Regional electricity consumption prediction method and device, medium and equipment
By acquiring and correcting historical electricity consumption data, establishing a gray prediction model and optimizing the prediction results, the error problem of traditional electricity consumption prediction models in complex electricity consumption situations is solved, and a higher precision electricity consumption prediction is achieved.
Patent Information
- Application Number
- CN202411330636.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-24
- Publication Date
- 2024-12-24
AI Technical Summary
The traditional electricity consumption prediction model cannot meet the high-precision prediction needs of each subject's electricity consumption under complex electricity consumption, and there are large errors.
By obtaining historical electricity consumption data in the target area, correcting the data to reduce the impact of economic factors, establishing a gray electricity consumption prediction model, and optimizing the prediction results using the comprehensive impact coefficient to generate accurate electricity consumption predictions.
It improves the prediction accuracy of regional electricity consumption and meets the high-precision electricity consumption needs of each entity.
Smart Images
Figure CN119180379A_ABST
Abstract
Description
[Technical field]
[0001] The present invention relates to the field of power system power consumption analysis, and in particular to a method, device, medium and equipment for predicting regional power consumption. [Background technology]
[0002] In recent years, with the rapid development of science and technology and the continuous improvement of residents' personal economic capabilities, more and more electrical products have appeared in residents' daily lives, such as electronic products, hybrid electric vehicles, pure electric vehicles, etc., providing a more convenient and comfortable living environment and promoting the improvement of residents' living standards. At the same time, with the surge in the number of electrical products, the electricity consumption of different electricity users, such as residents, corporate users, and commercial users, has also shown a significant increase. Sustainable management of energy and efficient economic operation have become more important, which constitutes a coordination problem between electricity consumption demand and social and economic development. On the basis of ensuring the electricity consumption of various entities, improving electricity efficiency is a major response measure, which reduces the pollution of power generation to the environment, improves the safe and stable operation of the power system, and contributes to the sustainable development of society.
[0003] At the same time, electricity consumption forecasting is also crucial in power security and power marketing. Accurate electricity consumption forecasting can provide important data support for the rational dispatch of power resources and regional marketing of power customers. The traditional electricity consumption forecasting model and system still often have significant fluctuations in prediction errors in practical applications. Its prediction model is not completely matched with today's complex electricity consumption situation, resulting in the inability to meet the high-precision prediction needs of various entities. [Summary of the invention]
[0004] The present invention provides a method, device, medium and equipment for predicting regional electricity consumption, which solve the above-mentioned technical problems.
[0005] A first aspect of an embodiment of the present invention provides a method for predicting regional electricity consumption, comprising the following steps:
[0006] Step 1: Acquire first historical electricity consumption data of a target area, wherein the first historical electricity consumption data includes actual electricity consumption of different electricity users in the target area during a target period;
[0007] Step 2, obtaining elasticity coefficients of electricity consumption of different entities affected by economic factors during the target period, and correcting the first historical electricity consumption data according to the elasticity coefficients to generate second historical electricity consumption data;
[0008] Step 3: Process the second historical electricity consumption data to generate accumulation sequences corresponding to different electricity users, and generate a gray prediction model for electricity consumption of each electricity user based on the accumulation sequences;
[0009] Step 4, generating a predicted power consumption of each power user in a future time period based on the power consumption grey prediction model;
[0010] Step 5, querying a preset coefficient table to obtain comprehensive influence coefficients corresponding to different electricity users, and using the comprehensive influence coefficients to optimize the predicted electricity consumption of each electricity user;
[0011] Step 6: Generate the total predicted electricity consumption of all entities in the target area in the future time period according to the optimization result.
[0012] A second aspect of an embodiment of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the method for predicting regional electricity consumption described above is implemented.
[0013] A third aspect of an embodiment of the present invention provides a regional electricity consumption prediction device, including a computer-readable storage medium and a processor, wherein the processor implements the steps of the above-mentioned regional electricity consumption prediction method when executing a computer program on the computer-readable storage medium.
[0014] A fourth aspect of an embodiment of the present invention provides a device for predicting regional electricity consumption, including an acquisition module, a correction module, a construction module, a prediction module, an optimization module, and a calculation module.
[0015] The acquisition module is used to acquire first historical electricity consumption data of the target area, wherein the first historical electricity consumption data includes actual electricity consumption of different electricity users in the target area during the target period;
[0016] The correction module is used to obtain the elasticity coefficient of the electricity consumption of different entities affected by economic factors during the target period, and to correct the first historical electricity consumption data according to the elasticity coefficient to generate second historical electricity consumption data;
[0017] The construction module is used to process the second historical electricity consumption data to generate accumulation sequences corresponding to different electricity users, and generate a gray prediction model for electricity consumption of each electricity user based on the accumulation sequences;
[0018] The prediction module is used to generate the predicted power consumption of each power user in a future time period based on the power consumption grey prediction model;
[0019] The optimization module is used to query the preset coefficient table, obtain the comprehensive influence coefficients corresponding to different electricity users, and use the comprehensive influence coefficients to optimize the predicted power consumption of each electricity user;
[0020] The calculation module is used to generate the total predicted power consumption of all entities in the target area in the future time period according to the optimization result.
[0021] The beneficial effects of the present invention are as follows: the present invention provides a method, device, medium and equipment for predicting regional electricity consumption, which first collects historical electricity consumption data of different electricity users, corrects them to reduce the impact of economic factors, such as GDP indicators, and then establishes gray prediction models for different electricity users based on the corrected benchmark data and performs the first electricity consumption prediction, and optimizes the first prediction results using a comprehensive influence coefficient, thereby obtaining accurate prediction results for different electricity users, further improving the prediction accuracy of regional electricity consumption.
[0022] In order to make the above-mentioned objects, features and advantages of the invention more obvious and easy to understand, the preferred embodiments of the present invention are specifically cited below and described in detail with reference to the attached drawings.
Brief Description of the Drawings
[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.
[0024] Figure 1 is a flow chart of a method for predicting regional electricity consumption provided in Example 1;
[0025] Figure 2 is a schematic diagram of the structure of a device for predicting regional electricity consumption provided in Example 2;
[0026] Figure 3 This is a schematic diagram of the structure of the regional electricity consumption prediction device provided in Example 3. [Specific implementation method]
[0027] In the following description, the specific embodiments of the present application will be described with reference to the steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be mentioned several times as being performed by a computer, and the computer execution referred to herein includes the operation of a computer processing unit by an electronic signal representing data in a structured form. This operation converts the data or maintains it at a location in the memory system of the computer, which can be reconfigured or otherwise change the operation of the computer in a manner familiar to testers in the field. The data structure maintained by the data is a physical location in the memory, which has specific characteristics defined by the data format. However, the principles of the present application are described in the above text, which does not represent a limitation, and testers in the field will understand that the various steps and operations described below can also be implemented in hardware.
[0028] The principles of the present application may be operated using many other general or specific purpose computing, communication environments or configurations. Examples of well-known computing systems, environments and configurations suitable for use with the present application may include, but are not limited to, handheld phones, personal computers, servers, multiprocessor systems, microcomputer-based systems, mainframe computers, and distributed computing environments, including any of the above systems or devices.
[0029] The terms "first", "second", and "third" in this application are used to distinguish different objects rather than to describe a specific order. In addition, the terms "include", "have", and any variations thereof are intended to cover non-exclusive inclusions.
[0030] The processing device may be a server, a physical host, a UE or other device, and may also exist in the form of a device cluster. The UE may be a smart phone, a tablet computer, a laptop computer, a personal digital assistant (PDA), a desktop computer, an all-in-one computer or other different types of terminal devices.
[0031] Next, we will introduce the regional electricity consumption prediction method provided by this application.
[0032] Figure 1 It is a flow chart of a method for predicting regional electricity consumption provided in Example 1.
[0033] like Figure 1 As shown, the following steps are included:
[0034] Step 1: Acquire first historical electricity consumption data of a target area, wherein the first historical electricity consumption data includes actual electricity consumption of different electricity users in the target area during a target period;
[0035] Step 2, obtaining elasticity coefficients of electricity consumption of different entities affected by economic factors during the target period, and correcting the first historical electricity consumption data according to the elasticity coefficients to generate second historical electricity consumption data;
[0036] Step 3: Process the second historical electricity consumption data to generate accumulation sequences corresponding to different electricity users, and generate a gray prediction model for electricity consumption of each electricity user based on the accumulation sequences;
[0037] Step 4, generating a predicted power consumption of each power user in a future time period based on the power consumption grey prediction model;
[0038] Step 5, querying a preset coefficient table to obtain comprehensive influence coefficients corresponding to different electricity users, and using the comprehensive influence coefficients to optimize the predicted electricity consumption of each electricity user;
[0039] Step 6: Generate the total predicted electricity consumption of all entities in the target area in the future time period according to the optimization result.
[0040] The above embodiment provides a method for predicting regional electricity consumption. First, historical electricity consumption data of different electricity users are collected and corrected to reduce the impact of economic factors, such as GDP indicators. Then, based on the corrected benchmark data, grey prediction models are established for different electricity users and the first electricity consumption forecast is performed. The first prediction result is optimized using a comprehensive influence coefficient, thereby obtaining accurate prediction results for different electricity users, further improving the prediction accuracy of regional electricity consumption.
[0041] Let's continue with the above Figure 1 The various steps in the illustrated embodiment and the specific implementation methods that may be adopted in practical applications.
[0042] In this application, the target area refers to the area where electricity consumption forecasting is triggered or performed. The area can be obtained by geographical administrative division, for example, XX Street, XX District, City A, XX Industrial Zone, XX Street, City B, or it can be obtained by manual division or machine division, which is not limited here.
[0043] In addition to manual triggering, the present application can also configure corresponding triggering conditions to trigger the selection of a target area for regional power consumption forecasting.
[0044] For example, a scheduled update scenario can be set to select areas where a preset period has passed since the last power consumption data update as the target area for current triggering; or a real-time update scenario can be set to select areas where the power consumption efficiency has not reached the preset power consumption efficiency within a certain period as the target area for current triggering, and so on.
[0045] From this, it can be seen that the target area for predicting electricity consumption data referred to in this application can not only be targeted at the original, pre-divided area, but also at the currently divided area, and the specific selection conditions of the target area itself can also be adjusted according to actual needs, so that the target area has the characteristics of highly flexible setting.
[0046] The first historical power consumption data of the target area may be provided by the power system involved in the target area, or may be obtained from other devices storing the first historical power consumption data of the target area. Of course, the first historical power consumption data of the target area may also be local to the processing device storing the power consumption data.
[0047] In a preferred implementation, the prediction result can also be put into practical application, which has further application value. For example, in a preferred embodiment, the method for predicting regional power consumption of the present invention further includes a control step, specifically: generating an early warning instruction based on the total predicted power consumption of all entities in the target area in the future time period, and the early warning instruction is used to remind the target user to execute the preset power dispatch plan and / or preset electricity price adjustment plan of the target area.
[0048] It is understandable that due to the fact that electricity is difficult to store or the storage cost is high, and it must also be based on safety considerations, the entire power supply system, from the production link, transportation link to the supply link, involves complex scheduling content. Therefore, when determining the prediction results of electricity consumption data, the power scheduling strategy for the target area can be further generated and processed accordingly based on the prediction results, so as to automatically allocate the scheduling content involved in the production link, transportation link or supply link in the target area.
[0049] For example, some regions may adopt a dynamic pricing approach to incentivize electricity consumption in the region based on considerations of both electricity consumption and power generation, so as to achieve the goal of low-cost electricity consumption and improved electricity efficiency, or appropriately suppress electricity consumption in the region to achieve the goal of high-cost electricity consumption and reduced electricity consumption. Typically, a step-by-step pricing strategy can be used to adjust electricity prices.
[0050] It is understandable that different electricity users, including commercial entities, industrial entities, and residential users, have different electricity usage patterns and different electricity prices. After obtaining the first historical electricity usage data of the target area, the present invention can classify and sort the data according to different entities, thereby obtaining the electricity usage data of each electricity user, and establishing a prediction model corresponding to each electricity user in a targeted manner.
[0051] At the same time, when the present invention collects the first historical electricity consumption data, the first historical electricity consumption data collected is also different according to the specific application scenario, such as whether to predict the annual electricity consumption or the quarterly, monthly or daily electricity consumption. For example, if the electricity consumption of the target area from 2024 to 2030 is to be predicted, the annual electricity consumption data from 2000 to 2023 can be collected; if the electricity consumption of the target area is to be predicted for each month in 2024, the electricity consumption data for each month from 2000 to 2023 can be collected, thereby further improving the accuracy of the prediction result.
[0052] In a specific embodiment, the gray prediction model of electricity consumption of the present application is the GM (1,1) model. The gray prediction model (Grey Pred ict i on Mode l) is a mathematical model for time series prediction with a small amount of data, which has the advantages of less required information, high accuracy, simple calculation, and easy verification. The gray prediction model processes the original data to generate a new sequence, called the "accumulated generated sequence", and then models and predicts the sequence. The GM (1,1) model is a relatively commonly used gray prediction model. The first 1 indicates that the model is a first-order differential equation model, and the second 1 indicates that the model is a single variable.
[0053] In one embodiment, the grey prediction model is not directly established using the classified first historical electricity consumption data, but the first historical electricity consumption data is first processed using elasticity coefficients.
[0054] Specifically, electricity consumption is related to many factors, especially economic factors, including GDP indicators, industrial structure, etc. The growth of GDP in a region represents the sustainable economic development of the region, so the dependence of production and people's daily life on electricity consumption will become stronger and stronger, and this growth in electricity consumption will in turn promote the increase of regional GDP. The actual electricity consumption collected in step 1 is also the historical electricity consumption data under the comprehensive influence of multiple factors such as GDP indicators, seasonal factors, and population factors. However, the gray prediction model can usually only consider the electricity consumption itself, and it is difficult to involve the GDP indicator, that is, the development and changes of GDP are difficult to be clearly reflected in the prediction results of the gray model. In order to improve the prediction accuracy of regional electricity consumption of the present invention, the present invention first processes the first historical electricity consumption data, first reduces or eliminates the influence of economic factors on these data, so that the prediction data obtained by the gray prediction module is the benchmark prediction data without considering the influence of economic changes, and in the subsequent steps, the benchmark prediction data is compensated with a comprehensive coefficient that can reflect the influence of economic factors, thereby improving the accuracy of the prediction results. In this embodiment, the elasticity coefficient refers to the degree to which electricity consumption is affected by economic factors. The elasticity coefficient table can be established in advance based on economic development data, policy data, etc. For example, the electricity consumption of a region in December 2000 is collected as A1, and the GDP elasticity coefficient in December 2000 is B, then the revised electricity consumption data is A1 / B, and then the GM(1,1) grey prediction model is established based on the revised electricity consumption data.
[0055] It can be understood that the establishment of the grey prediction model specifically includes the following steps: constructing a cumulative generation sequence, calculating a neighboring mean sequence, constructing a data matrix and vector, estimating parameter values using the least squares method, bringing the parameter values into the model, and making predictions, etc. The above steps are conventional technical solutions known to those skilled in the art and will not be described in detail here. In this way, step 4 can be executed to use the constructed power consumption grey prediction model to generate the predicted power consumption of each power user in the future time period.
[0056] It can be understood that the predicted electricity consumption obtained in step 3 does not take into account the impact of economic factors. Therefore, it is necessary to query the preset coefficient table to obtain the comprehensive influence coefficients corresponding to different electricity users, and use the comprehensive influence coefficients to optimize the predicted electricity consumption of each electricity user to compensate for the impact of economic factors, seasonal factors, etc. on the predicted value.
[0057] In a preferred embodiment, the method for predicting regional electricity consumption of the present application further includes a step of constructing a preset coefficient table, specifically:
[0058] S001, filtering first power usage data within a first preset time range and second power usage data within a second preset time range from the first historical power usage data.
[0059] S002. Based on the first electricity consumption data and using multiple preset prediction methods (excluding the gray prediction method), multiple predicted electricity consumption data for each electricity user in the second preset time range are generated respectively. The multiple preset prediction methods include an exponential smoothing prediction method, a linear regression prediction method, and a WOA whale prediction method, etc. The above methods can all be used to obtain electricity consumption prediction results, but there are differences in accuracy and prediction speed.
[0060] S003, selecting target predicted power consumption data closest to the second power consumption data from the plurality of predicted power consumption data, that is, target predicted power consumption data closest to the actual power consumption data, so as to know which prediction method is most accurate.
[0061] S004, using the power consumption grey prediction model to generate basic predicted power consumption data for each power user in the second preset time range;
[0062] S005, calculate the ratio of the target predicted power consumption data and the basic predicted power consumption data of each power user, generate the comprehensive influence coefficient corresponding to each power user, and establish a preset coefficient table. The comprehensive influence coefficient represents the difference between the predicted value obtained by the most accurate prediction method and the predicted value obtained by the gray model, so as to perform numerical compensation after obtaining the predicted power consumption of each power user at the future moment in step 4.
[0063] It can be understood that the target period of this embodiment includes every year, every quarter, every month and even every day. In a preferred embodiment, when the first historical electricity consumption data is the actual daily electricity consumption of different electricity users, there is no need to obtain the daily elasticity coefficient. Instead, the elasticity coefficient of the electricity consumption of different users affected by economic factors in each month is obtained, and the actual daily electricity consumption of each electricity user is corrected according to the target elasticity coefficient of the corresponding month, thereby simplifying the steps and improving the prediction efficiency.
[0064] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.
[0065] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned method for predicting regional electricity consumption is implemented.
[0066] Figure 2 is a schematic diagram of the structure of the regional power consumption prediction device provided in Example 2, such as Figure 2 As shown, it includes an acquisition module 100, a correction module 200, a construction module 300, a prediction module 400, an optimization module 500 and a calculation module 600.
[0067] The acquisition module 100 is used to acquire first historical electricity consumption data of the target area, wherein the first historical electricity consumption data includes actual electricity consumption of different electricity users in the target area during the target period;
[0068] The correction module 200 is used to obtain the elasticity coefficient of the electricity consumption of different entities affected by economic factors during the target period, and to correct the first historical electricity consumption data according to the elasticity coefficient to generate second historical electricity consumption data;
[0069] The construction module 300 is used to process the second historical electricity consumption data, generate accumulation sequences corresponding to different electricity users, and generate a gray prediction model for electricity consumption of each electricity user based on the accumulation sequences;
[0070] The prediction module 400 is used to generate the predicted power consumption of each power user in a future time period based on the power consumption grey prediction model;
[0071] The optimization module 500 is used to query the preset coefficient table, obtain the comprehensive influence coefficients corresponding to different electricity users, and use the comprehensive influence coefficients to optimize the predicted power consumption of each electricity user;
[0072] The calculation module 600 is used to generate the total predicted power consumption of all entities in the target area in the future time period according to the optimization result.
[0073] The above embodiment provides a regional electricity consumption prediction device, which first collects historical electricity consumption data of different electricity users, corrects them to reduce the impact of economic factors, such as GDP indicators, and then establishes gray prediction models for different electricity users based on the corrected benchmark data and performs the first electricity consumption prediction. The first prediction result is optimized using a comprehensive influence coefficient, thereby obtaining accurate prediction results for different electricity users, further improving the prediction accuracy of regional electricity consumption.
[0074] In a preferred embodiment, the device for predicting regional electricity consumption further includes a second building module, and the second building module specifically includes:
[0075] a first screening unit, configured to screen first power usage data within a first preset time range and second power usage data within a second preset time range from the first historical power usage data;
[0076] a first prediction unit, configured to generate a plurality of predicted power consumption data of each power user in the second preset time range based on the first power consumption data and using a plurality of preset prediction methods;
[0077] a second screening unit, configured to screen out target predicted power usage data closest to the second power usage data from the plurality of predicted power usage data;
[0078] A second prediction unit, configured to generate basic predicted power consumption data for each power user within the second preset time range by using the power consumption grey prediction model;
[0079] The calculation unit is used to calculate the ratio of the target predicted electricity consumption data and the basic predicted electricity consumption data of each electricity user, generate the comprehensive influence coefficient corresponding to each electricity user, and establish a preset coefficient table.
[0080] In a preferred embodiment, the electricity consumption grey prediction model is a GM (1,1) model, and the multiple preset prediction methods include an exponential smoothing prediction method, a linear regression prediction method, and a WOA whale prediction method.
[0081] In a preferred embodiment, the regional electricity consumption prediction device also includes an early warning module, which is used to generate an early warning instruction based on the total predicted electricity consumption of all entities in the target area in the future time period, and the early warning instruction is used to remind the target user to execute the preset power dispatch plan and / or preset electricity price adjustment plan for the target area.
[0082] An embodiment of the present invention further provides a device for predicting regional electricity consumption, comprising a computer-readable storage medium and a processor, wherein the processor implements the steps of the above-mentioned method for predicting regional electricity consumption when executing a computer program on the computer-readable storage medium.
[0083] Figure 3 : is a schematic diagram of the structure of a device for predicting regional power consumption provided by Embodiment 3 of the present invention, such as Figure 3 As shown, the regional power consumption prediction device 8 of this embodiment includes: a processor 80, a readable storage medium 81, and a computer program 82 stored in the readable storage medium 81 and executable on the processor 80. When the processor 80 executes the computer program 82, the steps in the above-mentioned various method embodiments are implemented, for example Figure 1 Alternatively, when the processor 80 executes the computer program 82, the functions of each module in the above-mentioned device embodiments are realized, for example Figure 2 Functionality of the modules shown.
[0084] Exemplarily, the computer program 82 may be divided into one or more modules, which are stored in the readable storage medium 81 and executed by the processor 80 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 82 in the regional power consumption prediction device 8.
[0085] The regional power consumption prediction device 8 may include, but is not limited to, a processor 80 and a readable storage medium 81. Those skilled in the art will appreciate that Figure 3 It is only an example of the regional electricity consumption prediction device 8 and does not constitute a limitation of the regional electricity consumption prediction device 8. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the regional electricity consumption prediction device may also include a power management module, an operation processing module, input and output devices, a network access device, a bus, etc.
[0086] The processor 80 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.
[0087] The readable storage medium 81 may be an internal storage unit of the prediction device 8 for regional power consumption, such as a hard disk or memory of the prediction device 8 for regional power consumption. The readable storage medium 81 may also be an external storage device of the prediction device 8 for regional power consumption, such as a plug-in hard disk, a smart memory card (SmartMed iaCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the prediction device 8 for regional power consumption. Further, the readable storage medium 81 may also include both an internal storage unit and an external storage device of the prediction device 8 for regional power consumption. The readable storage medium 81 is used to store the computer program and other programs and data required by the prediction device for regional power consumption. The readable storage medium 81 may also be used to temporarily store data that has been output or is to be output.
[0088] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.
[0089] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0090] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein 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 the present invention.
[0091] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0092] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0093] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0094] The present invention is not limited to what is described in the specification and implementation modes, and therefore additional advantages and modifications can be easily realized by those skilled in the art. Therefore, without departing from the spirit and scope of the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details, representative devices, and illustrative examples shown and described herein.
Claims
1. A method for predicting regional electricity consumption, characterized in that: The following steps are involved: Step 1: Acquire first historical electricity consumption data of a target area, wherein the first historical electricity consumption data includes actual electricity consumption of different electricity users in the target area during a target period; Step 2, obtaining elasticity coefficients of electricity consumption of different entities affected by economic factors during the target period, and correcting the first historical electricity consumption data according to the elasticity coefficients to generate second historical electricity consumption data; Step 3: Process the second historical electricity consumption data to generate accumulation sequences corresponding to different electricity users, and generate a gray prediction model for electricity consumption of each electricity user based on the accumulation sequences; Step 4, generating a predicted power consumption of each power user in a future time period based on the power consumption grey prediction model; Step 5, querying a preset coefficient table to obtain comprehensive influence coefficients corresponding to different electricity users, and using the comprehensive influence coefficients to optimize the predicted electricity consumption of each electricity user; Step 6: Generate the total predicted electricity consumption of all entities in the target area in the future time period according to the optimization result.
2. The method for predicting regional electricity consumption according to claim 1, characterized in that: It also includes the steps of building a preset coefficient table, specifically: Filtering first power usage data within a first preset time range and second power usage data within a second preset time range from the first historical power usage data; Based on the first power consumption data and using multiple preset prediction methods, generate multiple predicted power consumption data for each power user in the second preset time range; Filtering out target predicted power usage data closest to the second power usage data from the plurality of predicted power usage data; The power consumption grey prediction model is used to generate basic predicted power consumption data for each power user in the second preset time range; Calculate the ratio of the target forecast electricity consumption data and the basic forecast electricity consumption data of each electricity user, generate the comprehensive impact coefficient corresponding to each electricity user, and establish a preset coefficient table.
3. The method for predicting regional electricity consumption according to claim 2, characterized in that: The electricity consumption grey prediction model is a GM (1,1) model, and the multiple preset prediction methods include an exponential smoothing prediction method, a linear regression prediction method, and a WOA whale prediction method.
4. The method for predicting regional electricity consumption according to any one of claims 1 to 3, characterized in that: It also includes a control step, specifically: generating an early warning instruction based on the total predicted electricity consumption of all entities in the target area in the future time period, and the early warning instruction is used to remind the target user to execute the preset power dispatch plan and / or preset electricity price adjustment plan for the target area.
5. The method for predicting regional electricity consumption according to claim 4, characterized in that: The first historical electricity consumption data includes actual electricity consumption of different electricity users in the target area each year, each quarter, each month or each day.
6. The method for predicting regional electricity consumption according to claim 5, characterized in that: When the first historical electricity consumption data is the actual daily electricity consumption of different electricity users, the step 2 is specifically: obtaining the elasticity coefficient of electricity consumption of different users affected by economic factors in each month, and correcting the actual daily electricity consumption of each electricity user according to the target elasticity coefficient of the corresponding month.
7. A device for predicting regional electricity consumption, characterized in that: It includes acquisition module, correction module, construction module, prediction module, optimization module and calculation module. The acquisition module is used to acquire first historical electricity consumption data of the target area, wherein the first historical electricity consumption data includes actual electricity consumption of different electricity users in the target area during the target period; The correction module is used to obtain the elasticity coefficient of the electricity consumption of different entities affected by economic factors during the target period, and to correct the first historical electricity consumption data according to the elasticity coefficient to generate second historical electricity consumption data; The construction module is used to process the second historical electricity consumption data to generate accumulation sequences corresponding to different electricity users, and generate a gray prediction model for electricity consumption of each electricity user based on the accumulation sequences; The prediction module is used to generate the predicted power consumption of each power user in a future time period based on the power consumption grey prediction model; The optimization module is used to query the preset coefficient table, obtain the comprehensive influence coefficients corresponding to different electricity users, and use the comprehensive influence coefficients to optimize the predicted power consumption of each electricity user; The calculation module is used to generate the total predicted power consumption of all entities in the target area in the future time period according to the optimization result.
8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for predicting regional electricity consumption according to any one of claims 1 to 6 above is implemented.
9. A device for predicting regional electricity consumption, comprising a computer-readable storage medium and a processor, characterized in that: When the processor executes the computer program on the computer-readable storage medium, the processor implements the steps of the method for predicting regional electricity consumption according to any one of claims 1 to 6.