Short-term load prediction method and system based on improved GWO-VMD-LSTM

Abstract

The invention discloses a short-term load prediction method and system based on improved GWO-VMD-LSTM, and the method comprises the steps: carrying out the parameter optimization of a mode decomposition number and a penalty factor which need to be determined before VMD feature extraction, carrying out the variational mode decomposition of a load feature time sequence signal, decomposing the signal into a series of intrinsic mode function components, and carrying out the VMD-LSTM. An improved wavelet packet soft threshold function is used, a lugish-greed mechanism is provided to improve the function, denoising processing is performed on IMF component signals, feature coefficients and modal kurtosis values of all components are extracted, and the denoising effect is evaluated; a network structure of a long short-term neural network is improved, a parameter adaptive adjustment optimization algorithm is provided, the learning rate of the LSTM is dynamically optimized, and data is input into the network for training and prediction. The accuracy of short-term load prediction and the precision of classification can be improved, and short-term accurate prediction of the power load is facilitated.

Description

Technical Field

[0001] The present invention belongs to the technical field of power load forecasting, and in particular relates to a short-term load forecasting method and system based on an improved GWO-VMD-LSTM. Background Art

[0002] The power industry is the basic guarantee for social production and people's daily life. It not only affects the overall development speed of social economy, but is also closely related to everyone's daily life. Compared with other energy sources, electric energy has many advantages, such as low pollution, high efficiency, easy conversion, and convenient long-distance transmission. Therefore, it has become one of the indispensable important energy sources. The essence of power load forecasting is to integrate the operating characteristics of the power system, influencing factors and other conditions, use scientific and reasonable methods, explore the laws and development trends of historical power loads, and estimate and infer the power load demand value for a period of time in the future.

[0003] At present, the long short-term memory neural network provides a new idea for solving the problem. It collects various original power load data, analyzes the characteristics of load influencing factors, and uses the long short-term memory neural network to predict short-term power load. Summary of the invention

[0004] The present invention provides a short-term load forecasting method and system based on improved GWO-VMD-LSTM, which are used to solve the technical problems of high forecasting difficulty caused by complex nonlinearity and large volatility of power load data, and insufficient robustness and accuracy of traditional methods.

[0005] In a first aspect, the present invention provides a short-term load forecasting method based on an improved GWO-VMD-LSTM, comprising: Determine the modal decomposition quantity and penalty factor of variational modal decomposition according to the improved grey wolf optimization algorithm; Performing variational mode decomposition on the load characteristic time series signal according to the mode decomposition quantity and the penalty factor to obtain at least one IMF component signal, wherein the IMF component signal includes a load influence sub-signal and a load result sub-signal corresponding to the load influence sub-signal; Performing denoising processing on the at least one IMF component signal according to a preset wavelet packet soft threshold function to obtain at least one target IMF component signal; Inputting the at least one target IMF component signal into an improved long-term and short-term neural network, dynamically optimizing the learning rate of the improved long-term and short-term neural network according to a preset parameter adaptive adjustment optimization algorithm, and obtaining a short-term load forecasting model; A real-time load impact sub-signal is obtained, and the real-time load impact sub-signal is input into the short-term load forecasting model, and the short-term load forecasting model outputs a load forecasting result.

[0006] In a second aspect, the present invention provides a short-term load forecasting system based on an improved GWO-VMD-LSTM, comprising: a determination module configured to determine the number of modal decompositions and a penalty factor of the variational modal decomposition according to an improved grey wolf optimization algorithm; a decomposition module configured to perform variational mode decomposition on the load characteristic time series signal according to the mode decomposition quantity and the penalty factor to obtain at least one IMF component signal, wherein the IMF component signal includes a load impact sub-signal and a load result sub-signal corresponding to the load impact sub-signal; A processing module, configured to perform denoising processing on the at least one IMF component signal according to a preset wavelet packet soft threshold function to obtain at least one target IMF component signal; A training module is configured to input the at least one target IMF component signal into an improved long-term and short-term neural network, dynamically optimize the learning rate of the improved long-term and short-term neural network according to a preset parameter adaptive adjustment optimization algorithm, and obtain a short-term load forecasting model; The output module is configured to obtain a real-time load impact sub-signal, input the real-time load impact sub-signal into the short-term load forecasting model, and the short-term load forecasting model outputs a load forecasting result.

[0007] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the short-term load forecasting method based on the improved GWO-VMD-LSTM of any embodiment of the present invention.

[0008] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program instructions are executed by a processor, the processor executes the steps of the short-term load forecasting method based on the improved GWO-VMD-LSTM of any embodiment of the present invention.

[0009] The short-term load forecasting method and system based on the improved GWO-VMD-LSTM of the present application improves the gray wolf optimization algorithm, performs parameter optimization on the modal decomposition quantity and penalty factor that must be determined before VMD feature extraction, performs variational modal decomposition on the load characteristic time series signal, decomposes it into a series of intrinsic modal function components, uses an improved wavelet packet soft threshold function, proposes a sluggish-greed mechanism to improve the function, denoises the IMF component signal, extracts the characteristic coefficient and modal kurtosis value of each component, and evaluates the denoising effect; improves the network structure of the long-term and short-term neural network, proposes a parameter adaptive adjustment optimization algorithm, dynamically optimizes the learning rate of the LSTM, and inputs data into the network for training and prediction, which can improve the accuracy of short-term load forecasting and the accuracy of classification, and is conducive to the short-term accurate prediction of power load. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flowchart of a short-term load forecasting method based on an improved GWO-VMD-LSTM provided by an embodiment of the present invention; Figure 2 A structural block diagram of a short-term load forecasting system based on an improved GWO-VMD-LSTM provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] See also Figure 1 , which shows a flow chart of a short-term load forecasting method based on improved GWO-VMD-LSTM of the present application.

[0014] like Figure 1As shown in FIG. 1 , the short-term load forecasting method based on the improved GWO-VMD-LSTM specifically includes the following steps: Step S101, determining the modal decomposition quantity and penalty factor of variational modal decomposition according to the improved grey wolf optimization algorithm.

[0015] In this step, the gray wolf optimization algorithm (GWO) is improved, and the number of modal decompositions and penalty factors that must be determined before the variational modal decomposition feature extraction are optimized. The specific content is: the envelope exploration weighting factor is introduced, so that the weighting factor can be properly and timely dynamically adjusted based on the changes in the hunting state of the wolf pack, so as to enhance the adaptability and mobility of the algorithm, and thus better cope with the complex hunting optimization environment. In order to expand the global hunting coverage of the algorithm, the alpha wolf hunting leadership and cooperation strategy is proposed. Under the hunting leadership and cooperation strategy of the alpha wolf, the wolf pack constantly adjusts the position of the individuals in the group and approaches the prey in an orderly manner to hunt. Specifically, the envelope escape coefficient and exploration weighting factor under the current iteration are calculated, and the expression is: , , In the formula, is the envelope escape coefficient at the tth iteration, is the exploration weight factor at the tth iteration, is the total number of gray wolf individuals, is the probability distribution sequence of the i-th gray wolf individual, is the wolf pack aliasing parameter sequence at the tth iteration, is the exploration correction factor, is the objective weight coefficient, is the upper and lower limit interval of the number of iterations, is a non-zero number between 0 and 1. Empowering factors for the wolf pack; Determine the leader hunting leadership cooperation strategy of the gray wolf algorithm, the expression is: , , In the formula, , , They are Wolf pack leader, Wolf pack leader, The hunting influence coefficient between the wolf pack leader and other gray wolves, is the comprehensive hunting influence coefficient of the wolf pack leader, is the mean function, , , They are The orientation factor of the wolf pack leader, The orientation factor of the wolf pack leader, The orientation factor of the wolf pack leader, , , They are The current location of the wolf pack leader, The current location of the wolf pack leader, The current location of the wolf pack leader. , , They are The orientation correlation coefficient of the wolf pack leader, The orientation correlation coefficient of the wolf pack leader, The orientation correlation coefficient of the wolf pack leader, is the current position of the prey, , , They are The deviation coefficient of the wolf pack, The deviation coefficient of the wolf pack, The deviation coefficient of the wolf pack, is the two-norm operator, is the position of the gray wolf individual at the tth iteration, is the convergence resolution coefficient, is the orientation coefficient of the prey at the tth iteration, is a natural constant, and are the upper and lower limits of the hunting range, is the prey displacement probability factor ranging from 0 to 1; The number of modal decompositions and the penalty factor combination that need to be optimized are used as the original input of the gray wolf iterative position, and the iterative update position output, that is, the optimal solution combination, is obtained. The expression is: , In the formula, is the iterative update position of the nth gray wolf in the m-dimensional space, is the matrix window function, is the prey position constant, is the prey space offset factor, is the gray wolf's azimuth inertia matrix, is the maximum number of iterations, is the determination coefficient of the initial position of the prey.

[0016] Step S102, performing variational modal decomposition on the load characteristic time series signal according to the modal decomposition quantity and the penalty factor to obtain at least one IMF component signal, wherein the IMF component signal includes a load impact sub-signal and a load result sub-signal corresponding to the load impact sub-signal.

[0017] In this step, the load characteristic time series signal is subjected to variational mode decomposition (VMD) to decompose it into a series of intrinsic mode function components (IMF). Specifically, the original time series signal is decomposed into several intrinsic mode functions with different bandwidth constraints and fluctuating around the center frequency. The Wiener filter and alternating direction multiplier method are used to iteratively update each sub-modal function and the center frequency. The ultimate goal is to reconstruct the sum of each IMF as close to the original signal as possible and minimize the sum of each modal bandwidth. Specifically: Construct the variational problem equation and calculate the IMF components decomposed from the original signal , continue to solve the square norm of the demodulated signal gradient to obtain the bandwidth of each mode, and construct the constrained variational problem equation, the expression is: , , In the formula, for The amplitude of for The phase angle, is the original signal, For decomposition A set of IMF components, for The set of center frequencies of the modes, is the Tikhonov matrix, is the impulse function, is an imaginary unit, is the number of modal components; The constrained variational problem is transformed into an unconstrained form, the Lagrangian augmented function is constructed, and the alternating direction multiplier method is used to solve the IMF components and The center frequency of the mode Iterative update is completed by updating K intrinsic mode functions, that is, updating the Lagrange multiplier To update, the expression is: , , , , In the formula, for The Lagrangian augmented function of is the pulse function, is the quadratic penalty term, is an imaginary unit, is the current iteration number, , , They are The Fourier transform of The Fourier transform of The Fourier transform of is the frequency, For the The center frequency of the mode, For the The iteration The center frequency of the mode, For the The Fourier transform of the Lagrange multiplier at the iteration is, For the The Fourier transform of the Lagrange multiplier at the iteration is, For the At iteration Fourier transform of If the convergence condition is met, the iteration is stopped and at least one IMF component signal is output, otherwise the iteration continues. The expression of the convergence condition is: , In the formula, To determine the accuracy and meet .

[0018] Step S103: performing denoising processing on the at least one IMF component signal according to a preset wavelet packet soft threshold function to obtain at least one target IMF component signal.

[0019] In this step, a wavelet packet soft threshold function is constructed so that the IMF component can better reflect the characteristics of the original signal at different frequencies. A sluggish-greed mechanism is proposed to denoise the IMF component signal, extract the characteristic coefficients and modal kurtosis values ​​of each component, and evaluate the denoising effect, which effectively suppresses the "over-killing" phenomenon and improves the signal-to-noise ratio of the denoised signal, which is conducive to extracting the influencing characteristics of power load forecasting and improving the analysis accuracy and reliability of power load data time series. Specifically: Construct the wavelet packet soft threshold function, obtain the IMF component after wavelet packet transformation, and calculate the wavelet packet soft threshold. The expression is: , , In the formula, is the IMF component after the kth wavelet packet transform, is the spectral flatness coefficient, is the number of IMF components, For the The signal-to-noise ratio of the IMF components, is the frequency parameter, is the sliding parameter, is the wavelet basis constant, is the wavelet noise factor, For the The original signal projection value of the IMF component, is the characteristic information parameter, is the wavelet packet soft threshold, is the symbolic function, is the local threshold; Considering that there are many factors affecting power load forecasting and the complexity of noise signals is high, in order to improve the prediction accuracy of the algorithm, an IMF component sluggish-greed denoising mechanism is proposed. This mechanism obtains the greed weight factor according to the frequency characteristics and disturbance characteristics of the noise. , as the judgment parameter of the sluggish function, calculate the wavelet packet soft threshold coefficient to denoise the IMF component and filter out the interference noise signal that is not conducive to the accurate prediction of the algorithm. The expression is: , , , In the formula, is the greed trade-off factor, is the activation function, For Sluggish parsing function, is the IMF component weight matrix, is the IMF component bias matrix, is the wavelet gradient state parameter, is the interference weight, For the The wavelet packet soft threshold coefficients of the IMF components are For the The execution threshold value of each IMF component, is the same frequency disturbance factor, For the The gamma weights of the IMF components, is the kth IMF component denoised by wavelet packet soft thresholding.

[0020] Step S104, inputting the at least one target IMF component signal into the improved long-term and short-term neural network, dynamically optimizing the learning rate of the improved long-term and short-term neural network according to a preset parameter adaptive adjustment optimization algorithm, and obtaining a short-term load forecasting model.

[0021] In this step, the expression of the loss function of the improved long-term short-term neural network is: , In the formula, is the Boltzmann-cross entropy loss function, For the sample, is the true value, is the predicted value, is the cross entropy quantization factor, is the generalization coefficient.

[0022] The learning rate is an important hyperparameter that controls the update step size of the LSTM model parameters. It determines that the size of the parameters should be adjusted in the opposite direction of the gradient each time the parameters are updated. If the learning rate is too large or too small, the model will not converge, the training process will be slow, the convergence speed will be slow, and it may fall into the local optimal solution. Therefore, a Levy-Adam parameter optimization algorithm is introduced to automatically adjust the learning rate according to the gradient of the parameters to adapt to the update requirements of different parameters, so as to accelerate the training process of the model and improve the performance of the model. The expression of the parameter adaptive adjustment optimization algorithm is: , , , , , In the formula, is the first-order Adam momentum at the t-th iteration, is the first-order Adam momentum at the t-1th iteration, is the first-order momentum hyperparameter, is the Levy distribution coefficient, is the Adam second-order momentum at the t-th iteration, is the Adam second-order momentum at the t-1th iteration, is the second-order momentum hyperparameter, is the Levy update value of the first-order momentum, is the momentum gradient descent rate, is the Levy modified index, is the Levy update value of the second-order momentum, is the hyperparameter update of the learning rate at the tth iteration, is the learning rate at the tth iteration, is the learning rate at the t-1th iteration.

[0023] Step S105, obtaining a real-time load impact sub-signal, inputting the real-time load impact sub-signal into the short-term load forecasting model, and the short-term load forecasting model outputs a load forecasting result.

[0024] In summary, the method and system of the present application improve the gray wolf optimization algorithm, perform parameter optimization on the modal decomposition number and penalty factor that must be determined before VMD feature extraction, perform variational modal decomposition on the load characteristic time series signal, decompose it into a series of intrinsic modal function components, use an improved wavelet packet soft threshold function, propose a sluggish-greed mechanism to improve the function, denoise the IMF component signal, extract the characteristic coefficient and modal kurtosis value of each component, and evaluate the denoising effect; improve the network structure of the long-term and short-term neural network, propose a parameter adaptive adjustment optimization algorithm, dynamically optimize the learning rate of LSTM, and input data into the network for training and prediction, which can improve the accuracy of short-term load prediction and classification accuracy, and is conducive to the short-term accurate prediction of power load.

[0025] See also Figure 2 , which shows a structural block diagram of a short-term load forecasting system based on improved GWO-VMD-LSTM of the present application.

[0026] like Figure 2 As shown, the short-term load forecasting system 200 includes a determination module 210 , a decomposition module 220 , a processing module 230 , a training module 240 and an output module 250 .

[0027] Among them, the determination module 210 is configured to determine the modal decomposition number and penalty factor of the variational modal decomposition according to the improved grey wolf optimization algorithm; the decomposition module 220 is configured to perform variational modal decomposition on the load characteristic time series signal according to the modal decomposition number and the penalty factor to obtain at least one IMF component signal, wherein the IMF component signal includes a load impact sub-signal and a load result sub-signal corresponding to the load impact sub-signal; the processing module 230 is configured to perform denoising processing on the at least one IMF component signal according to a preset wavelet packet soft threshold function to obtain at least one target IMF component signal; the training module 240 is configured to input the at least one target IMF component signal into the improved long-term and short-term neural network, dynamically optimize the learning rate of the improved long-term and short-term neural network according to a preset parameter adaptive adjustment optimization algorithm, and obtain a short-term load forecasting model; the output module 250 is configured to obtain a real-time load impact sub-signal, input the real-time load impact sub-signal into the short-term load forecasting model, and the short-term load forecasting model outputs the load forecasting result.

[0028] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0029] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor is caused to execute the short-term load forecasting method based on the improved GWO-VMD-LSTM in any of the above method embodiments; As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows: Determine the modal decomposition quantity and penalty factor of variational modal decomposition according to the improved grey wolf optimization algorithm; Performing variational mode decomposition on the load characteristic time series signal according to the mode decomposition quantity and the penalty factor to obtain at least one IMF component signal, wherein the IMF component signal includes a load influence sub-signal and a load result sub-signal corresponding to the load influence sub-signal; Performing denoising processing on the at least one IMF component signal according to a preset wavelet packet soft threshold function to obtain at least one target IMF component signal; Inputting the at least one target IMF component signal into an improved long-term and short-term neural network, dynamically optimizing the learning rate of the improved long-term and short-term neural network according to a preset parameter adaptive adjustment optimization algorithm, and obtaining a short-term load forecasting model; A real-time load impact sub-signal is obtained, and the real-time load impact sub-signal is input into the short-term load forecasting model, and the short-term load forecasting model outputs a load forecasting result.

[0030] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the short-term load forecasting system based on the improved GWO-VMD-LSTM, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the short-term load forecasting system based on the improved GWO-VMD-LSTM via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0031] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 In the example, the connection through the bus is taken as an example. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the short-term load forecasting method based on the improved GWO-VMD-LSTM in the above-mentioned method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the short-term load forecasting system based on the improved GWO-VMD-LSTM. The output device 340 may include display devices such as display screens.

[0032] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0033] As an implementation mode, the electronic device is applied to a short-term load forecasting system based on an improved GWO-VMD-LSTM, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: Determine the modal decomposition quantity and penalty factor of variational modal decomposition according to the improved grey wolf optimization algorithm; Performing variational mode decomposition on the load characteristic time series signal according to the mode decomposition quantity and the penalty factor to obtain at least one IMF component signal, wherein the IMF component signal includes a load influence sub-signal and a load result sub-signal corresponding to the load influence sub-signal; Performing denoising processing on the at least one IMF component signal according to a preset wavelet packet soft threshold function to obtain at least one target IMF component signal; Inputting the at least one target IMF component signal into an improved long-term and short-term neural network, dynamically optimizing the learning rate of the improved long-term and short-term neural network according to a preset parameter adaptive adjustment optimization algorithm, and obtaining a short-term load forecasting model; A real-time load impact sub-signal is obtained, and the real-time load impact sub-signal is input into the short-term load forecasting model, and the short-term load forecasting model outputs a load forecasting result.

[0034] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A short-term load forecasting method based on improved GWO-VMD-LSTM, characterized in that: include: Determine the modal decomposition quantity and penalty factor of variational modal decomposition according to the improved grey wolf optimization algorithm; Performing variational mode decomposition on the load characteristic time series signal according to the modal decomposition quantity and the penalty factor to obtain at least one IMF component signal, wherein the IMF component signal includes a load influence sub-signal and a load result sub-signal corresponding to the load influence sub-signal; Performing denoising processing on the at least one IMF component signal according to a preset wavelet packet soft threshold function to obtain at least one target IMF component signal; Inputting the at least one target IMF component signal into an improved long-term and short-term neural network, dynamically optimizing the learning rate of the improved long-term and short-term neural network according to a preset parameter adaptive adjustment optimization algorithm, and obtaining a short-term load forecasting model; A real-time load impact sub-signal is obtained, and the real-time load impact sub-signal is input into the short-term load forecasting model, and the short-term load forecasting model outputs a load forecasting result.

2. According to the method for short-term load forecasting based on improved GWO-VMD-LSTM in claim 1, it is characterized in that: The method of determining the modal decomposition quantity and penalty factor of the variational modal decomposition according to the improved grey wolf optimization algorithm includes: Calculate the envelope escape coefficient and exploration weight factor under the current iteration. The expression is: ， ， In the formula, is the envelope escape coefficient at the tth iteration, is the exploration weight factor at the tth iteration, is the total number of gray wolf individuals, is the probability distribution sequence of the i-th gray wolf individual, is the wolf pack aliasing parameter sequence at the tth iteration, is the exploration correction factor, is the objective weight coefficient, is the upper and lower limit interval of the number of iterations, is a non-zero number between 0 and 1. Empowering factors for the wolf pack; Determine the leader hunting leadership cooperation strategy of the gray wolf algorithm, the expression is: ， ， In the formula, , , They are Wolf pack leader, Wolf pack leader, The hunting influence coefficient between the wolf pack leader and other gray wolves, is the comprehensive hunting influence coefficient of the wolf pack leader, is the mean function, , , They are The orientation factor of the wolf pack leader, The orientation factor of the wolf pack leader, The orientation factor of the wolf pack leader, , , They are The current location of the wolf pack leader, The current location of the wolf pack leader, The current location of the wolf pack leader. , , They are The orientation correlation coefficient of the wolf pack leader, The orientation correlation coefficient of the wolf pack leader, The orientation correlation coefficient of the wolf pack leader, is the current position of the prey, , , They are The deviation coefficient of the wolf pack, The deviation coefficient of the wolf pack, The deviation coefficient of the wolf pack, is the two-norm operator, is the position of the gray wolf individual at the tth iteration, is the convergence resolution coefficient, is the orientation coefficient of the prey at the tth iteration, is a natural constant, and are the upper and lower limits of the hunting range, is the prey displacement probability factor ranging from 0 to 1; The number of modal decompositions and the penalty factor combination that need to be optimized are used as the original input of the gray wolf iterative position, and the iterative update position output, that is, the optimal solution combination, is obtained. The expression is: ， In the formula, is the iterative update position of the nth gray wolf in the m-dimensional space, is the matrix window function, is the prey position constant, is the prey space offset factor, is the gray wolf's azimuth inertia matrix, is the maximum number of iterations, is the determination coefficient of the initial position of the prey.

3. The short-term load forecasting method based on improved GWO-VMD-LSTM according to claim 1 is characterized in that: The step of performing variational modal decomposition on the load characteristic time series signal according to the modal decomposition quantity and the penalty factor to obtain at least one IMF component signal comprises: Construct the variational problem equation and calculate the IMF components decomposed from the original signal , continue to solve the square norm of the demodulated signal gradient to obtain the bandwidth of each mode, and construct the constrained variational problem equation, the expression is: ， ， In the formula, for The amplitude of for The phase angle, is the original signal, For decomposition A set of IMF components, for The set of center frequencies of the modes, is the Tikhonov matrix, is the impulse function, is an imaginary unit, is the number of modal components; The constrained variational problem is transformed into an unconstrained form, the Lagrangian augmented function is constructed, and the alternating direction multiplier method is used to solve the IMF Components and The center frequency of the mode Iterative update is completed by updating K intrinsic mode functions, that is, updating the Lagrange multiplier To update, the expression is: ， ， ， ， In the formula, for The Lagrangian augmented function of is the pulse function, is the quadratic penalty term, is an imaginary unit, is the current iteration number, , , They are The Fourier transform of The Fourier transform of The Fourier transform of is the frequency, For the The center frequency of the mode, For the The first iteration The center frequency of the mode, For the The Fourier transform of the Lagrange multiplier at the iteration is, For the The Fourier transform of the Lagrange multiplier at the iteration is, For the At iteration Fourier transform of If the convergence condition is met, the iteration is stopped and at least one IMF component signal is output, otherwise the iteration continues. The expression of the convergence condition is: ， In the formula, To determine the accuracy and meet .

4. The short-term load forecasting method based on improved GWO-VMD-LSTM according to claim 1 is characterized in that: The performing denoising processing on the at least one IMF component signal according to a preset wavelet packet soft threshold function to obtain at least one target IMF component signal comprises: Construct the wavelet packet soft threshold function, obtain the IMF component after wavelet packet transformation, and calculate the wavelet packet soft threshold. The expression is: ， ， In the formula, is the IMF component after the kth wavelet packet transform, is the spectral flatness coefficient, is the number of IMF components, For the The signal-to-noise ratio of the IMF components, is the frequency parameter, is the sliding parameter, is the wavelet basis constant, is the wavelet noise factor, For the The original signal projection value of the IMF component, is the characteristic information parameter, is the wavelet packet soft threshold, is the symbolic function, is the local threshold; Get the greed trade-off factor , as the judgment parameter of the sluggish function, calculate the wavelet packet soft threshold coefficient to denoise the IMF component. The expression is: ， ， ， In the formula, is the greed trade-off factor, is the activation function, For Sluggish parsing function, is the IMF component weight matrix, is the IMF component bias matrix, is the wavelet gradient state parameter, is the interference weight, For the The wavelet packet soft threshold coefficients of the IMF components are For the The execution threshold value of each IMF component, is the same frequency disturbance factor, For the The gamma weights of the IMF components, is the kth IMF component denoised by wavelet packet soft thresholding.

5. The short-term load forecasting method based on improved GWO-VMD-LSTM according to claim 1 is characterized in that: After performing denoising processing on the at least one IMF component signal according to a preset wavelet packet soft threshold function, the method further includes: According to the wavelet packet soft threshold coefficient obtained by the sluggish-greed mechanism ,Will As the input parameter of IMF judgment index, each IMF component decomposed by VMD is reconstructed and demodulated, and the denoising effect judgment index of each IMF component, namely the modal kurtosis value and characteristic coefficient, is calculated. The expression is: ， ， ， ， In the formula, is the noise signal sensitive period, is the clockwise contour integral, For the The signal sampling frequency of the IMF component is is the signal noise frequency, is the maximum frequency of the IMF component, For the The modal kurtosis value of the IMF component, is the mixing kurtosis parameter, For the The covariance of the IMF components, is the global interference coefficient of white noise, For the The shifted mean of the IMF components, is the shift coefficient, For the The ambient noise parameters of the IMF components are: For the The characteristic coefficients of the IMF components are is the signal strength coefficient.

6. The short-term load forecasting method based on improved GWO-VMD-LSTM according to claim 1 is characterized in that: The expression of the parameter adaptive adjustment optimization algorithm is: ， ， ， ， ， In the formula, is the first-order Adam momentum at the t-th iteration, is the first-order Adam momentum at the t-1th iteration, is the first-order momentum hyperparameter, is the Levy distribution coefficient, is the Adam second-order momentum at the t-th iteration, is the Adam second-order momentum at the t-1th iteration, is the second-order momentum hyperparameter, is the Levy update value of the first-order momentum, is the momentum gradient descent rate, is the Levy modified index, is the Levy update value of the second-order momentum, is the hyperparameter update of the learning rate at the tth iteration, is the learning rate at the tth iteration, is the learning rate at the t-1th iteration.

7. The short-term load forecasting method based on improved GWO-VMD-LSTM according to claim 1 is characterized in that: The expression of the loss function of the improved long short-term neural network is: ， In the formula, is the Boltzmann-cross entropy loss function, For the sample, is the true value, is the predicted value, is the cross entropy quantization factor, is the generalization coefficient.

8. A short-term load forecasting system based on improved GWO-VMD-LSTM, characterized in that: include: a determination module configured to determine the number of modal decompositions and a penalty factor of the variational modal decomposition according to an improved grey wolf optimization algorithm; a decomposition module configured to perform variational mode decomposition on the load characteristic time series signal according to the mode decomposition quantity and the penalty factor to obtain at least one IMF component signal, wherein the IMF component signal includes a load impact sub-signal and a load result sub-signal corresponding to the load impact sub-signal; A processing module, configured to perform denoising processing on the at least one IMF component signal according to a preset wavelet packet soft threshold function to obtain at least one target IMF component signal; A training module is configured to input the at least one target IMF component signal into an improved long-term and short-term neural network, dynamically optimize the learning rate of the improved long-term and short-term neural network according to a preset parameter adaptive adjustment optimization algorithm, and obtain a short-term load forecasting model; The output module is configured to obtain a real-time load impact sub-signal, input the real-time load impact sub-signal into the short-term load forecasting model, and the short-term load forecasting model outputs a load forecast result.

9. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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