CN115983459A - Multipoint power load prediction method and device and terminal equipment - Google Patents
Multipoint power load prediction method and device and terminal equipment Download PDFInfo
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Abstract
The application is applicable to the technical field of power load prediction, and provides a multipoint power load prediction method, a multipoint power load prediction device and terminal equipment, wherein the method comprises the following steps: the method comprises the steps of obtaining a plurality of data, wherein the data represent data of a plurality of point locations, and the point locations represent any power distribution area in the power system; determining a category to which each data in the plurality of data belongs based on the plurality of data and a similarity algorithm; obtaining the identifier of each data based on the category of each data; and obtaining a load prediction result of each point location based on the multi-point power load prediction model and the identification of each data. The method and the device can reduce the burden of the server and network transmission and improve the prediction efficiency.
Description
Technical Field
The application belongs to the technical field of power load prediction, and particularly relates to a multi-point power load prediction method, a multi-point power load prediction device and terminal equipment.
Background
The power load prediction is to estimate the demand of a power system and study the influence of relevant factors on the power load by analyzing historical data of the power load based on the change of the power load and the change of external factors and by using a specific mathematical method or a mode of establishing a mathematical model as a means. The load prediction comprises two meanings, and the prediction of the power demand determines the size of newly added capacity of a power generation system, a power transmission system and a power distribution system; the prediction of the electric quantity demand determines the type of the power generation equipment, such as the base load type of a peak shaving unit and the like.
The purpose of the power load prediction is to provide the development condition and level of the load, determine the power supply areas, the planned annual power supply and consumption amount, the maximum load of the power supply and consumption amount and the total load development level of the planned areas at the same time, and determine the power load composition of the planned annual power consumption; the load prediction of the power system is related to the dispatching operation and the production plan of the power system, and the accurate load prediction is beneficial to improving the safety and the stability of the system and reducing the power generation cost; in the operation process of the power system, the power load prediction problem plays an important role for a plurality of power departments, and relates to a plurality of aspects such as power system planning and design, power system economic and safe operation, power market transaction and the like.
In the prior art, multiple factors need to be integrated for predicting the power load, and the power load is judged for a period of time in the future based on longer-time monitoring data, so that more factor data need to be prepared before prediction, the calculation process of a prediction model consumes long time, and larger burden is brought to network transmission and calculation of a server.
Disclosure of Invention
The embodiment of the application provides a multi-point power load prediction method, a multi-point power load prediction device and terminal equipment, so that the load of a server and network transmission is reduced, and the prediction efficiency is improved.
The application is realized by the following technical scheme:
in a first aspect, an embodiment of the present application provides a multi-point power load prediction method, including:
the method comprises the steps of obtaining a plurality of data, wherein the data represent data of a plurality of point locations, and the point locations represent any transformer area in the power system;
determining a category to which each of the plurality of data belongs based on the plurality of data and a similarity algorithm;
obtaining an identifier of each data based on the category of each data;
and obtaining a load prediction result of each point location based on the multi-point power load prediction model and the identification of each data.
With reference to the first aspect, in some possible implementations, determining a category to which each data in the plurality of data belongs based on the plurality of data and a similarity algorithm includes: acquiring a historical data set; and classifying the plurality of data based on historical data and a similarity algorithm to obtain the category of each data in the plurality of data.
With reference to the first aspect, in some possible implementations, a difference between each of the plurality of data and other data in the category to which the data belongs is smaller than a first threshold.
With reference to the first aspect, in some possible implementations, the multi-point power load prediction model includes an input layer, a hidden layer, and an output layer, where the output layer includes a plurality of nodes, and an output of each node corresponds to a prediction result of a plurality of point locations, respectively.
With reference to the first aspect, in some possible implementations, obtaining a load prediction result of each point location based on a multi-point location power load prediction model and an identifier of each data includes: obtaining a class corresponding to each data based on the identifier of the data; based on the class corresponding to the data, obtaining a class center data set of the class, wherein the class center data set of the class is a set of representative data in all historical data in the class; and obtaining a load prediction result of each point location based on the multi-point power load prediction model and the class center data set of the class.
With reference to the first aspect, in some possible implementations, the method for predicting a multi-point power load further includes: acquiring historical load data; obtaining a residual error based on the load prediction result and the historical load data of each point location, wherein the residual error represents the deviation between the load prediction result and the historical load data of each point location; based on the residual error, adjusting model parameters in the power load prediction model, and then predicting again; and when the residual error is smaller than a second threshold value, outputting a final load prediction result of each point.
With reference to the first aspect, in some possible implementations, the residual is calculated by the following formula:
C＝|P measuring -P Fruit of Chinese wolfberry |
Wherein C denotes the residual, P Measuring Representing the load prediction result, P, of each point Fruit of Chinese wolfberry Representing historical load data.
In a second aspect, an embodiment of the present application provides a multi-point power load prediction apparatus, including:
the acquisition module is used for acquiring a plurality of data, and the plurality of data represent data of a plurality of point locations;
the category module is used for determining the category of each data in the data based on the data and a similarity algorithm;
the identification module is used for obtaining the identification of each data based on the category of each data;
and the result module is used for obtaining a load prediction result of each point location based on the multi-point power load prediction model and the identification of each data.
In a third aspect, an embodiment of the present application provides a terminal device, including: a processor and a memory for storing a computer program which when executed by the processor implements the multi-point bit power load prediction method according to any one of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, where a computer program is stored, and when executed by a processor, the computer program implements the multi-point power load prediction method according to any one of the first aspect.
In a fifth aspect, the present application provides a computer program product, when the computer program product runs on a terminal device, causing the terminal device to execute the multi-point power load prediction method according to any one of the first aspect.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Compared with the prior art, the embodiment of the application has the advantages that:
according to the method, the corresponding data of the point locations in the power system are subjected to the similarity algorithm to obtain the category of each data, then the identification of each data is obtained, and the load prediction result of each point location is obtained according to the multi-point power load prediction model.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the specification.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic view of an application scenario of a multi-point power load prediction method according to an embodiment of the present application;
fig. 2 is a schematic flowchart illustrating a multi-point power load prediction method according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a multi-point power load prediction apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather mean "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The power load prediction is to estimate the demand of a power system and study the influence of relevant factors on the power load by analyzing historical data of the power load based on the change of the power load and the change of external factors and by using a specific mathematical method or a mode of establishing a mathematical model as a means. The load prediction comprises two meanings, and the prediction of the power demand determines the size of newly added capacity of a power generation system, a power transmission system and a power distribution system; the prediction of the electric quantity demand determines the type of the power generation equipment, such as the base load type of a peak shaving unit and the like.
The purpose of the power load prediction is to provide the development condition and level of the load, determine the total load development level of each power supply area, each planned annual power supply and consumption amount, the maximum load of the power supply and consumption amount and the planned area at the same time, and determine the composition of each planned annual power load; the load prediction of the power system is related to the dispatching operation and the production plan of the power system, and the accurate load prediction is beneficial to improving the safety and the stability of the system and reducing the power generation cost; in the operation process of the power system, the power load prediction problem plays an important role for a plurality of power departments, and relates to a plurality of aspects such as power system planning and design, power system economic and safe operation, power market transaction and the like.
In the prior art, multiple factors need to be integrated for predicting the power load, and the power load is judged in a period of time in the future based on long-time monitoring data, so that the factor data needing to be prepared before prediction is more, the calculation process of a prediction model is long in time consumption, and great burden is brought to network transmission and calculation of a server.
Based on the above problem, a multi-point power load prediction method is proposed, and the method of the embodiment of the present application may be applied to an exemplary scenario as shown in fig. 1. In this scenario, the data acquisition device 10 is configured to acquire data of multiple point locations (any area of the power system), and send the data into the multi-point power load prediction device 20, where the multi-point power load prediction device 20 obtains a load prediction result of each point location according to the data of the multiple point locations, and provides a basis for multiple aspects such as subsequent power system planning and design, power system economic and safe operation, and power market trading.
Fig. 2 is a schematic flowchart of a multi-point power load prediction method according to an embodiment of the present disclosure, and referring to fig. 2, the multi-point power load prediction method is described in detail as follows:
Specifically, each of the plurality of data is a set of data such as air temperature, humidity, and wind speed at the point X year, X month, and X day.
Illustratively, determining the category to which each of the plurality of data belongs based on the plurality of data and a similarity algorithm includes: acquiring a historical data set; and classifying the plurality of data based on historical data and a similarity algorithm to obtain the category of each data in the plurality of data.
Specifically, assuming that the data to be determined in the category in the plurality of data is a, the data in the historical data set includes B, C and D, cosine values cos (a, B) of the data a and the data B are obtained based on the cosine theorem, cosine values cos (a, C) of the data a and the data C are obtained based on the cosine theorem, and cosine values cos (a, D) of the data a and the data D are obtained based on the cosine theorem. The two corresponding data with the value closest to 1 in cos (a, B), cos (a, C) and cos (a, D) are most similar, for example, when cos (a, B) is closest to 1, data a and data B are the two most similar data, and the belonging category of data a is considered to be consistent with the category of data B.
In particular, the method comprises the following steps of,
wherein A is n Is characteristic data in data A, B n Is the characteristic data in the data B. And selecting the same number from the data A and the data B to obtain the characteristic data. The closer the cosine value is to 1, the more similar the two data are, and the closer the cosine value is to 0, the more dissimilar the two data are.
Illustratively, each of the plurality of data differs from the other data in the category to which the data belongs by less than a first threshold.
Specifically, assuming that the data is a set of data of air temperature, humidity and wind speed in X months and X days in X years, the time is 12 and 19 months in 2022 years, the air temperature is-9 ℃, the humidity is 27%, and the wind speed is north wind level 1, in order to determine the category to which the data belongs, the data in the historical data set needs to be matched by using a similarity algorithm to find the category most similar to the data. For example, the data may be categorized as early in winter.
And 103, obtaining the identifier of each data based on the category of each data.
Specifically, in order to enable the server side to perform work for processing certain specific numerical values which do not need data, the data identification can play a role in reducing server operation, reducing the server operation load and improving the server operation efficiency.
And 104, obtaining a load prediction result of each point location based on the multi-point power load prediction model and the identification of each data.
Illustratively, the multi-point power load prediction model comprises an input layer, a hidden layer and an output layer, wherein the output layer comprises a plurality of nodes, and the output of each node corresponds to the prediction results of a plurality of point positions respectively.
Illustratively, obtaining the load prediction result of each point location based on the multi-point power load prediction model and the identifier of each data includes: obtaining a class corresponding to each data based on the identifier of the data; based on the class corresponding to the data, obtaining a class center data set of the class, wherein the class center data set of the class is a set of representative data in all historical data in the class; and obtaining a load prediction result of each point location based on the multi-point power load prediction model and the class center data set of the class.
Specifically, data received by an input layer of the multi-point power load prediction model is a class center data set, and then a final prediction result is obtained through calculation of a hidden layer and output through an output layer. The class corresponding to the data of each point location is known, and the multi-point power load prediction model outputs prediction results of different classes, so that the prediction result of each point location can be obtained, namely the output of each node corresponds to the prediction results of a plurality of point locations respectively.
The exemplary multi-point power load prediction method further comprises: acquiring historical load data; obtaining a residual error based on the load prediction result and the historical load data of each point location, wherein the residual error represents the deviation between the load prediction result and the historical load data of each point location; based on the residual error, adjusting model parameters in the power load prediction model, and then predicting again; and when the residual error is smaller than a second threshold value, outputting a final load prediction result of each point.
Specifically, for the multi-point power load prediction model, the multi-point power load prediction model is arranged at a server side of the power system, and after the server side obtains the identifier of each data, the power load prediction process of the point can be carried out according to the identifier and the multi-point power load prediction model, so that the pressure of data processing at the server side is reduced, the prediction efficiency is improved, and the real-time performance is stronger.
Illustratively, the residual is calculated by the following equation:
C＝|P side survey -P Fruit of Chinese wolfberry |
Wherein C denotes the residual, P Side survey Representing the load prediction result, P, of each point Fruit of Chinese wolfberry Representing historical load data.
According to the multi-point power load prediction method, the corresponding data of the points in the power system are subjected to the similarity algorithm to obtain the category of each data, then the identification of each data is obtained, and the load prediction result of each point is obtained according to the multi-point power load prediction model.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 3 shows a block diagram of a multi-point power load prediction device provided in the embodiment of the present application, corresponding to the multi-point power load prediction method described in the above embodiment, and only the relevant parts of the multi-point power load prediction device are shown for convenience of description.
Referring to fig. 3, the multi-point power load prediction apparatus in the embodiment of the present application may include an obtaining module 301, a category module 302, an identification module 303, and a result module 304.
Optionally, the obtaining module 301 is configured to obtain a plurality of data, where the plurality of data represents data of a plurality of point locations.
Optionally, the category module 302 is configured to determine a category to which each data in the plurality of data belongs based on the plurality of data and a similarity algorithm.
Illustratively, the category module 302 is further configured to: acquiring a historical data set; and classifying the plurality of data based on the historical data and the similarity algorithm to obtain the category of each data in the plurality of data.
Illustratively, each of the plurality of data differs from the other data in the category to which the data belongs by less than a first threshold.
Optionally, the identifying module 303 is configured to obtain an identifier of each data based on the category to which each data belongs.
Optionally, the result module 304 is configured to obtain a load prediction result of each point location based on the multi-point power load prediction model and the identifier of each data.
Illustratively, the multi-point power load prediction model comprises an input layer, a hidden layer and an output layer, wherein the output layer comprises a plurality of nodes, and the output of each node corresponds to the prediction results of a plurality of points respectively.
Illustratively, the results module 304 is further configured to: obtaining a class corresponding to each data based on the identifier of the data; based on the class corresponding to the data, obtaining a class center data set of the class, wherein the class center data set of the class is a set of representative data in all historical data in the class; and obtaining a load prediction result of each point location based on the multi-point power load prediction model and the class center data set of the class.
Illustratively, the results module 304 is further configured to: acquiring historical load data; obtaining a residual error based on the load prediction result and the historical load data of each point location, wherein the residual error represents the deviation between the load prediction result and the historical load data of each point location; based on the residual error, adjusting model parameters in the power load prediction model, and then predicting again; and when the residual error is smaller than a second threshold value, outputting a final load prediction result of each point.
Illustratively, the residual is calculated by the following equation:
C＝|P measuring -P Fruit of Chinese wolfberry |
Wherein C denotes the residual, P Measuring Representing the load prediction result, P, of each point Fruit of Chinese wolfberry Representing historical load data.
It should be noted that, for the information interaction, execution process, and other contents between the above devices/units, the specific functions and technical effects thereof based on the same concept as those of the method embodiment of the present application can be specifically referred to the method embodiment portion, and are not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
An embodiment of the present application further provides a terminal device, and referring to fig. 4, the terminal device 500 may include: at least one processor 510, a memory 520, the memory 520 being configured to store a computer program 521, the processor 510 being configured to call and execute the computer program 521 stored in the memory 520 to implement the steps in any of the method embodiments described above, for example, the steps 101 to 104 in the embodiment shown in fig. 2. Alternatively, the processor 510, when executing the computer program, implements the functions of the modules/units in the above-described device embodiments, such as the functions of the modules 301 to 304 shown in fig. 3.
Illustratively, the computer program 521 may be divided into one or more modules/units, which are stored in the memory 520 and executed by the processor 510 to accomplish the present application. The one or more modules/units may be a series of computer program segments capable of performing specific functions, which are used to describe the execution of the computer program in the terminal device 500.
Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device and is not meant to be limiting and may include more or fewer components than those shown, or some of the components may be combined, or different components such as input output devices, network access devices, buses, etc.
The Processor 510 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 520 may be an internal storage unit of the terminal device, or may be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. The memory 520 is used for storing the computer programs and other programs and data required by the terminal device. The memory 520 may also be used to temporarily store data that has been output or is to be output.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.
The multi-point power load prediction method provided by the embodiment of the application can be applied to terminal devices such as computers, wearable devices, vehicle-mounted devices, tablet computers, notebook computers and netbooks, and the specific type of the terminal device is not limited at all.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program may implement the steps in the embodiments of the multipoint power load prediction method described above.
The embodiment of the present application provides a computer program product, which when running on a mobile terminal, enables the mobile terminal to implement the steps in the above multi-point power load prediction method when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-drive, a removable hard drive, a magnetic or optical disk, etc.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present application, and they should be construed as being included in the present application.
Claims (10)
1. A multi-point power load prediction method, comprising:
acquiring a plurality of data, wherein the data represent data of a plurality of point locations, and the point locations represent any transformer area in an electric power system;
determining a category to which each of the plurality of data belongs based on the plurality of data and a similarity algorithm;
obtaining the identifier of each data based on the category of each data;
and obtaining a load prediction result of each point location based on the multi-point power load prediction model and the identification of each data.
2. The multi-site power load prediction method of claim 1 wherein determining the category to which each of the plurality of data belongs based on the plurality of data and the similarity algorithm comprises:
acquiring a historical data set;
and classifying the plurality of data based on the historical data and the similarity algorithm to obtain the category of each data in the plurality of data.
3. The multi-site power load prediction method of claim 1, wherein each of the plurality of data differs from other data in the category to which the data belongs by less than a first threshold.
4. The multi-point location power load prediction method according to claim 1, wherein the multi-point location power load prediction model comprises an input layer, a hidden layer and an output layer, the output layer comprises a plurality of nodes, and the output of each node corresponds to the prediction results of the plurality of point locations respectively.
5. The multi-point electrical load forecasting method of claim 1, wherein the obtaining a load forecast for each point based on the multi-point electrical load forecasting model and the identification of each data, comprises:
obtaining a class corresponding to each data based on the identifier of the data;
based on the class corresponding to the data, obtaining a class center data set of the class, wherein the class center data set of the class is a set of representative data in all historical data in the class;
and obtaining a load prediction result of each point location based on the multi-point power load prediction model and the class-like central data set of the class.
6. The multi-point electrical load prediction method according to claim 4, further comprising:
acquiring historical load data;
obtaining a residual error based on the load prediction result of each point location and the historical load data, wherein the residual error represents the deviation between the load prediction result of each point location and the historical load data;
based on the residual error, adjusting model parameters in the power load prediction model, and then predicting again;
and when the residual error is smaller than a second threshold value, outputting a final load prediction result of each point.
7. The multi-point electric load prediction method according to claim 6, characterized in that the residual error is calculated by the following formula:
C＝P measuring -P Fruit of Chinese wolfberry
Wherein C denotes the residual, P Measuring Representing the load prediction result, P, of each point Fruit of Chinese wolfberry Representing historical load data.
8. A multi-point power load prediction apparatus, comprising:
the acquisition module is used for acquiring a plurality of data, and the data represents data of a plurality of point positions;
a category module for determining a category to which each of the plurality of data belongs based on the plurality of data and a similarity algorithm;
the identification module is used for obtaining the identification of each data based on the category of each data;
and the result module is used for obtaining the load prediction result of each point location based on the multi-point power load prediction model and the identification of each data.
9. A terminal device, comprising: processor and a memory, in which a computer program is stored which is executable on the processor, wherein the processor, when executing the computer program, implements a multi-point electrical load prediction method according to any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a multi-point electrical load prediction method according to any one of claims 1 to 7.
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