the future time period; the weight of the current interaction relationship data with less positive influence is appropriately lower, so that the current interaction relationship data with less positive influence contributes less to the future load of the power grid in the future time period.

[0131] In this way, the present application can use the attention weights corresponding to each historical interaction relationship data to weight each historical interaction relationship data respectively, so as to provide assistance for the accuracy of predicting the future load of the power grid in the future time period through the attention mechanism. For example, the attention mechanism can make full use of each historical interaction relationship data according to the degree of contribution, and use the attention weights corresponding to each current interaction relationship data to weight each current interaction relationship data respectively, so as to provide assistance for the accuracy of predicting the future load of the power grid in the future time period through the attention mechanism. For example, the attention mechanism can make full use of each current interaction relationship data according to the degree of contribution, thereby improving the accuracy of the future load of the power grid in the future time period.

[0132] It should be noted that for the method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this 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 this specification are all optional embodiments, and the actions involved are not necessarily required by this application.

[0133] Reference Figure 2 , shows a device for predicting power grid load of the present application, comprising:

[0134] The first acquisition module 11 is used to obtain the historical load of the power grid in the historical time period before the current moment, and obtain the current load of the power grid in the current time period at the current moment;

[0135] A second acquisition module 12 is configured to acquire historical additional electricity-related events within a power consumption area supplied by the power grid within a historical time period, acquire current additional electricity-related events within the power consumption area within a current time period, and acquire future additional electricity-related events within the power consumption area within a future time period;

[0136] The third acquisition module 13 is used to obtain historical weather data of the power consumption area in a historical time period, obtain current weather data of the power consumption area in a current time period, and obtain future weather data of the power consumption area in a future time period;

[0137] A fourth acquisition module 14 is used to acquire historical calendar data of historical time periods, current calendar data of current time periods, and future calendar data of future time periods;

[0138] A fifth acquisition module 15 is configured to acquire historical electricity prices within a power consumption area supplied by the power grid during historical time periods, acquire current electricity prices within the power consumption area during a current time period, and acquire future electricity prices within the power consumption area during future time periods;

[0139] The prediction module 16 is used to predict the future load of the power grid in a future time period based on historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices.

[0140] In an optional implementation, the prediction module includes:

[0141] A first acquisition unit is used to acquire historical interaction relationship data between historical additional power-related events and historical loads;

[0142] A second acquisition unit is used to acquire historical interaction relationship data between historical weather data and historical load;

[0143] a third acquiring unit, configured to acquire historical interaction relationship data between historical calendar data and historical loads;

[0144] a fourth acquiring unit, configured to acquire historical interactive relationship data between historical electricity prices and historical loads;

[0145] a fifth acquisition unit, configured to acquire historical interaction relationship data among historical weather data, historical calendar data, and historical load;

[0146] a sixth acquisition unit, configured to acquire historical interactive relationship data among historical weather data, historical additional power-related events, and historical load;

[0147] a seventh acquisition unit, configured to acquire historical interactive relationship data among historical calendar data, historical additional power-related events, and historical loads;

[0148] an eighth acquiring unit, configured to acquire current interactive relationship data between a current additional power-related event and a current load;

[0149] a ninth obtaining unit, configured to obtain current interactive relationship data between current weather data and current load;

[0150] a tenth obtaining unit, configured to obtain current interaction relationship data between current calendar data and current load;

[0151] an eleventh obtaining unit, configured to obtain current interactive relationship data between current electricity price and current load;

[0152] a twelfth obtaining unit, configured to obtain current interactive relationship data among current weather data, current calendar data, and current load;

[0153] a thirteenth obtaining unit, configured to obtain current interactive relationship data among current weather data, current additional power-related events, and current load;

[0154] A fourteenth obtaining unit is used to obtain current interactive relationship data among current calendar data, current additional power-related events, and current load;

[0155] The prediction unit is used to predict the future load of the power grid in a future time period based on future additional power-related events, future weather data, future calendar data, future electricity prices, various historical interaction relationship data, and various current interaction relationship data.

[0156] In an optional implementation, the prediction unit includes:

[0157] A first weighting subunit is configured to obtain attention weights corresponding to respective pieces of historical interaction relationship data, and weight the corresponding historical interaction relationship data according to the attention weights corresponding to the respective pieces of historical interaction relationship data to obtain historical weighted interaction relationship data corresponding to the respective pieces of historical interaction relationship data;

[0158] The second weighting subunit is used to obtain the attention weights corresponding to each current interaction relationship data; weight the corresponding current interaction relationship data according to the attention weights corresponding to each current interaction relationship data, to obtain the current weighted interaction relationship data corresponding to each current interaction relationship data;

[0159] The prediction subunit is used to predict the future load of the power grid in a future time period based on future additional power-related events, future weather data, future calendar data, future user prices, various historical weighted interaction relationship data, and various current weighted interaction relationship data.

[0160] In an optional implementation, the prediction subunit is specifically used to: aggregate future additional electricity-related events, future weather data, future calendar data, future user prices, various historical weighted interaction relationship data, and various current weighted interaction relationship data to obtain aggregated data; and predict the future load of the power grid in a future time period based on the aggregated data.

[0161] In this application, the historical load of the power grid in the historical time period before the current moment is obtained, and the current load of the power grid in the current time period at the current moment is obtained; the historical additional electricity-related events in the historical time period within the power consumption area supplied by the power grid are obtained, the current additional electricity-related events in the current time period within the power consumption area are obtained, and the future additional electricity-related events in the future time period within the power consumption area are obtained; the historical weather data of the power consumption area in the historical time period is obtained, the current weather data of the power consumption area in the current time period is obtained, and the future weather data of the power consumption area in the future time period is obtained; the historical calendar data of the historical time period is obtained, and the current time period is obtained. Current calendar data, obtain future calendar data for future time periods; obtain historical electricity prices in historical time periods within the power consumption area supplied by the power grid, obtain current electricity prices in the current time period within the power consumption area, and obtain future electricity prices in future time periods within the power consumption area; predict the future load of the power grid in the future time period based on historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices.

[0162] Through the present application, when predicting the future load of the power grid in a future time period, multiple factors that may affect the future load of the power grid in a future time period are taken into account, such as historical load, current load, historical additional electricity-related events, current additional electricity-related events, future additional electricity-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices, etc. The factors considered are more comprehensive, which can improve the accuracy of the predicted future load of the power grid in a future time period. Secondly, there can be no human participation in the process of predicting the future load of the power grid in a future time period, which reduces labor costs and avoids the problem of low prediction accuracy caused by human experience problems.

[0163] Optionally, an embodiment of the present application also provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and runnable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0164] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the various processes of the above-described method embodiments are implemented and the same technical effects are achieved. To avoid repetition, the details are not described here. The computer-readable storage medium may be, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0165] Figure 3 8 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0166] Reference Figure 3 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0167] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

[0168] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0169] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0170] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also monitor the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0171] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0172] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0173] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can monitor the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800. The sensor assembly 814 can also monitor the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to monitor the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0174] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, an operator network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0175] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0176] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by the processor 820 of the electronic device 800 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0177] Figure 41 is a block diagram of an electronic device 1900 shown in the present application. For example, the electronic device 1900 can be provided as a server.

[0178] Reference Figure 4 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above-described method.

[0179] The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0180] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0181] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0182] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

[0183] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software 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 this application.

[0184] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0185] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0186] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0187] 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.

[0188] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0189] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for predicting power grid load, characterized in that: include: Obtain the historical load of the power grid in the historical time period before the current moment, and obtain the current load of the power grid in the current time period at the current moment; Obtain historical additional electricity-related events within a historical time period within a power consumption area supplied by the power grid, obtain current additional electricity-related events within a current time period within the power consumption area, and obtain future additional electricity-related events within a future time period within the power consumption area; Acquire historical weather data of the power consumption area in a historical time period, acquire current weather data of the power consumption area in a current time period, and acquire future weather data of the power consumption area in a future time period; Get historical calendar data for historical time periods, get current calendar data for the current time period, and get future calendar data for future time periods; Obtaining historical electricity prices within a power consumption area supplied by the power grid during historical time periods, obtaining current electricity prices within the power consumption area during a current time period, and obtaining future electricity prices within the power consumption area during future time periods; The future load of the power grid in a future time period is predicted based on historical load, current load, historical additional power-related events, current additional power-related events, future additional power-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices, and future electricity prices.

2. The method according to claim 1, characterized in that The method of predicting the future load of the power grid in a future time period based on historical load, current load, historical additional power-related events, current additional power-related events, future additional power-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices, and future electricity prices includes: Obtain historical interactive relationship data between historical additional power-related events and historical loads; Obtain historical interactive relationship data between historical weather data and historical load; Obtain historical interaction relationship data between historical calendar data and historical loads; Obtain historical interactive relationship data between historical electricity prices and historical loads; Obtain historical interactive relationship data among historical weather data, historical calendar data, and historical load; Obtain historical interactive relationship data among historical weather data, historical additional power-related events, and historical load; Obtain historical interactive relationship data among historical calendar data, historical additional power-related events, and historical load; Obtaining current interactive relationship data between current additional power-related events and current load; Obtain the current interactive relationship data between current weather data and current load; Get the current interactive relationship data between the current calendar data and the current load; Obtain the current interactive relationship data between the current electricity price and the current load; Obtain the current interactive relationship data among current weather data, current calendar data and current load; Obtain the current interactive relationship data among current weather data, current additional power-related events, and current load; Obtain the current interactive relationship data among the current calendar data, the current additional power-related events, and the current load; The future load of the power grid in a future time period is predicted based on future additional power-related events, future weather data, future calendar data, future electricity prices, various historical interaction relationship data, and various current interaction relationship data.

3. The method according to claim 2, characterized in that The method of predicting the future load of the power grid in a future time period based on future additional electricity-related events, future weather data, future calendar data, future electricity prices, various historical interaction relationship data, and various current interaction relationship data includes: Obtaining the attention weights corresponding to each piece of historical interaction relationship data, and weighting the corresponding historical interaction relationship data according to the attention weights corresponding to each piece of historical interaction relationship data, to obtain the historical weighted interaction relationship data corresponding to each piece of historical interaction relationship data; Obtaining the attention weights corresponding to each current interaction relationship data; weighting the corresponding current interaction relationship data according to the attention weights corresponding to each current interaction relationship data to obtain the current weighted interaction relationship data corresponding to each current interaction relationship data; The future load of the power grid in a future time period is predicted based on future additional power-related events, future weather data, future calendar data, future user prices, various historical weighted interaction relationship data, and various current weighted interaction relationship data.

4. The method according to claim 3, characterized in that The method of predicting the future load of the power grid in a future time period based on future additional power-related events, future weather data, future calendar data, future user prices, various historical weighted interaction relationship data, and various current weighted interaction relationship data includes: Aggregating future additional electricity-related events, future weather data, future calendar data, future user prices, each historical weighted interaction relationship data, and each current weighted interaction relationship data to obtain aggregated data; Predict the future load on the grid for future time periods based on aggregated data.

5. A device for predicting power grid load, characterized in that: include: The first acquisition module is used to obtain the historical load of the power grid in the historical time period before the current moment, and obtain the current load of the power grid in the current time period at the current moment; A second acquisition module is configured to acquire historical additional electricity-related events within a historical time period within a power consumption area supplied by the power grid, acquire current additional electricity-related events within a current time period within the power consumption area, and acquire future additional electricity-related events within a future time period within the power consumption area; A third acquisition module is used to obtain historical weather data of the power consumption area in a historical time period, obtain current weather data of the power consumption area in a current time period, and obtain future weather data of the power consumption area in a future time period; A fourth acquisition module is used to acquire historical calendar data of historical time periods, current calendar data of current time periods, and future calendar data of future time periods; A fifth acquisition module is configured to acquire historical electricity prices within a power consumption area supplied by the power grid during historical time periods, acquire current electricity prices within the power consumption area during a current time period, and acquire future electricity prices within the power consumption area during future time periods; The prediction module is used to predict the future load of the power grid in a future time period based on historical load, current load, historical additional power-related events, current additional power-related events, future additional power-related events, historical weather data, current weather data, future weather data, historical calendar data, current calendar data, future calendar data, historical electricity prices, current electricity prices and future electricity prices.

6. The device according to claim 5, characterized in that The prediction module includes: A first acquisition unit is used to acquire historical interaction relationship data between historical additional power-related events and historical loads; A second acquisition unit is used to acquire historical interaction relationship data between historical weather data and historical load; a third acquiring unit, configured to acquire historical interaction relationship data between historical calendar data and historical loads; a fourth acquiring unit, configured to acquire historical interactive relationship data between historical electricity prices and historical loads; a fifth acquisition unit, configured to acquire historical interaction relationship data among historical weather data, historical calendar data, and historical load; a sixth acquisition unit, configured to acquire historical interactive relationship data among historical weather data, historical additional power-related events, and historical load; a seventh acquisition unit, configured to acquire historical interactive relationship data among historical calendar data, historical additional power-related events, and historical loads; an eighth acquiring unit, configured to acquire current interactive relationship data between a current additional power-related event and a current load; a ninth obtaining unit, configured to obtain current interactive relationship data between current weather data and current load; a tenth obtaining unit, configured to obtain current interaction relationship data between current calendar data and current load; an eleventh obtaining unit, configured to obtain current interactive relationship data between current electricity price and current load; a twelfth obtaining unit, configured to obtain current interactive relationship data among current weather data, current calendar data, and current load; a thirteenth obtaining unit, configured to obtain current interactive relationship data among current weather data, current additional power-related events, and current load; A fourteenth obtaining unit is used to obtain current interactive relationship data among current calendar data, current additional power-related events, and current load; The prediction unit is used to predict the future load of the power grid in a future time period based on future additional power-related events, future weather data, future calendar data, future electricity prices, various historical interaction relationship data, and various current interaction relationship data.

7. The device according to claim 6, characterized in that The prediction unit includes: A first weighting subunit is configured to obtain attention weights corresponding to respective pieces of historical interaction relationship data, and weight the corresponding historical interaction relationship data according to the attention weights corresponding to the respective pieces of historical interaction relationship data to obtain historical weighted interaction relationship data corresponding to the respective pieces of historical interaction relationship data; The second weighting subunit is used to obtain the attention weights corresponding to each current interaction relationship data; weight the corresponding current interaction relationship data according to the attention weights corresponding to each current interaction relationship data, to obtain the current weighted interaction relationship data corresponding to each current interaction relationship data; The prediction subunit is used to predict the future load of the power grid in a future time period based on future additional power-related events, future weather data, future calendar data, future user prices, various historical weighted interaction relationship data, and various current weighted interaction relationship data.

8. The device according to claim 7, characterized in that The prediction subunit is specifically used to: aggregate future additional electricity-related events, future weather data, future calendar data, future user prices, various historical weighted interaction relationship data, and various current weighted interaction relationship data to obtain aggregated data; and predict the future load of the power grid in a future time period based on the aggregated data.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 4 when executed by the processor.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which implements the method according to any one of claims 1 to 4 when executed by a processor.

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