CN115545361B - Method, system and medium for predicting climate environment of power grid transmission line - Google Patents
Method, system and medium for predicting climate environment of power grid transmission line Download PDFInfo
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Abstract
The application relates to a method, a system and a medium for predicting climate environment of a power grid transmission line, wherein the method comprises the steps of obtaining historical climate environment data and constructing an input sample; constructing a transducer prediction model of a sparse attention mechanism, and predicting an input sample to obtain an original prediction result; comparing the original prediction result with real data to obtain an error sequence, and inputting the error sequence into an ARIMA model for error prediction to obtain an error correction sequence; and adding the original prediction result and the error correction sequence to obtain a final prediction result. The method and the device effectively improve the stability and accuracy of overall prediction, effectively reduce the operand of self-attention operation in the traditional transducer model, accelerate the model training and prediction speed, and reduce the hardware requirements for deploying the model.
Description
Technical Field
The application relates to the field of power systems, in particular to a method, a system and a medium for predicting a climate environment of a power grid transmission line based on ARIMA correction transducer.
Background
In the power system, the climate environment information of the power grid transmission line has important reference value for planning, scheduling and maintaining the power network, and the climate environment information of the power grid transmission line can be predicted in advance to assist scheduling decision, so that the construction of the intelligent power grid is assisted. Therefore, the climate environment prediction method of the power grid transmission line has important significance.
Existing predictive models mainly include traditional linear models and emerging recurrent neural network models. The traditional linear model performs poorly for nonlinear parts in the environmental data, and the predictive performance depends on the choice of parameters. However, a series of models based on the recurrent neural network have difficulty in capturing long-term dependency relationships between historical data, and the required calculation amount is large. The use of hybrid models has received a great deal of attention due to the limitations of a single predictive model. Prediction in combination with different models is considered as an effective way to improve the prediction results.
Disclosure of Invention
The embodiment of the application aims to provide a method, a system and a medium for predicting the climate environment of a power grid transmission line, which effectively reduce the operation amount of self-attention operation in a traditional transducer model, accelerate the model training and prediction speed and reduce the hardware requirement for deploying the model.
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 a climate environment of a transmission line of a power grid, including the following specific steps:
acquiring historical climate environment data to construct an input sample;
constructing a transducer prediction model of a sparse attention mechanism, and predicting an input sample to obtain an original prediction result;
comparing the original prediction result with real data to obtain an error sequence, inputting an ARIMA model for error prediction to obtain an error correction sequence, wherein the error correction sequence gives possible deviation between the original prediction result of the transducer and the actual data;
and adding the original prediction result and the error correction sequence, and improving the defect of lack of linear characteristics of a transformation former model through the error correction sequence of the ARIMA model to obtain a final predicted future environmental data sequence with higher accuracy and stability.
The obtained historical climate environment data construction input sample is specifically that the historical climate environment data comprises a time sequence composed of historical values of temperature, humidity and wind speed information around a transmission line, the historical values are normalized and stored as vectors
As a model input.
The transducer prediction model of the sparse attention mechanism comprises an encoder and a decoder, wherein the encoder performs feature extraction on input data; the decoder performs multi-head sparse self-attention operation by utilizing the features extracted by the encoder, and finally realizes the climate environment prediction of the transmission line, wherein the input of the encoding layer is as follows
The input of the decoding layer is +.>
Output of coding layer ∈>
The encoder consists of several identical encoding layers, the decoder consists of several identical decoding layers except that the first encoding layer is input as +.>
All coding layers have the output of the last coding layer as its input, the output of the last coding layer as the output of the encoder>
Except that the first decoding layer input is +.>
And->
All the decoding layers take the output of the last decoding layer as input, and the output of the last decoding layer is the predicted sequence of the transducer prediction model for future climate data.
The coding layer of the transform prediction model comprises a one-dimensional convolution layer and sparse self-attentionThe encoding layer performs characteristic extraction, specifically, an input vector
After one-dimensional convolution layer and adding position coding, the +.>
Three weight matrices are used respectively +.>
，/>
And->
And->
Matrix multiplication is performed to obtain a multi-channel query matrix respectively>
Key matrix->
And value matrix->
In order to reduce the calculation amount of the model and prevent overfitting, the self-attention is obtained by calculating three matrixes through sparse self-attention, and the calculated self-attention is mapped into the original input dimension through a linear layer and then is combined with +.>
Adding, and then obtaining the output +.A result of the adding is obtained through the feedforward layer>
。
The sparse self-attention operation operates as follows: selecting a matrix
Maximum value +.>
The matrix after thinning is obtained by setting other elements to 0 by each element>
Wherein->
For a number of appropriate sizes set in advance, then matrix +.>
，/>
，/>
The self-attention operation is carried out as input, and the formula is
Wherein the method comprises the steps of
Activating a function for softmax +.>
For matrix->
Dimension of->
To calculate the self-attention.
The decoding layer of the transform prediction model comprises a decoding one-dimensional convolution layer, a decoding sparse self-attention layer, a decoding feedforward network layer and a decoding linear layer, and the decoding layer performs specific prediction operation to carry out the first input data
After decoding one-dimensional convolution layer and one layer of decoding sparse self-attention layer, mapping into query matrix by decoding linear layer
And thinning to obtain->
Then use the second input data +.>
Mapping into key matrix by decoding linear layer
And value matrix->
Attention is then calculated, given by
Wherein the method comprises the steps of
Is->
Is used for obtaining a transducer prediction model for future environment after the calculated attention is decoded by a linear layer and a feedforward layerPredicted sequence of data.
In a second aspect, embodiments of the present application provide a system for predicting climate conditions of a power grid transmission line, comprising,
the input sample construction module is used for acquiring historical climate environment data and constructing an input sample;
the transducer prediction model construction module is used for constructing a transducer prediction model of a sparse attention mechanism and predicting an input sample to obtain an original prediction result;
the error correction sequence acquisition module is used for comparing the original prediction result with real data to obtain an error sequence, and inputting the error sequence into the ARIMA model for error prediction to obtain an error correction sequence;
and the prediction result acquisition module is used for adding the original prediction result and the error correction sequence to obtain a final prediction result.
In a third aspect, embodiments of the present application provide a computer readable storage medium storing program code which, when executed by a processor, implements the steps of a grid transmission line climate environment prediction method as described above.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the method, the prediction model based on the transducer is used for effectively extracting the time domain characteristics and the long-term dependence information of the input long-time sequence by utilizing the one-dimensional convolutional neural network and the sparse self-attention mechanism, so that the problem that the long-time sequence data are difficult to effectively process in the prior art is solved;
(2) According to the method, the prediction model based on the transducer and the ARIMA is combined, and the nonlinear feature extraction capacity and the linear feature extraction capacity of the transducer model are combined, so that the ARIMA is utilized to carry out error correction on the prediction model based on the transducer, and the stability and the accuracy of overall prediction are effectively improved;
(3) According to the method and the device, sparse self-attention operation is introduced, so that the operation amount of self-attention operation in a traditional transducer model is effectively reduced, the model training and predicting speed is accelerated, and the hardware requirement for deploying the model is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed 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 should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method implementation in an embodiment of the present application;
fig. 3 is a schematic diagram of an adaptive dynamic convolution AdaConv module according to an embodiment of the present application;
FIG. 4 is a system block diagram of an embodiment of the present application;
fig. 5 is a graph showing the comparison of the prediction effect in 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 numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following 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 phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Referring to fig. 1, fig. 1 provides a method for predicting climate environment of a transmission line of a power grid according to an embodiment of the present application, including the following specific steps:
acquiring historical climate environment data to construct an input sample;
constructing a transducer prediction model of a sparse attention mechanism, and predicting an input sample to obtain an original prediction result;
comparing the original prediction result with real data to obtain an error sequence, inputting an ARIMA model for error prediction to obtain an error correction sequence, wherein the error correction sequence gives possible deviation between the original prediction result of the transducer and the actual data;
and adding the original prediction result and the error correction sequence, and improving the defect of lack of linear characteristics of a transformation former model through the error correction sequence of the ARIMA model to obtain a final predicted future environmental data sequence with higher accuracy and stability.
The obtained historical climate environment data construction input sample is specifically that the historical climate environment data comprises a time sequence composed of historical values of temperature, humidity and wind speed information around a transmission line, the historical values are normalized and stored as vectors
As a model input.
As shown in fig. 2 and fig. 3, the transform prediction model of the sparse attention mechanism comprises an encoder and a decoder, and the encoder performs feature extraction on input data; the decoder performs multi-head sparse self-attention operation by utilizing the features extracted by the encoder, and finally realizes the climate environment prediction of the transmission line, wherein the input of the encoding layer is as follows
The input of the decoding layer is +.>
Output of coding layer ∈>
The encoder consists of several identical coding layer groupsThe decoder consists of several identical decoding layers except that the first coding layer is input +.>
All coding layers have the output of the last coding layer as its input, the output of the last coding layer as the output of the encoder>
Except that the first decoding layer input is +.>
And->
All the decoding layers take the output of the last decoding layer as input, and the output of the last decoding layer is the predicted sequence of the transducer prediction model for future climate data.
The coding layer of the transform prediction model comprises a one-dimensional convolution layer, a sparse self-attention layer and a feedforward network layer, and the characteristic extraction operation of the coding layer is specifically that an input vector is input
After one-dimensional convolution layer and adding position coding, the +.>
Three weight matrices are used respectively +.>
，/>
And->
And->
Matrix multiplication is performed to obtain a multi-channel query matrix respectively>
Key matrix->
And value matrix->
In order to reduce the calculation amount of the model and prevent overfitting, the self-attention is obtained by calculating three matrixes through sparse self-attention, and the calculated self-attention is mapped into the original input dimension through a linear layer and then is combined with +.>
Adding, and then obtaining the output +.A result of the adding is obtained through the feedforward layer>
。
The sparse self-attention operation operates as follows: selecting a matrix
Maximum value +.>
The matrix after thinning is obtained by setting other elements to 0 by each element>
Wherein->
For a number of appropriate sizes set in advance, then matrix +.>
，/>
，/>
The self-attention operation is carried out as input, and the formula is
Wherein the method comprises the steps of
Activating a function for softmax +.>
For matrix->
Dimension of->
To calculate the self-attention.
The decoding layer of the transform prediction model comprises a decoding one-dimensional convolution layer, a decoding sparse self-attention layer, a decoding feedforward network layer and a decoding linear layer, and the decoding layer performs specific prediction operation to carry out the first input data
After decoding one-dimensional convolution layer and one layer of decoding sparse self-attention layer, mapping into query matrix by decoding linear layer
And thinning to obtain->
Then use the second input data +.>
Mapping into key matrix by decoding linear layer
And value matrix->
Attention is then calculated, given by
Wherein the method comprises the steps of
Is->
The calculated attention is decoded by the linear layer and the feedforward layer to obtain a predicted sequence of the transducer prediction model on future environment data.
The ARIMA model is established as follows:
drawing the historical data time sequence, observing whether the historical data time sequence is stable or not, and differentiating the historical data time sequence to obtain a stable time sequence if the historical data time sequence is a non-stable sequence;
for the obtained stationary time series, the autocorrelation coefficients ACF and the partial autocorrelation coefficients PACF are calculated, and the formula for calculating the autocorrelation coefficients ACF is as follows:
where N is the length of the sequence,
for the number of sequence lags, +.>
For the sequence mean>
For the sequence->
A point. The partial autocorrelation coefficients PACF are typically calculated using the least squares method.
Determining appropriate model parameters from ACF and PACF
. Wherein->
For the hysteresis number of the time series data itself used in the predictive model,/->
Differential times required for obtaining a smooth time sequence, < > are given>
Is the hysteresis number of the prediction error employed in the prediction model.
As shown in fig. 4, an embodiment of the present application provides a climate environment prediction system for a power grid transmission line, comprising,
the input sample construction module 1 is used for acquiring historical climate environment data and constructing an input sample;
the transducer prediction model construction module 2 is used for constructing a transducer prediction model of a sparse attention mechanism, and predicting an input sample to obtain an original prediction result;
the error correction sequence acquisition module 3 is used for comparing the original prediction result with real data to obtain an error sequence, and inputting the error sequence into the ARIMA model for error prediction to obtain an error correction sequence;
the prediction result obtaining module 4 is configured to add the original prediction result to the error correction sequence to obtain a final prediction result.
As shown in fig. 5, by applying the method of the present application, the air temperature of the power transmission line is predicted, and according to the comparative analysis of the measured temperature and the predicted temperature, it can be known that the weather environment prediction method of the power grid transmission line of the present application has a small difference between the predicted temperature and the measured temperature, and the prediction accuracy is high.
Embodiments of the present application provide a computer readable storage medium storing program code which, when executed by a processor, implements the steps of a grid transmission line climate environment prediction method as described above.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. 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 storage media for a computer 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, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
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 phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application, and various modifications and variations may be suggested to one skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (6)
1. The method for predicting the climate environment of the power grid transmission line is characterized by comprising the following specific steps of:
acquiring historical climate environment data to construct an input sample;
constructing a transducer prediction model of a sparse attention mechanism, and predicting an input sample to obtain an original prediction result;
comparing the original prediction result with real data to obtain an error sequence, inputting an ARIMA model for error prediction to obtain an error correction sequence, wherein the error correction sequence gives possible deviation between the original prediction result of the transducer and the actual data;
adding the original prediction result and the error correction sequence, and improving the defect of lack of linear characteristics of a transformation former model through the error correction sequence of the ARIMA model to obtain a final predicted future environmental data sequence with higher accuracy and stability;
the obtained historical climate environment data construction input sample is specifically that the historical climate environment data comprises a time sequence composed of historical values of temperature, humidity and wind speed information around a transmission line, the historical values are normalized and stored as vectors
As a model input;
the ARIMA model is established as follows:
drawing the historical data time sequence, observing whether the historical data time sequence is stable or not, and differentiating the historical data time sequence to obtain a stable time sequence if the historical data time sequence is a non-stable sequence;
for the obtained stationary time series, the autocorrelation coefficients ACF and the partial autocorrelation coefficients PACF are calculated, and the formula for calculating the autocorrelation coefficients ACF is as follows:
where N is the length of the sequence,
for the number of sequence lags, +.>
For the sequence mean>
For the sequence->
The partial autocorrelation coefficients PACF are calculated using the least squares method,
determining appropriate model parameters from ACF and PACF
Wherein->
For the hysteresis number of the time series data itself used in the predictive model,/->
Differential times required for obtaining a smooth time sequence, < > are given>
Hysteresis number of prediction error adopted in the prediction model;
the transducer prediction model of the sparse attention mechanism comprises an encoder and a decoder, wherein the encoder performs feature extraction on input data; the decoder performs multi-head sparse self-attention operation by utilizing the features extracted by the encoder, and finally realizes the climate environment prediction of the transmission line, wherein the input of the encoding layer is as follows
The input of the decoding layer is +.>
Output of coding layer ∈>
The encoder consists of several identical encoding layers, the decoder consists of several identical decoding layers except that the first encoding layer is input as +.>
All coding layers have the output of the last coding layer as its input, the output of the last coding layer as the output of the encoder>
Except that the first decoding layer input is +.>
And->
All the decoding layers take the output of the last decoding layer as input, and the output of the last decoding layer is the predicted sequence of the transducer prediction model for future climate data.
2. A method for predicting climate environment of power grid transmission line according to claim 1The method is characterized in that the coding layer of the transformation former prediction model comprises a one-dimensional convolution layer, a sparse self-attention layer and a feedforward network layer, and the characteristic extraction operation of the coding layer is that an input vector is specifically
After one-dimensional convolution layer and adding position coding to obtain
Three weight matrices are used respectively +.>
，/>
And->
And->
Matrix multiplication is performed to obtain multi-channel query matrix respectively
Key matrix->
And value matrix->
In order to reduce the calculation amount of the model and prevent overfitting, the self-attention is obtained by calculating three matrixes through sparse self-attention, and the calculated self-attention is mapped into the original input dimension through a linear layer and then is combined with +.>
Adding, and then obtaining the output +.A result of the adding is obtained through the feedforward layer>
。
3. A method of predicting a climate environment in a power grid transmission line according to claim 2, wherein the sparse self-attention algorithm operates as: selecting a matrix
Maximum value +.>
The matrix after thinning is obtained by setting other elements to 0 by each element>
Wherein->
For a number of appropriate sizes set in advance, then matrix +.>
，/>
，/>
The self-attention operation is carried out as input, and the formula is
Wherein the method comprises the steps of
Activating a function for softmax +.>
For matrix->
Dimension of->
To calculate the self-attention.
4. The method of claim 2, wherein the decoding layer of the transform prediction model comprises a decoding one-dimensional convolution layer, a decoding sparse self-attention layer, a decoding feed-forward network layer, and a decoding linear layer, wherein the decoding layer performs prediction in such a way that the first input data is processed
After decoding one-dimensional convolution layer and one layer of decoding sparse self-attention layer, mapping into query matrix by decoding linear layer>
And thinning to obtain->
Then use the second input data +.>
Mapping into key matrix by decoding linear layer>
And value matrix->
Attention is then calculated, given by
Wherein the method comprises the steps of
Is->
The calculated attention is decoded by the linear layer and the feedforward layer to obtain a predicted sequence of the transducer prediction model on future environment data.
5. A system for predicting the climate environment of a power grid transmission line, which is configured to implement the method for predicting the climate environment of a power grid transmission line according to any one of claims 1 to 4.
6. A computer readable storage medium, characterized in that the computer readable storage medium stores a program code which, when executed by a processor, implements the steps of the grid transmission line climate environment prediction method according to any of claims 1-4.
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