Power supply and demand prediction method and device, storage medium and electronic device

Abstract

The invention discloses a power supply and demand prediction method, and the method comprises the steps: obtaining power dispatching data, static feature data and dynamic feature data; performing feature extraction based on the power dispatching data to determine a first vector representation corresponding to the power dispatching data, performing feature extraction based on the static feature data to determine a second vector representation corresponding to the static feature data, and performing feature extraction based on the dynamic feature data to determine a third vector representation corresponding to the dynamic feature data; fusing the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation; and on the basis of the target vector representation, predicting power supply and demand of different provinces to obtain a power supply and demand prediction result. And the power supply and demand can be accurately predicted.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and more specifically, to a method and device for predicting power supply and demand, a storage medium, and an electronic device. Background Art

[0002] Electricity supply and demand forecasting is the basis for power system planning, dispatching, operation and management. Accurate forecasting of electricity supply and demand is of great significance to ensuring the safe and economic operation of the power system. Electricity supply and demand forecasting is generally divided into two categories: electricity consumption forecasting and electricity supply and demand balance forecasting. Electricity consumption forecasting mainly refers to the forecast of total electricity consumption in a certain period in the future, while electricity supply and demand balance forecasting mainly refers to the forecast of the balance state of electricity supply and demand in a certain period.

[0003] Electricity supply and demand are affected by many factors, such as weather conditions, economic activities, and user behavior. The data generated by these factors have different sources and formats, such as weather data from meteorological stations, economic indicators from economic statistics agencies, and electricity consumption data from households and industrial users. This multi-source heterogeneity makes data integration and feature extraction a complex task.

[0004] At present, electricity supply and demand forecasting is mainly based on a single forecasting model, such as the support vector machine model, the genetic algorithm model, etc. Since electricity supply and demand are affected by many factors, a single forecasting model cannot provide accurate forecasting results.

[0005] Therefore, how to accurately predict electricity supply and demand has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] The embodiments of the present application provide a method and device for predicting power supply and demand, a storage medium, and an electronic device to at least solve the problem of low accuracy of power supply and demand prediction in the related art.

[0007] According to an embodiment of the embodiments of the present application, a method for predicting power supply and demand is provided, including: obtaining power dispatching data, static feature data and dynamic feature data; wherein the power dispatching data is data used to characterize the power allocation situation between different provinces; the static feature data is data used to characterize the energy structure situation of the target city associated with the different provinces; the dynamic feature data is data used to characterize the regional power consumption situation of different parks in the target city; performing feature extraction based on the power dispatching data to determine a first vector representation corresponding to the power dispatching data, performing feature extraction based on the static feature data to determine a second vector representation corresponding to the static feature data, and performing feature extraction based on the dynamic feature data to determine a third vector representation corresponding to the dynamic feature data; fusing the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation; based on the target vector representation, predicting the power supply and demand of the different provinces to obtain a power supply and demand prediction result.

[0008] In an exemplary embodiment, the feature extraction based on the power dispatch data to determine the first vector representation corresponding to the power dispatch data includes: cleaning the power dispatch data to obtain the cleaned power dispatch data; performing time series analysis on the cleaned power dispatch data to determine the time series characteristics of the power dispatch data changing over time; and performing spatial analysis on the cleaned power dispatch data to obtain the geographical distribution characteristics corresponding to the power dispatch data; fusing the time series characteristics, the geographical distribution characteristics and the external environment characteristics to obtain the first target characteristics; and performing vector representation on the first target characteristics to obtain the first vector representation.

[0009] In an exemplary embodiment, the feature extraction based on the static feature data to determine the second vector representation corresponding to the static feature data includes: cleaning the static feature data to obtain cleaned static feature data; extracting energy structure information from the cleaned static feature data, and calculating an energy diversity index based on the energy structure information; wherein the energy diversity index is a parameter used to characterize the degree of energy diversification; and feature encoding the energy diversity index to determine the second vector representation corresponding to the static feature data.

[0010] In an exemplary embodiment, the feature extraction based on the dynamic feature data to determine the third vector representation corresponding to the dynamic feature data includes: decomposing the dynamic feature data to obtain a trend item, a seasonal item and a noise item; and encoding the trend item, the seasonal item and the noise item respectively to obtain a third vector representation corresponding to the dynamic feature data.

[0011] In an exemplary embodiment, the fusing of the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation includes: splicing the first vector representation, the second vector representation and the third vector representation in a preset splicing order to obtain a long vector containing all feature information; and obtaining a target vector representation based on the long vector.

[0012] In an exemplary embodiment, the power supply and demand of the different provinces are predicted based on the target vector representation to obtain the power supply and demand prediction result, including: inputting the target vector representation into a pre-trained power supply and demand prediction model, and predicting the power supply and demand of the different provinces based on the power supply and demand prediction model to obtain the power supply and demand prediction result, wherein the power supply and demand prediction model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: historical power dispatch data with added weights, historical static feature data, and historical dynamic feature data.

[0013] According to another embodiment of the embodiment of the present application, there is also provided an electric power supply and demand forecasting device, including: an acquisition module, used to acquire electric power dispatching data, static feature data and dynamic feature data; wherein the electric power dispatching data is data used to characterize the electric power allocation situation between different provinces; the static feature data is data used to characterize the energy structure situation of the target city associated with the different provinces; the dynamic feature data is data used to characterize the regional power consumption situation of different parks in the target city; a determination module, used to perform feature extraction based on the electric power dispatching data, determine a first vector representation corresponding to the electric power dispatching data, perform feature extraction based on the static feature data, determine a second vector representation corresponding to the static feature data, and perform feature extraction based on the dynamic feature data, determine a third vector representation corresponding to the dynamic feature data; a fusion module, used to fuse the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation; a prediction module, used to predict the electric power supply and demand of the different provinces based on the target vector representation to obtain an electric power supply and demand prediction result.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above method when running.

[0015] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method through the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0017] In an embodiment of the present application, power dispatching data, static feature data, and dynamic feature data are obtained, and by determining a first vector representation of the power dispatching data, a second vector representation of the static feature data is determined, and a third vector representation corresponding to the dynamic feature data is determined. The first vector representation, the second vector representation, and the third vector representation are merged into a target vector representation, which integrates the macro characteristics of power dispatching, the energy structure background of the target city, and the power consumption at the park level, and realizes a multi-level power supply and demand forecast from macro to micro. The use of the target vector representation for power supply and demand forecasting effectively improves the forecast accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal of a power supply and demand forecasting method according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for generating a power supply and demand forecasting method according to an embodiment of the present application;

[0021] Figure 3 It is a structural block diagram of an electric power supply and demand prediction device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices; "plurality" means two or more.

[0024] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or similar computing device or cloud platform or independent physical server or software platform, wherein the above software platform is run through one or more servers. Taking running on a computer terminal as an example, Figure 1 is a hardware structure block diagram of a method for predicting power supply and demand according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 and memory 103 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0025] The memory 103 can be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the methods in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 103, that is, the above-mentioned methods are implemented. The memory 103 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 103 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0026] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0027] In this embodiment, a method for predicting power supply and demand is provided, which is applied to the above-mentioned computer device. Figure 2 : is a flow chart of a method for predicting power supply and demand according to an embodiment of the present application, the process comprising the following steps:

[0028] Step S202, obtaining power dispatching data, static characteristic data and dynamic characteristic data; wherein the power dispatching data is data used to characterize the power dispatching situation between different provinces; the static characteristic data is data used to characterize the energy structure situation of the target city associated with the different provinces; the dynamic characteristic data is data used to characterize the regional power consumption situation of different parks in the target city;

[0029] Step S204, performing feature extraction based on the power dispatching data to determine a first vector representation corresponding to the power dispatching data, performing feature extraction based on the static feature data to determine a second vector representation corresponding to the static feature data, and performing feature extraction based on the dynamic feature data to determine a third vector representation corresponding to the dynamic feature data;

[0030] Step S206, fusing the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation;

[0031] Step S208: Based on the target vector representation, predict the power supply and demand of the different provinces to obtain a power supply and demand prediction result.

[0032] In an embodiment of the present application, power dispatching data, static feature data, and dynamic feature data are obtained, and by determining a first vector representation of the power dispatching data, a second vector representation of the static feature data is determined, and a third vector representation corresponding to the dynamic feature data is determined. The first vector representation, the second vector representation, and the third vector representation are merged into a target vector representation, which integrates the macro characteristics of power dispatching, the energy structure background of the target city, and the power consumption at the park level, and realizes a multi-level power supply and demand forecast from macro to micro. Using the target vector representation to forecast power supply and demand effectively improves the prediction accuracy.

[0033] In an exemplary embodiment, the feature extraction based on the power dispatch data to determine the first vector representation corresponding to the power dispatch data includes: cleaning the power dispatch data to obtain the cleaned power dispatch data; performing time series analysis on the cleaned power dispatch data to determine the time series characteristics of the power dispatch data changing over time; and performing spatial analysis on the cleaned power dispatch data to obtain the geographical distribution characteristics corresponding to the power dispatch data; fusing the time series characteristics, the geographical distribution characteristics and the external environment characteristics to obtain the first target characteristics; and performing vector representation on the first target characteristics to obtain the first vector representation.

[0034] It should be noted that power dispatching data usually contains diverse information such as inter-provincial power transmission, power generation plans, dispatching instructions, etc., and may have problems such as missing values, outliers and duplicate records. Data cleaning is the basis for ensuring the accuracy of subsequent analysis.

[0035] The data cleaning process can include: missing value processing: filling missing values ​​through interpolation (such as time series interpolation) or prediction methods based on historical data; outlier detection: using machine learning techniques (such as Isolation Forest) to identify and process outliers. Data consistency check: ensuring that the timestamp, unit and other information of the data are consistent, excluding duplicate records, and maintaining the integrity of the data set.

[0036] Time series analysis refers to identifying the patterns and trends of power dispatch data over time, providing feature support in the time dimension for the prediction model.

[0037] Spatial analysis refers to the distribution characteristics of power dispatch data in geographic space, providing information at the regional level. When conducting spatial analysis, the geographic location information (such as province) in the dispatch data can be converted into geographic coordinates to facilitate spatial analysis.

[0038] The external environment feature integration can include external data such as weather forecasts, holiday information, and market activities, which have a direct impact on power dispatch. Use correlation analysis, principal component analysis (PCA) and other techniques to screen out the most relevant features, reduce computational complexity, and avoid overfitting. The first target feature set includes the time law of power dispatch, geographical distribution characteristics, and the influence of external environmental factors, forming a multi-level feature description.

[0039] In an exemplary embodiment, the feature extraction based on the static feature data to determine the second vector representation corresponding to the static feature data includes: cleaning the static feature data to obtain cleaned static feature data; extracting energy structure information from the cleaned static feature data, and calculating an energy diversity index based on the energy structure information; wherein the energy diversity index is a parameter for characterizing the degree of energy diversification; and feature encoding the energy diversity index to determine the second vector representation corresponding to the static feature data.

[0040] Among them, static characteristic data, such as energy structure, geographical location, population density, industrial structure, etc., reflect the inherent attributes of the city and have a long-term and stable impact on electricity demand.

[0041] Data processing can include: Removing duplicate data: Checking whether there are duplicate records in the static feature data, and removing duplicates based on the unique identifier of the data (such as city ID, year, etc.). Filling missing values: For missing static feature data, use a reasonable filling strategy, such as filling based on the average value of data from adjacent years. Data format unification: Convert all static feature data into a consistent format and unit to facilitate model understanding and processing.

[0042] Energy structure information is used to analyze the energy structure of the target city and identify the proportion and diversity of different energy types (such as coal, natural gas, hydropower, wind power, solar energy, etc.) in the energy supply. Information about energy supply is extracted from static feature data sets, which may include annual production, power generation or consumption of various energy sources. Energy classification: Classify data according to energy source and type, such as renewable energy, fossil energy, etc.

[0043] The energy diversity index is used to quantify the diversity of the energy structure and evaluate the stability and sustainability of energy supply. The higher the energy diversity index, the richer the energy structure and the lower the risk of dependence. The calculated energy diversity index is standardized to obtain the vector representation of the energy diversity index.

[0044] In the above embodiment, the second vector representation based on static feature data extraction can fully reflect the inherent characteristics of the target city in terms of energy supply through data cleaning, energy structure information extraction, energy diversity index calculation and feature coding. This vector representation not only covers the quantitative indicators of energy diversity, but may also contain other static features related to the energy structure, providing a stable and rich long-term background information for the power supply and demand model, which helps to improve the accuracy and reliability of the forecast, especially in evaluating long-term energy planning and power demand elasticity.

[0045] In an exemplary embodiment, the feature extraction is performed based on the dynamic feature data to determine a third vector representation corresponding to the dynamic feature data, including: decomposing the dynamic feature data to obtain a trend item, a seasonal item, and a noise item; and encoding the trend item, the seasonal item, and the noise item respectively to obtain a third vector representation corresponding to the dynamic feature data.

[0046] It can be understood that data decomposition decomposes the time series of dynamic feature data into trend terms, seasonal terms, and noise terms. The trend term reflects the long-term trend of the time series, such as the year-on-year increase or decrease in electricity demand. The seasonal term captures the cyclical changes in the data, such as the cyclical fluctuations in electricity demand throughout the year or the seasonal pattern of power generation with weather changes. The noise term separates the random fluctuations in the time series, that is, the part that cannot be explained by the trend and seasonality, which usually represents short-term or sudden fluctuations.

[0047] Convert the decomposed trend term, seasonal term and noise term into a numerical vector representation to obtain a third vector representation. Trend term encoding: The trend term can be encoded using linear regression, polynomial fitting and other methods to convert trend changes into numerical representations. Seasonal term encoding: Use Fourier transform and other techniques to convert seasonal patterns into frequency domain representations, or directly use seasonal factors (such as months of the year, days of the week) for encoding. Noise term encoding: Statistically describe the noise term, such as calculating the standard deviation, variance, etc., to quantify the degree of random fluctuations.

[0048] In the above embodiment, the characteristics reflecting the long-term trend, seasonal law and random fluctuation of the time series can be extracted from the dynamic feature data and converted into a numerical vector representation. This process not only helps the model to model the complexity of time series data, but also reduces the impact of noise on the prediction results to a certain extent, thereby improving the accuracy and stability of power supply and demand prediction. The generation of the third vector representation realizes a refined description of the operating status of the power system and is an important part of the input of the power supply and demand prediction model.

[0049] In an exemplary embodiment, the fusing of the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation includes: splicing the first vector representation, the second vector representation and the third vector representation in a preset splicing order to obtain a long vector containing all feature information; and obtaining a target vector representation based on the long vector.

[0050] Specifically, the first vector representation, the second vector representation and the third vector representation respectively extracted from the power dispatching data, the static feature data and the dynamic feature data are integrated to form a long vector containing all key information.

[0051] The three vector representations are spliced ​​in a preset order (such as power dispatch data first, then static feature data, and finally dynamic feature data). This order reflects the logical process of feature extraction, that is, from macro to micro, from long-term to short-term. Dimension alignment: Before splicing, ensure that the dimensions of all vector representations can be adapted. If there is a dimension inconsistency, add a zero vector or perform feature dimensionality reduction to enable splicing. Data type conversion: If the data types in the vector representation are inconsistent (such as one is an integer and the other is a floating point number), data type conversion is required to ensure the consistency of the data type in the spliced ​​long vector. Finally, a long vector containing the features of power dispatch data, static feature data, and dynamic feature data is obtained. This vector integrates multi-level information from macro, meso to micro.

[0052] The concatenated long vectors are further processed to generate target vector representations to optimize model input and improve prediction efficiency and accuracy.

[0053] In an exemplary embodiment, the power supply and demand of the different provinces are predicted based on the target vector representation to obtain the power supply and demand prediction result, including: inputting the target vector representation into a pre-trained power supply and demand prediction model, and predicting the power supply and demand of the different provinces based on the power supply and demand prediction model to obtain the power supply and demand prediction result, wherein the power supply and demand prediction model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: historical power dispatch data with added weights, historical static feature data, and historical dynamic feature data.

[0054] It should be noted that when training the power supply and demand forecasting model, a suitable machine learning or deep learning model can be selected as the power supply and demand forecasting model. For example, a long short-term memory network (LSTM), a spatiotemporal fusion transformer (TFT) or other sequence forecasting models can be used. Data weighting and integration: weight the historical power dispatching data, historical static feature data and historical dynamic feature data to ensure that their relative importance is reflected in the model training. The weighting strategy can be determined automatically based on domain knowledge or through model learning. Training data preparation: use multiple groups of historical data for model training, each group of data includes at least weighted historical power dispatching data, historical static feature data and historical dynamic feature data to ensure that the model can learn the power supply and demand patterns from different dimensions. Model training: train the prepared data set through a machine learning algorithm to optimize the model parameters to minimize the prediction error and improve the prediction accuracy. A prediction model that can accurately predict the power supply and demand based on the target vector representation is obtained.

[0055] Use the pre-trained power supply and demand forecasting model to predict the power supply and demand in different provinces based on the target vector representation. Take the target vector representation as the input of the model. The target vector representation is an optimized vector representation that integrates the power dispatching data, static feature data, and dynamic feature data information. Model prediction: Input the target vector representation into the power supply and demand forecasting model, and the model predicts the power supply and demand in different provinces based on the rules learned internally. Result interpretation: The forecast results output by the model may be one or more numerical values, representing the predicted power demand, supply, or supply-demand ratio, etc. Depending on the specific task requirements, these forecast results may need to be further interpreted and transformed to facilitate the understanding and decision-making of power dispatchers.

[0056] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an 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 a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0057] The present application also provides a structural block diagram of a power supply and demand prediction device. Figure 3 is a structural block diagram of a power supply and demand prediction device according to an embodiment of the present application; Figure 3 As shown, including:

[0058] The acquisition module 32 is used to acquire power dispatching data, static characteristic data and dynamic characteristic data; wherein the power dispatching data is data used to characterize the power dispatching situation between different provinces; the static characteristic data is data used to characterize the energy structure situation of the target city associated with the different provinces; the dynamic characteristic data is data used to characterize the regional power consumption situation of different parks in the target city;

[0059] a determination module 34, configured to perform feature extraction based on the power dispatching data to determine a first vector representation corresponding to the power dispatching data, perform feature extraction based on the static feature data to determine a second vector representation corresponding to the static feature data, and perform feature extraction based on the dynamic feature data to determine a third vector representation corresponding to the dynamic feature data;

[0060] A fusion module 36, configured to fuse the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation;

[0061] The prediction module 38 is used to predict the power supply and demand of the different provinces based on the target vector representation to obtain the power supply and demand prediction result.

[0062] Through the above device, power dispatching data, static feature data and dynamic feature data are obtained, and the first vector representation of the power dispatching data is determined, the second vector representation of the static feature data is determined, and the third vector representation corresponding to the dynamic feature data is determined. The first vector representation, the second vector representation and the third vector representation are integrated into a target vector representation, which integrates the macro characteristics of power dispatching, the energy structure background of the target city and the power consumption at the park level, and realizes multi-level power supply and demand forecasting from macro to micro. The use of target vector representation for power supply and demand forecasting effectively improves the forecast accuracy.

[0063] In an exemplary embodiment, the determination module 34 is further used to clean the power dispatch data to obtain cleaned power dispatch data; perform time series analysis on the cleaned power dispatch data to determine the time series characteristics of the power dispatch data changing over time; and perform spatial analysis on the cleaned power dispatch data to obtain the geographical distribution characteristics corresponding to the power dispatch data; integrate the time series characteristics, the geographical distribution characteristics and the external environment characteristics to obtain the first target characteristics; and perform vector representation on the first target characteristics to obtain a first vector representation.

[0064] In an exemplary embodiment, the determination module 34 is also used to clean the static feature data to obtain cleaned static feature data; extract the energy structure information in the cleaned static feature data, and calculate the energy diversity index based on the energy structure information; wherein the energy diversity index is a parameter used to characterize the degree of energy diversification; feature encode the energy diversity index to determine a second vector representation corresponding to the static feature data.

[0065] In an exemplary embodiment, the determination module 34 is further used to decompose the dynamic feature data to obtain a trend item, a seasonal item and a noise item; and encode the trend item, the seasonal item and the noise item respectively to obtain a third vector representation corresponding to the dynamic feature data.

[0066] In an exemplary embodiment, the fusion module 36 is further used to splice the first vector representation, the second vector representation and the third vector representation in a preset splicing order to obtain a long vector containing all feature information; and based on the long vector, obtain a target vector representation.

[0067] In an exemplary embodiment, the prediction module 38 is also used to input the target vector representation into a pre-trained electricity supply and demand prediction model, predict the electricity supply and demand of the different provinces based on the electricity supply and demand prediction model, and obtain the electricity supply and demand prediction result, wherein the electricity supply and demand prediction model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: historical power dispatch data with added weights, historical static feature data, and historical dynamic feature data.

[0068] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0069] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0070] S1, obtaining power dispatching data, static characteristic data and dynamic characteristic data; wherein the power dispatching data is data used to characterize the power dispatching situation between different provinces; the static characteristic data is data used to characterize the energy structure situation of the target city associated with the different provinces; the dynamic characteristic data is data used to characterize the regional power consumption situation of different parks in the target city;

[0071] S2, performing feature extraction based on the power dispatching data to determine a first vector representation corresponding to the power dispatching data, performing feature extraction based on the static feature data to determine a second vector representation corresponding to the static feature data, and performing feature extraction based on the dynamic feature data to determine a third vector representation corresponding to the dynamic feature data;

[0072] S3, fusing the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation;

[0073] S4, based on the target vector representation, predicting the power supply and demand of the different provinces to obtain a power supply and demand prediction result.

[0074] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0075] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0076] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0077] S1, obtaining power dispatching data, static characteristic data and dynamic characteristic data; wherein the power dispatching data is data used to characterize the power dispatching situation between different provinces; the static characteristic data is data used to characterize the energy structure situation of the target city associated with the different provinces; the dynamic characteristic data is data used to characterize the regional power consumption situation of different parks in the target city;

[0078] S2, performing feature extraction based on the power dispatching data to determine a first vector representation corresponding to the power dispatching data, performing feature extraction based on the static feature data to determine a second vector representation corresponding to the static feature data, and performing feature extraction based on the dynamic feature data to determine a third vector representation corresponding to the dynamic feature data;

[0079] S3, fusing the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation;

[0080] S4, based on the target vector representation, predicting the power supply and demand of the different provinces to obtain a power supply and demand prediction result.

[0081] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0082] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0083] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0084] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0085] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0086] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0087] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for predicting power supply and demand, characterized in that: include: Obtaining power dispatching data, static feature data, and dynamic feature data; wherein the power dispatching data is data used to characterize the power dispatching situation between different provinces; the static feature data is data used to characterize the energy structure situation of the target city associated with the different provinces; the dynamic feature data is data used to characterize the regional power consumption situation of different parks in the target city; Performing feature extraction based on the power dispatching data to determine a first vector representation corresponding to the power dispatching data, performing feature extraction based on the static feature data to determine a second vector representation corresponding to the static feature data, and performing feature extraction based on the dynamic feature data to determine a third vector representation corresponding to the dynamic feature data; fusing the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation; Based on the target vector representation, the power supply and demand of the different provinces are predicted to obtain a power supply and demand prediction result.

2. The method according to claim 1, characterized in that The extracting features based on the electric power dispatching data and determining a first vector representation corresponding to the electric power dispatching data includes: Cleaning the power dispatching data to obtain cleaned power dispatching data; Performing time series analysis on the cleaned power dispatching data to determine the time series characteristics of the power dispatching data changing over time; and performing spatial analysis on the cleaned power dispatching data to obtain the geographical distribution characteristics corresponding to the power dispatching data; The time series feature, the geographical distribution feature and the external environment feature are integrated to obtain a first target feature; Perform vector representation on the first target feature to obtain a first vector representation.

3. The method according to claim 1, characterized in that The extracting features based on the static feature data and determining a second vector representation corresponding to the static feature data includes: Cleaning the static feature data to obtain cleaned static feature data; Extracting energy structure information from the cleaned static feature data, and calculating an energy diversity index based on the energy structure information; wherein the energy diversity index is a parameter used to characterize the degree of energy diversification; Feature encoding is performed on the energy diversity index to determine a second vector representation corresponding to the static feature data.

4. The method according to claim 1, characterized in that The extracting features based on the dynamic feature data and determining a third vector representation corresponding to the dynamic feature data includes: Decomposing the dynamic characteristic data to obtain a trend term, a seasonal term and a noise term; The trend term, seasonal term and noise term are encoded respectively to obtain a third vector representation corresponding to the dynamic feature data.

5. The method according to claim 1, characterized in that The fusing the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation includes: splicing the first vector representation, the second vector representation and the third vector representation according to a preset splicing order to obtain a long vector containing all feature information; Based on the long vector, a target vector representation is obtained.

6. The method according to claim 1, characterized in that The power supply and demand of the different provinces are predicted based on the target vector representation to obtain the power supply and demand prediction result, including: The target vector representation is input into a pre-trained electricity supply and demand forecasting model, and the electricity supply and demand of the different provinces are predicted based on the electricity supply and demand forecasting model to obtain an electricity supply and demand forecasting result, wherein the electricity supply and demand forecasting model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: historical power dispatching data with added weights, historical static feature data, and historical dynamic feature data.

7. A power supply and demand forecasting device, characterized in that: include: An acquisition module, used to acquire power dispatching data, static feature data and dynamic feature data; wherein the power dispatching data is data used to characterize the power dispatching situation between different provinces; the static feature data is data used to characterize the energy structure situation of the target city associated with the different provinces; the dynamic feature data is data used to characterize the regional power consumption situation of different parks in the target city; a determination module, configured to perform feature extraction based on the power dispatching data to determine a first vector representation corresponding to the power dispatching data, perform feature extraction based on the static feature data to determine a second vector representation corresponding to the static feature data, and perform feature extraction based on the dynamic feature data to determine a third vector representation corresponding to the dynamic feature data; a fusion module, configured to fuse the first vector representation, the second vector representation and the third vector representation to obtain a target vector representation; The prediction module is used to predict the power supply and demand of the different provinces based on the target vector representation to obtain the power supply and demand prediction result.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, characterized in that The computer program product comprises a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is performed.

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