CN115049169B - Regional power consumption prediction method, system and medium based on combination of frequency domain and spatial domain - Google Patents

Regional power consumption prediction method, system and medium based on combination of frequency domain and spatial domain Download PDF

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CN115049169B
CN115049169B CN202210978150.4A CN202210978150A CN115049169B CN 115049169 B CN115049169 B CN 115049169B CN 202210978150 A CN202210978150 A CN 202210978150A CN 115049169 B CN115049169 B CN 115049169B
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power consumption
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data
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王晟玮
周正
胡钰林
廖荣涛
王逸兮
李磊
叶宇轩
胡欢君
张剑
宁昊
董亮
刘芬
郭岳
罗弦
张岱
陈家璘
徐浩
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Wuhan University WHU
Information and Telecommunication Branch of State Grid Hubei Electric Power Co Ltd
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Information and Telecommunication Branch of State Grid Hubei Electric Power Co Ltd
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Abstract

The application relates to a regional power consumption prediction method, a system and a medium based on combination of a frequency domain and a spatial domain, wherein the method comprises the following specific steps: according to the acquired historical data of the power grid, comprehensively analyzing historical power consumption data and position information in the historical data of the power grid, and performing season-trend item decomposition on the historical data of the power grid; performing encoder processing of a transform model aiming at input power grid historical data, wherein the encoder processing comprises frequency domain feature extraction based on Fourier transform and space domain feature extraction based on a graph convolution neural network; and performing decoding layer operation on the processed data by using a decoder of a transform model, and combining frequency domain characteristics, space domain characteristics and multi-head frequency domain sub-attention operation in a long-time sequence to realize accurate power consumption prediction of a power grid area. The method solves the problem that the long-time sequence is difficult to effectively model in the existing prediction method, and meanwhile, the frequency domain and space domain characteristics are extracted, and the characteristics are utilized to predict the power consumption.

Description

Regional power consumption prediction method, system and medium based on combination of frequency domain and spatial domain
Technical Field
The application relates to the field of intelligent prediction of regional power consumption in the field of energy Internet, in particular to a regional power consumption prediction method, a regional power consumption prediction system and a regional power consumption prediction medium based on combination of a frequency domain and a spatial domain.
Background
With the continuous increase of the scale of the power grid, the information processing bottleneck problem of the traditional power grid can be effectively solved by the application of the artificial intelligence technology in the power grid. The power consumption of the user is important reference data of the intelligent power grid, and the power consumption data of the user is analyzed and predicted to further know the rule of the power consumption data of the user, so that planning and scheduling decisions of power grid power can be assisted. The existing power grid user power consumption prediction method mainly uses a model based on an autoregressive or recurrent neural network. The two models have poor processing capability on long-time sequences, and are difficult to acquire long-term dependence information of the sequences, so that the long-term dependence information cannot be effectively predicted. The Transformer model is proposed by Google corporation in 2017, is a sequence information processing model based on an encoder-decoder and a self-attention mechanism, and can effectively acquire long-term dependence characteristics in a sequence so as to improve the sequence prediction effect. The power consumption data of the users in the power grid has the characteristics of long sequence and much data, and the problem of overlarge calculated amount is faced by directly applying the Transformer model.
The power consumption of the user has obvious seasonality and periodicity, and in addition, the power consumption is greatly related to the distribution of the spatial position of the user, and the power consumption has certain frequency domain characteristics as time sequence data. Such features are difficult to obtain by conventional Transformer models.
Disclosure of Invention
The embodiment of the application aims to provide a regional power consumption prediction method, a regional power consumption prediction system and a regional power consumption prediction medium based on combination of a frequency domain and a spatial domain, a fast Fourier transform method and a graph convolution method are fused in a transform, a user power consumption data prediction model based on characteristics of the frequency domain and the spatial domain is constructed, and the feature extraction capability of the transform model is improved.
In order to achieve the above purpose, the present application provides the following technical solutions:
in a first aspect, an embodiment of the present application provides a method for predicting area power consumption based on a combination of a frequency domain and a spatial domain, including the following specific steps:
according to the acquired historical data of the power grid, comprehensively analyzing historical power consumption data and position information in the historical data of the power grid, and performing season-trend item decomposition on the historical data of the power grid;
performing encoder processing of a transform model aiming at input power grid historical data, wherein the encoder processing comprises frequency domain feature extraction based on Fourier transform and space domain feature extraction based on a graph convolution neural network;
and performing decoding layer operation on the processed data by using a decoder of a transform model, and realizing accurate power consumption prediction of the power grid region by combining frequency domain characteristics, space domain characteristics and multi-head frequency domain self-attention operation in a long-time sequence.
The historical power consumption data and the position information in the historical data of the comprehensive analysis power grid are specifically that the historical power consumption data comprises a user power consumption time sequence
Figure 520531DEST_PATH_IMAGE001
The user position information passes through a directed graph
Figure 846470DEST_PATH_IMAGE002
Is shown in which
Figure 9598DEST_PATH_IMAGE003
Is a collection of user locations, in common
Figure 130001DEST_PATH_IMAGE004
A user then has
Figure 641229DEST_PATH_IMAGE004
The elements are selected from the group consisting of,
Figure 505280DEST_PATH_IMAGE005
is the edge of the directed graph,
Figure 522914DEST_PATH_IMAGE006
the method is characterized in that an adjacency matrix for storing the distance between users is formed by splicing the time sequence and the position information of each power consumption data of the users into a matrix
Figure 79798DEST_PATH_IMAGE007
；
The method for decomposing the historical data of the power grid by the season-trend item specifically comprises the step of carrying out time series on the power consumption of a user by a transform model
Figure 815672DEST_PATH_IMAGE001
Performing sequence decomposition into seasonal items
Figure 217835DEST_PATH_IMAGE008
And trend item
Figure 89976DEST_PATH_IMAGE009
The specific calculation method is shown in the following formula:
Figure 817761DEST_PATH_IMAGE010
Figure 40932DEST_PATH_IMAGE011
(2)
wherein,
Figure 246785DEST_PATH_IMAGE012
in order to average out the pooling operation,
Figure 35749DEST_PATH_IMAGE013
operations are complemented by 0 at both ends of the sequence to keep the length of the sequence unchanged after the pooling operation.
The frequency domain feature extraction based on the fourier transform is specifically,
time series of electricity consumption of user
Figure 668856DEST_PATH_IMAGE001
In the step (1), the first step,
Figure 113744DEST_PATH_IMAGE014
is the length of the sequence, and is,
Figure 123288DEST_PATH_IMAGE015
for the characteristic quantity, will
Figure 32338DEST_PATH_IMAGE016
User electricity consumption time series of dimension
Figure 836346DEST_PATH_IMAGE001
Sending the data into a frequency domain characteristic layer for Fourier transform to extract frequency domain characteristics, wherein the specific operation of the frequency domain characteristic layer is to firstly carry out Fourier transform on the data
Figure 768530DEST_PATH_IMAGE001
Linear mapping is performed to map it into
Figure 581765DEST_PATH_IMAGE017
Dimensional query matrix
Figure 545655DEST_PATH_IMAGE018
In which
Figure 723826DEST_PATH_IMAGE019
Is a preset number with proper size, and then is paired
Figure 346569DEST_PATH_IMAGE018
Is subjected to fast Fourier transform to obtain
Figure 635599DEST_PATH_IMAGE017
Dimensional spectrum matrix
Figure 456924DEST_PATH_IMAGE020
For reducing the amount of calculation and preventing overfitting, the method carries out sparse operation
Figure 868314DEST_PATH_IMAGE021
Random sampling is carried out to obtain
Figure 571828DEST_PATH_IMAGE022
Dimension matrix
Figure 461286DEST_PATH_IMAGE023
Wherein
Figure 402698DEST_PATH_IMAGE024
Is far less than
Figure 453830DEST_PATH_IMAGE019
Second using a layer of randomly initialized convolutional neural network and
Figure 847902DEST_PATH_IMAGE025
is convolved to obtain
Figure 541052DEST_PATH_IMAGE022
Matrix of
Figure 334040DEST_PATH_IMAGE026
Finally to is aligned
Figure 87232DEST_PATH_IMAGE027
Performing zero-filling operation to make its dimension become
Figure 437442DEST_PATH_IMAGE017
And performing inverse fast Fourier transform to obtain an output
Figure 465441DEST_PATH_IMAGE028
；
The spatial domain feature extraction based on the graph convolution neural network specifically comprises the following steps of extracting a matrix
Figure 584707DEST_PATH_IMAGE007
Sending into graph volume network layer, defining in-graph
Figure 508800DEST_PATH_IMAGE029
The formula for the convolution of the above graph is:
Figure 877465DEST_PATH_IMAGE030
wherein
Figure 912417DEST_PATH_IMAGE031
Are the parameters of the convolution kernel and,
Figure 151768DEST_PATH_IMAGE032
is a matrix of the unit, and is,
Figure 246763DEST_PATH_IMAGE033
is shown as a drawing
Figure 899462DEST_PATH_IMAGE029
Degree matrix, matrix
Figure 206946DEST_PATH_IMAGE007
Obtaining the corresponding dimension of the user to be predicted after the graph convolution layer and carrying out zero filling to obtain the output of the spatial domain characteristic layer
Figure 831963DEST_PATH_IMAGE034
。
The structure of the encoder in the encoder processing of the transformer model aiming at the input power grid historical data is determined by
Figure 894596DEST_PATH_IMAGE035
The same coding layer is formed, and the coder processes the output of the frequency domain characteristic layer
Figure 706695DEST_PATH_IMAGE028
And spatial feature layer
Figure 349029DEST_PATH_IMAGE036
Adding the data, decomposing the sequence to obtain seasonal terms, sending the seasonal terms into a feedforward neural network, decomposing the sequence again to obtain the output of the coding layer, the input of the first layer in the coder is the original data, the output of the previous layer in each coding layer in the back is the input of the layer, and finally the coder outputs the coding result
Figure 625289DEST_PATH_IMAGE037
。
The decoder using the transform model performs a decoding layer operation on the processed data specifically,
first, the season item
Figure 796507DEST_PATH_IMAGE008
After the frequency domain characteristic layer and the space domain characteristic layer are input, the output of the characteristic layer is summed
Figure 158219DEST_PATH_IMAGE008
Adding and carrying out sequence decomposition to obtain seasonal components
Figure 70155DEST_PATH_IMAGE038
And trend component
Figure 669764DEST_PATH_IMAGE039
；
The second step, the seasonal component obtained in the previous step
Figure 746304DEST_PATH_IMAGE038
And the encoder outputs the encoding result
Figure 798574DEST_PATH_IMAGE037
Performing frequency domain self-attention operation;
thirdly, outputting the frequency domain self attention and the seasonal component obtained in the first step
Figure 517131DEST_PATH_IMAGE038
Adding the seasonal components and then performing sequence decomposition to obtain seasonal components
Figure 705667DEST_PATH_IMAGE040
And trend component
Figure 31737DEST_PATH_IMAGE041
Will be
Figure 60653DEST_PATH_IMAGE040
After passing through a feedforward neural network
Figure 786163DEST_PATH_IMAGE009
，
Figure 360364DEST_PATH_IMAGE042
，
Figure 309865DEST_PATH_IMAGE043
After addition, the output of the decoder is obtained
Figure 274410DEST_PATH_IMAGE044
。
The frequency domain self-attention operation process is as follows: first of all, multi is introduced through the linear layerHead mechanism, will
Figure 600349DEST_PATH_IMAGE038
Query matrix mapped to more channels by linear layers
Figure 763478DEST_PATH_IMAGE018
In the same way, will
Figure 883880DEST_PATH_IMAGE037
Mapping into value matrix V and key matrix through linear layer
Figure 194776DEST_PATH_IMAGE045
Then to
Figure 668614DEST_PATH_IMAGE018
，
Figure 686248DEST_PATH_IMAGE045
V, carrying out fast Fourier transform and random sampling to obtain a sampled frequency spectrum matrix
Figure 709043DEST_PATH_IMAGE046
Second, the matrix is divided
Figure 241656DEST_PATH_IMAGE047
Performing inner product operation, activating function and then performing AND through softmax
Figure 643818DEST_PATH_IMAGE048
Performing inner product operation, and finally performing zero filling and fast Fourier inverse transformation on the obtained result to obtain frequency domain self-attention output
Figure 515959DEST_PATH_IMAGE049
The formula is expressed as follows,
Figure 243744DEST_PATH_IMAGE050
wherein,
Figure 998073DEST_PATH_IMAGE051
the function is activated for the softmax and,
Figure 203927DEST_PATH_IMAGE052
is composed of
Figure 196154DEST_PATH_IMAGE018
Of (c) is measured.
In a second aspect, an embodiment of the present application provides a regional power consumption prediction system based on a combination of a frequency domain and a spatial domain, the system including,
the power grid historical data acquisition and analysis module is used for acquiring power grid historical data and comprehensively analyzing historical power consumption data and position information in the power grid historical data;
the season-trend item decomposition module is used for performing season-trend item decomposition on the historical data of the power grid;
the characteristic extraction module is used for extracting the frequency domain characteristic of the power grid historical data based on Fourier transform and the spatial domain characteristic of the power grid historical data based on a graph convolution neural network;
and the power consumption prediction module of the power grid area is used for combining frequency domain characteristics, airspace characteristics and multi-head frequency domain sub-attention operation in a long-time sequence to realize accurate power consumption prediction of the power grid area.
In a third aspect, an embodiment of the present application provides a computer-readable storage medium, which stores program codes, and when the program codes are executed by a processor, the method for predicting regional power consumption based on combination of a frequency domain and a spatial domain as described above is implemented.
Compared with the prior art, the invention has the beneficial effects that: a fast Fourier transform method and a graph convolution method are fused in a transform, a user power consumption data prediction model based on frequency domain and spatial domain characteristics is constructed, and the characteristic extraction capability of the transform model is improved. Meanwhile, the calculated amount is reduced by combining sparse operation, and finally the power consumption of the power grid user is predicted.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a flowchart of a regional power consumption prediction method based on a combination of a frequency domain and a spatial domain according to an embodiment of the present application;
FIG. 2 is a flow chart of an encoder process of a transform model according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating the operation of a decoding layer according to an embodiment of the present application;
FIG. 4 is a block diagram of a model for predicting power consumption of a user according to an embodiment of the present disclosure;
FIG. 5 is a schematic block diagram of a regional power consumption prediction system based on a combination of a frequency domain and a spatial domain according to an embodiment of the present disclosure;
FIG. 6 is a graph showing the actual amount of electricity used and the predicted amount of electricity used in 100 hours according to the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The terms "first," "second," and the like, are used solely to distinguish one entity or action from another entity or action without necessarily being construed as indicating or implying any actual such relationship or order between such entities or actions.
Referring to fig. 1, an embodiment of the present application provides a method for predicting area power consumption based on a combination of a frequency domain and a spatial domain, including the following specific steps:
s1, according to the acquired historical data of the power grid, comprehensively analyzing historical power consumption data and position information in the historical data of the power grid, and performing season-trend item decomposition on the historical data of the power grid;
s2, performing encoder processing of a transform model aiming at input power grid historical data, wherein the encoder processing comprises frequency domain feature extraction based on Fourier transform and space domain feature extraction based on a graph convolution neural network;
and S3, decoding layer operation is carried out on the processed data by using a decoder of a transform model, and accurate power consumption prediction of the power grid region is realized by combining frequency domain characteristics, space domain characteristics and multi-head frequency domain sub-attention operation in a long-time sequence.
The historical power consumption data and the position information in the historical data of the comprehensive analysis power grid are specifically that the historical power consumption data comprises a user power consumption time sequence
Figure 625998DEST_PATH_IMAGE001
The user position information passes through a directed graph
Figure 336465DEST_PATH_IMAGE002
Is shown in which
Figure 80430DEST_PATH_IMAGE003
Is a collection of user locations, in common
Figure 723901DEST_PATH_IMAGE004
A user then has
Figure 793488DEST_PATH_IMAGE004
The elements are selected from the group consisting of,
Figure 725672DEST_PATH_IMAGE005
is the edge of the directed graph,
Figure 538907DEST_PATH_IMAGE006
the method is characterized in that an adjacency matrix for storing the distance between users is formed, and the time sequence and the position information of each power consumption data of the users are spliced into the matrix
Figure 505726DEST_PATH_IMAGE007
；
The method for decomposing the historical data of the power grid by the season-trend item specifically comprises the step of carrying out time series on the power consumption of a user by a transform model
Figure 480636DEST_PATH_IMAGE001
Performing sequence decomposition into seasonal items
Figure 165695DEST_PATH_IMAGE008
And trend item
Figure 517042DEST_PATH_IMAGE009
The specific calculation method is shown by the following formula:
Figure 335437DEST_PATH_IMAGE010
Figure 215669DEST_PATH_IMAGE011
(2)
wherein,
Figure 528970DEST_PATH_IMAGE012
in order to average out the pooling operation,
Figure 684007DEST_PATH_IMAGE053
operations are padded with 0's at both ends of the sequence to keep the length of the sequence unchanged after the pooling operation.
As shown in figure 2 of the drawings, in which,
s21, the frequency domain feature extraction based on the Fourier transform specifically comprises the steps of,
time series of electricity consumption of user
Figure 359839DEST_PATH_IMAGE001
In (1),
Figure 676551DEST_PATH_IMAGE014
in order to be the length of the sequence,
Figure 805044DEST_PATH_IMAGE015
for the characteristic quantity, will
Figure 498194DEST_PATH_IMAGE016
User power consumption time series of dimension
Figure 90849DEST_PATH_IMAGE001
Sending the frequency domain characteristic layer to perform Fourier transform to extract frequency domain characteristics, wherein the specific operation of the frequency domain characteristic layer is to firstly carry out Fourier transform on the frequency domain characteristic layer
Figure 844041DEST_PATH_IMAGE001
Linear mapping is performed to map it into
Figure 194251DEST_PATH_IMAGE017
Dimensional query matrix
Figure 487829DEST_PATH_IMAGE018
Wherein
Figure 138254DEST_PATH_IMAGE019
Is a preset number with proper size, and then is paired
Figure 796768DEST_PATH_IMAGE018
Is subjected to fast Fourier transform to obtain
Figure 962170DEST_PATH_IMAGE017
Dimensional spectrum matrix
Figure 994193DEST_PATH_IMAGE020
In order to reduce the amount of calculation and prevent overfitting, sparse operation is carried out, and the specific method is to
Figure 233544DEST_PATH_IMAGE021
Random sampling is carried out to obtain
Figure 328539DEST_PATH_IMAGE054
Dimension matrix
Figure 715658DEST_PATH_IMAGE023
Wherein
Figure 288722DEST_PATH_IMAGE024
Is far less than
Figure 710476DEST_PATH_IMAGE019
Second using a layer of randomly initialized convolutional neural network and
Figure 976372DEST_PATH_IMAGE055
is convolved to obtain
Figure 788470DEST_PATH_IMAGE054
Matrix array
Figure 165225DEST_PATH_IMAGE026
Finally to make a pair
Figure 441486DEST_PATH_IMAGE027
Performing zero-filling operation to make its dimension become
Figure 878283DEST_PATH_IMAGE017
And performing inverse fast Fourier transform to obtain an output
Figure 177677DEST_PATH_IMAGE028
；
The spatial domain feature extraction based on the graph convolution neural network specifically comprises the following steps of extracting a matrix
Figure 154861DEST_PATH_IMAGE007
Sending into graph volume network layer, defining in-graph
Figure 754469DEST_PATH_IMAGE029
The above graph convolution formula is:
Figure 96589DEST_PATH_IMAGE030
wherein
Figure 883279DEST_PATH_IMAGE031
Are the parameters of the convolution kernel and,
Figure 664154DEST_PATH_IMAGE032
is a matrix of the unit, and is,
Figure 852689DEST_PATH_IMAGE033
is shown as a drawing
Figure 428027DEST_PATH_IMAGE029
Degree matrix, matrix
Figure 702014DEST_PATH_IMAGE007
Obtaining the corresponding dimension of the user to be predicted after the graph convolution layer and carrying out zero filling to obtain the output of the spatial domain characteristic layer
Figure 958683DEST_PATH_IMAGE034
。
S23, the structure of an encoder in the encoder processing of the transformer model aiming at the input power grid historical data is determined by
Figure 64042DEST_PATH_IMAGE035
The same coding layer is formed, and the coder processes the output of the frequency domain characteristic layer
Figure 13543DEST_PATH_IMAGE028
And spatial feature layer
Figure 771896DEST_PATH_IMAGE036
Adding the data, decomposing the sequence to obtain seasonal terms, sending the seasonal terms into a feedforward neural network, decomposing the sequence again to obtain the output of the coding layer, the input of the first layer in the coder is the original data, the output of the previous layer in each coding layer in the back is the input of the layer, and finally the coder outputs the coding result
Figure 832256DEST_PATH_IMAGE037
。
As shown in fig. 3, the decoding layer operation performed on the processed data by the decoder using the transform model is specifically that the decoder data input operation first extracts half of the seasonal item obtained by the decomposition in step S1, and the half sequence length is equal to
Figure 57701DEST_PATH_IMAGE056
After that, 0 is added to make the total length equal to
Figure 381366DEST_PATH_IMAGE014
And then input into a decoder for processing.
S31, selecting the season item
Figure 426683DEST_PATH_IMAGE008
After the frequency domain characteristic layer and the spatial domain characteristic layer are input, the output of the characteristic layer is compared with
Figure 25154DEST_PATH_IMAGE008
Adding and carrying out sequence decomposition to obtain seasonal components
Figure 308368DEST_PATH_IMAGE038
And trend component
Figure 599672DEST_PATH_IMAGE039
；
S32, the seasonal component obtained in the last step
Figure 132285DEST_PATH_IMAGE038
And the encoder outputs the encoding result
Figure 534447DEST_PATH_IMAGE037
Performing frequency domain self-attention operation;
s33, outputting the frequency domain from attention and the seasonal component obtained in the first step
Figure 672167DEST_PATH_IMAGE038
Adding the obtained seasonal components and performing sequence decomposition to obtain seasonal components
Figure 931111DEST_PATH_IMAGE040
Sum trend component
Figure 154281DEST_PATH_IMAGE041
Will be
Figure 360135DEST_PATH_IMAGE040
After passing through feedforward neural network and
Figure 352362DEST_PATH_IMAGE009
，
Figure 782206DEST_PATH_IMAGE057
，
Figure 227094DEST_PATH_IMAGE043
adding to obtain the output of the decoder
Figure 236638DEST_PATH_IMAGE044
。
The frequency domain self-attention operation process is as follows: first, a multi-head mechanism is introduced through the linear layer
Figure 880109DEST_PATH_IMAGE038
Query matrix mapped to more channels by linear layers
Figure 949696DEST_PATH_IMAGE018
In the same way, will
Figure 616301DEST_PATH_IMAGE037
Mapping into value matrix V and key matrix through linear layer
Figure 226274DEST_PATH_IMAGE045
Then is aligned with
Figure 924584DEST_PATH_IMAGE018
，
Figure 899493DEST_PATH_IMAGE045
V, carrying out fast Fourier transform and random sampling to obtain a sampled frequency spectrum matrix
Figure 318973DEST_PATH_IMAGE058
Secondly, the matrix is divided into
Figure 467058DEST_PATH_IMAGE047
Performing inner product operation, activating a function through softmax, and performing the operation
Figure 22804DEST_PATH_IMAGE048
Performing inner product operation, and finally performing zero filling and fast Fourier inverse transformation on the obtained result to obtain frequency domain self-attention output
Figure 434194DEST_PATH_IMAGE049
The formula is expressed as follows,
Figure 137708DEST_PATH_IMAGE050
wherein,
Figure 27166DEST_PATH_IMAGE051
the function is activated for the softmax and,
Figure 702998DEST_PATH_IMAGE052
is composed of
Figure 82027DEST_PATH_IMAGE018
Of (c) is calculated.
The method for predicting the user power consumption based on the transform of the frequency domain and the spatial domain features solves the problem that the long-time sequence is difficult to effectively model in the conventional prediction method, extracts the frequency domain and the spatial domain features at the same time, and predicts the power consumption by using the features.
As shown in fig. 4, a structure diagram of a user power consumption prediction model based on a transform model of frequency domain and spatial domain features according to an embodiment of the present application is provided.
As shown in fig. 5, an embodiment of the present application provides a regional power consumption prediction system based on a combination of a frequency domain and a spatial domain, the system includes,
the power grid historical data acquisition and analysis module 1 is used for acquiring power grid historical data and comprehensively analyzing historical power consumption data and position information in the power grid historical data;
the season-trend item decomposition module 2 is used for performing season-trend item decomposition on historical data of the power grid;
the characteristic extraction module 3 is used for performing frequency domain characteristic extraction based on Fourier transform and space domain characteristic extraction based on a graph convolution neural network on the historical data of the power grid;
and the power consumption prediction module 4 for the power grid region is used for realizing accurate power consumption prediction of the power grid region by combining frequency domain characteristics, spatial domain characteristics and multi-head frequency domain sub-attention operation in a long time sequence.
An embodiment of the present application further provides a computer-readable storage medium, where program codes are stored, and when the program codes are executed by a processor, the method for intelligently predicting regional power consumption as described above is implemented.
As shown in fig. 6, by using the method for predicting electricity consumption in an area based on combination of a frequency domain and an airspace according to the embodiment of the present application, the data of the electricity consumption of users in a certain cell in wuhan is analyzed to obtain a predicted electricity consumption curve in 100 hours, and the actual electricity consumption curve in 100 hours is contrastingly analyzed, so that it can be found that the similarity between the predicted electricity consumption curve obtained by the prediction method of the present application and the actual electricity consumption curve is high, and thus the effectiveness of the method of the present application can be determined.
The method for predicting the user power consumption of the transform based on the frequency domain and spatial domain features solves the problem that the long-time sequence is difficult to effectively model in the existing prediction method, extracts the frequency domain and spatial domain features, predicts the power consumption by using the features, and is high in prediction accuracy.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (3)

1. A regional power consumption prediction method based on combination of a frequency domain and a spatial domain is characterized by comprising the following specific steps:
according to the acquired historical data of the power grid, comprehensively analyzing historical power consumption data and position information in the historical data of the power grid, and performing season-trend item decomposition on the historical data of the power grid;
performing encoder processing of a transform model aiming at input power grid historical data, wherein the encoder processing comprises frequency domain feature extraction based on Fourier transform and space domain feature extraction based on a graph convolution neural network;
performing decoding layer operation on the processed data by using a decoder of a transform model, and realizing accurate power consumption prediction of a power grid area by combining frequency domain characteristics, space domain characteristics and multi-head frequency domain sub-attention operation in a long-time sequence;
the historical power consumption data and the position information in the historical data of the comprehensive analysis power grid are specifically that the historical power consumption data comprises a user power consumption time sequence
Figure 115310DEST_PATH_IMAGE001
The user position information passes through a directed graph
Figure 772688DEST_PATH_IMAGE002
Is shown in which
Figure 82446DEST_PATH_IMAGE003
Is a collection of user locations, in common
Figure 762957DEST_PATH_IMAGE004
A user is then provided with
Figure 894993DEST_PATH_IMAGE004
The elements are selected from the group consisting of,
Figure 406877DEST_PATH_IMAGE005
is the edge of the directed graph,
Figure 887536DEST_PATH_IMAGE006
the method is characterized in that an adjacency matrix for storing the distance between users is formed by splicing the time sequence and the position information of each power consumption data of the users into a matrix
Figure 524185DEST_PATH_IMAGE007
；
The method for decomposing the historical data of the power grid by the season-trend item specifically comprises the step of carrying out time series on the power consumption of a user by a transform model
Figure 787807DEST_PATH_IMAGE001
Performing sequence decomposition into seasonal items
Figure 623039DEST_PATH_IMAGE008
And trend item
Figure 274601DEST_PATH_IMAGE009
The specific calculation method is shown in the following formula:
Figure 132966DEST_PATH_IMAGE010
Figure 200279DEST_PATH_IMAGE011
(2)
wherein,
Figure 890018DEST_PATH_IMAGE012
in order to average out the pooling operation,
Figure 587847DEST_PATH_IMAGE013
operations supplement 0 at both ends of the sequence to keep the length of the sequence unchanged after pooling operations;
the frequency domain feature extraction based on the fourier transform is specifically,
time series of electricity consumption of user
Figure 730246DEST_PATH_IMAGE014
In the step (1), the first step,
Figure 929146DEST_PATH_IMAGE015
in order to be the length of the sequence,
Figure 676654DEST_PATH_IMAGE016
for the characteristic quantity, will
Figure 342121DEST_PATH_IMAGE017
User electricity consumption time series of dimension
Figure 971817DEST_PATH_IMAGE014
Sending the data into a frequency domain characteristic layer for Fourier transform to extract frequency domain characteristics, wherein the specific operation of the frequency domain characteristic layer is to firstly carry out Fourier transform on the data
Figure 974408DEST_PATH_IMAGE014
Linear mapping is performed to map it into
Figure 373159DEST_PATH_IMAGE018
Dimensional query matrix
Figure 412791DEST_PATH_IMAGE019
Wherein
Figure 529782DEST_PATH_IMAGE020
Is a preset number with proper size, and then is paired
Figure 70485DEST_PATH_IMAGE019
Is subjected to fast Fourier transform to obtain
Figure 300306DEST_PATH_IMAGE018
Dimensional spectrum matrix
Figure 776417DEST_PATH_IMAGE021
In order to reduce the amount of calculation and prevent overfitting, sparse operation is carried out, and the specific method is to
Figure 239760DEST_PATH_IMAGE022
Random sampling is carried out to obtain
Figure 459520DEST_PATH_IMAGE023
Dimension matrix
Figure 567284DEST_PATH_IMAGE024
In which
Figure 338931DEST_PATH_IMAGE025
Is far less than
Figure 164936DEST_PATH_IMAGE020
Second using a layer of randomly initialized convolutional neural network and
Figure 922807DEST_PATH_IMAGE026
by convolution to obtain
Figure 150657DEST_PATH_IMAGE023
Matrix of
Figure 968572DEST_PATH_IMAGE027
Finally to is aligned
Figure 406506DEST_PATH_IMAGE028
Performing zero-filling operation to make its dimension become
Figure 968069DEST_PATH_IMAGE018
And performing inverse fast Fourier transform to obtain an output
Figure 50425DEST_PATH_IMAGE029
；
The spatial domain feature extraction based on the graph convolution neural network specifically comprises the following steps of extracting a matrix
Figure 163875DEST_PATH_IMAGE030
Sending into graph volume network layer, defining in-graph
Figure 964472DEST_PATH_IMAGE031
The above graph convolution formula is:
Figure 64146DEST_PATH_IMAGE032
wherein
Figure 391222DEST_PATH_IMAGE033
Are the parameters of the convolution kernel and,
Figure 285360DEST_PATH_IMAGE034
is a matrix of the unit, and is,
Figure 573253DEST_PATH_IMAGE035
is shown as a drawing
Figure 476618DEST_PATH_IMAGE031
Degree matrix, matrix
Figure 658201DEST_PATH_IMAGE030
Obtaining the corresponding dimension of the user to be predicted after the graph convolution layer and carrying out zero filling to obtain the output of the spatial domain characteristic layer
Figure 988819DEST_PATH_IMAGE036
；
The structure of an encoder in the encoder processing of the transformer model aiming at the input power grid historical data is determined by
Figure 498429DEST_PATH_IMAGE037
The same coding layer is formed, and the encoder processes the output of the frequency domain characteristic layer
Figure 64539DEST_PATH_IMAGE029
And spatial feature layer
Figure 241574DEST_PATH_IMAGE038
Adding the data, decomposing the sequence to obtain seasonal terms, sending the seasonal terms into a feedforward neural network, decomposing the sequence again to obtain the output of the coding layer, the input of the first layer in the coder is the original data, the output of the previous layer in each coding layer in the back is the input of the layer, and finally the coder outputs the coding result
Figure 743094DEST_PATH_IMAGE039
；
The decoder using the transform model performs a decoding layer operation on the processed data specifically,
first, the season item
Figure 740000DEST_PATH_IMAGE008
After the frequency domain characteristic layer and the space domain characteristic layer are input, the output of the characteristic layer is summed
Figure 109801DEST_PATH_IMAGE008
Adding and carrying out sequence decomposition to obtain seasonal components
Figure 141342DEST_PATH_IMAGE040
And trend component
Figure 813763DEST_PATH_IMAGE041
；
The second step, the seasonal component obtained in the previous step
Figure 422599DEST_PATH_IMAGE040
And the encoder outputs the encoding result
Figure 205878DEST_PATH_IMAGE039
Carrying out frequency domain self-attention operation;
thirdly, outputting the frequency domain self attention and the seasonal component obtained in the first step
Figure 91926DEST_PATH_IMAGE040
Adding the obtained seasonal components and performing sequence decomposition to obtain seasonal components
Figure 59882DEST_PATH_IMAGE042
And trend component
Figure 31380DEST_PATH_IMAGE043
Will be
Figure 618350DEST_PATH_IMAGE042
After passing through a feedforward neural network
Figure 358904DEST_PATH_IMAGE009
，
Figure 497762DEST_PATH_IMAGE044
，
Figure 956556DEST_PATH_IMAGE045
Adding to obtain the output of the decoder
Figure 347217DEST_PATH_IMAGE046
；
The frequency domain self-attention operation process is as follows: first, a multi-head mechanism is introduced through the linear layer, and
Figure 66911DEST_PATH_IMAGE040
query matrix mapped to more channels by linear layers
Figure 252036DEST_PATH_IMAGE019
In the same way, will
Figure 198127DEST_PATH_IMAGE039
Mapping into value matrix V and key matrix through linear layer
Figure 126899DEST_PATH_IMAGE047
Then to
Figure 966680DEST_PATH_IMAGE019
，
Figure 346143DEST_PATH_IMAGE047
V, carrying out fast Fourier transform and random sampling to obtain a sampled frequency spectrum matrix
Figure 779530DEST_PATH_IMAGE048
Secondly, the matrix is divided into
Figure 371048DEST_PATH_IMAGE049
Performing inner product operation, activating function and then performing AND through softmax
Figure 940701DEST_PATH_IMAGE050
Performing inner product operation, and finally performing zero filling and inverse fast Fourier transform on the obtained result to obtain frequency domain self-attention output
Figure 733207DEST_PATH_IMAGE051
The formula is expressed as follows,
Figure 512945DEST_PATH_IMAGE052
wherein,
Figure 783520DEST_PATH_IMAGE053
the function is activated for the softmax and,
Figure 473258DEST_PATH_IMAGE054
is composed of
Figure 171087DEST_PATH_IMAGE019
Of (c) is calculated.
2. A regional power consumption prediction system based on a combination of frequency domain and spatial domain, wherein the system uses the method of claim 1 to predict, the system comprising,
the power grid historical data acquisition and analysis module is used for acquiring power grid historical data and comprehensively analyzing historical power consumption data and position information in the power grid historical data;
the season-trend item decomposition module is used for performing season-trend item decomposition on historical data of the power grid;
the characteristic extraction module is used for extracting the frequency domain characteristic of the power grid historical data based on Fourier transform and the spatial domain characteristic of the power grid historical data based on a graph convolution neural network;
and the power consumption prediction module of the power grid area is used for combining the frequency domain characteristics, the spatial domain characteristics and the multi-head frequency domain sub-attention operation in the long-time sequence to realize accurate power consumption prediction of the power grid area.
3. A computer-readable storage medium, characterized in that the computer-readable storage medium stores program code which, when executed by a processor, implements the steps of the method for regional power usage prediction based on a combination of frequency and spatial domains as claimed in claim 1.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016063660A1 (en) * 2014-10-23 2016-04-28 株式会社日立製作所 Power system monitoring device and power system monitoring method
CN111784043A (en) * 2020-06-29 2020-10-16 南京工程学院 Accurate prediction method for power selling amount of power distribution station area based on modal GRU learning network
CN114154700A (en) * 2021-11-25 2022-03-08 浙江工业大学 User power consumption prediction method based on transformer model
CN114519471A (en) * 2022-03-28 2022-05-20 杭州电子科技大学 Electric load prediction method based on time sequence data periodicity
CN114723583A (en) * 2022-03-24 2022-07-08 天津三源电力信息技术股份有限公司 Unstructured electric power big data analysis method based on deep learning

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113935534A (en) * 2021-10-20 2022-01-14 华能大理风力发电有限公司洱源分公司 Photovoltaic day-ahead prediction method, system and equipment based on convolution transform architecture
CN114819388A (en) * 2022-05-18 2022-07-29 江苏方天电力技术有限公司 Condenser vacuum degree prediction method and device based on frequency domain information guidance

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016063660A1 (en) * 2014-10-23 2016-04-28 株式会社日立製作所 Power system monitoring device and power system monitoring method
CN111784043A (en) * 2020-06-29 2020-10-16 南京工程学院 Accurate prediction method for power selling amount of power distribution station area based on modal GRU learning network
CN114154700A (en) * 2021-11-25 2022-03-08 浙江工业大学 User power consumption prediction method based on transformer model
CN114723583A (en) * 2022-03-24 2022-07-08 天津三源电力信息技术股份有限公司 Unstructured electric power big data analysis method based on deep learning
CN114519471A (en) * 2022-03-28 2022-05-20 杭州电子科技大学 Electric load prediction method based on time sequence data periodicity

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于提升小波的时间序列分析法的电力负荷预测;张帆等;《电气自动化》;20170530(第03期);第76-80页 *

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