Photovoltaic power generation power prediction method and system based on improved DBO-LSTM neural network

Abstract

The invention discloses a photovoltaic power generation power prediction method and system based on an improved DBO-LSTM neural network, and the method comprises the steps: obtaining photovoltaic data, and carrying out the data preprocessing of the photovoltaic data; performing correlation analysis on the preprocessed photovoltaic data, and selecting at least one piece of target photovoltaic data of which the correlation coefficient is greater than a preset threshold value according to an analysis result; and inputting the at least one piece of target photovoltaic data into an improved LSTM neural network, carrying out iterative training on the improved LSTM neural network to obtain a photovoltaic power generation prediction model, inputting the obtained real-time meteorological sub-data into the photovoltaic power generation prediction model, and outputting by the photovoltaic power generation prediction model to obtain a photovoltaic power generation power prediction result. The accuracy of photovoltaic power generation power prediction is improved, and dispatching and control of an electric power system, electric power market transaction, optimization of an energy storage system and operation and maintenance of a photovoltaic power station are facilitated.

Description

Technical Field

[0001] The present invention belongs to the field of energy and electric power technology, and in particular relates to a photovoltaic power generation power prediction method and system based on an improved DBO-LSTM neural network. Background Art

[0002] As a renewable energy source with abundant reserves and environmental friendliness, solar energy plays an important role in the sustainable development of energy by achieving efficient utilization of clean energy through photovoltaic power generation. However, the inherent randomness of sunlight and the rhythmic diurnal cycle bring fluctuations and intermittency to photovoltaic power generation. In addition, various factors such as weather and environmental conditions affect the output of photovoltaic systems, contributing to the significant unpredictability of power generation when large-scale photovoltaic power generation is connected to the grid. Therefore, this unpredictability will have a negative impact on the planning of the power grid. Considering the influence of the time-varying characteristics of multi-factor coupling, accurate prediction of photovoltaic power generation is effective for improving the stability of the power grid, enhancing the power grid's ability to absorb photovoltaic power generation, and improving the operating efficiency of photovoltaic power stations.

[0003] As a special type of recurrent neural network (RNN), LSTM neural network aims to solve the long-term dependency problem encountered by traditional RNN when processing long sequences, and has been widely used in fields such as time series prediction. However, LSTM neural network also has some disadvantages, mainly including: (1) complex calculation; (2) requiring a large amount of data for training; (3) long training time. Summary of the invention

[0004] The present invention provides a photovoltaic power generation power prediction method and system based on an improved DBO (Dung Beetle Optimizer)-LSTM neural network, which are used to solve the technical problems of insufficient processing capability of existing models for nonlinear relationships and time series characteristics, and the inefficiency of traditional parameter optimization methods.

[0005] In a first aspect, the present invention provides a photovoltaic power prediction method based on an improved DBO-LSTM neural network, comprising: Acquire photovoltaic data and perform data preprocessing on the photovoltaic data, wherein the photovoltaic data includes meteorological sub-data and photovoltaic power generation data corresponding to the meteorological sub-data; Performing correlation analysis on the preprocessed photovoltaic data, and selecting at least one target photovoltaic data having a correlation coefficient greater than a preset threshold value according to the analysis result, wherein the target photovoltaic data includes target meteorological sub-data and target photovoltaic data corresponding to the target meteorological sub-data; The at least one target photovoltaic data is input into the improved LSTM neural network, and the improved LSTM neural network is iteratively trained to obtain a photovoltaic power generation prediction model, wherein the improved LSTM neural network is obtained by optimizing the hyperparameters of the LSTM neural network according to the improved dung beetle optimization algorithm, and the expression of the dung beetle function mapping strategy in the improved dung beetle optimization algorithm is: , , , , , In the formula, is the probability that a dung beetle is assigned as a rolling ball dung beetle, is the probability of a dung beetle being assigned as a breeding dung beetle, is the probability that a dung beetle is assigned as a foraging dung beetle, is the probability that a dung beetle is assigned as a thieving dung beetle, For the The fitness of a dung beetle at the tth iteration, is the minimum fitness of the current population, is the maximum fitness of the current population, is the population size, For the The fitness of a dung beetle at the tth iteration, is the average fitness of all dung beetles in the population at the tth iteration, The ratio of reproduction and foraging functions to the number of iterations; The acquired real-time meteorological sub-data is input into the photovoltaic power generation prediction model, and the photovoltaic power generation prediction model outputs a photovoltaic power generation power prediction result.

[0006] In a second aspect, the present invention provides a photovoltaic power generation prediction system based on an improved DBO-LSTM neural network, comprising: A processing module configured to obtain photovoltaic data and perform data preprocessing on the photovoltaic data, wherein the photovoltaic data includes meteorological sub-data and photovoltaic power generation data corresponding to the meteorological sub-data; A selection module is configured to perform a correlation analysis on the preprocessed photovoltaic data, and select at least one target photovoltaic data having a correlation coefficient greater than a preset threshold value according to the analysis result, wherein the target photovoltaic data includes target meteorological sub-data and target photovoltaic data corresponding to the target meteorological sub-data; The training module is configured to input the at least one target photovoltaic data into an improved LSTM neural network, iteratively train the improved LSTM neural network, and obtain a photovoltaic power generation prediction model, wherein the improved LSTM neural network is obtained by optimizing the hyperparameters of the LSTM neural network according to an improved dung beetle optimization algorithm, and the expression of the dung beetle function mapping strategy in the improved dung beetle optimization algorithm is: , , , , , In the formula, is the probability that a dung beetle is assigned as a rolling ball dung beetle, is the probability of a dung beetle being assigned as a breeding dung beetle, is the probability that a dung beetle is assigned as a foraging dung beetle, is the probability that a dung beetle is assigned as a thieving dung beetle, For the The fitness of a dung beetle at the tth iteration, is the minimum fitness of the current population, is the maximum fitness of the current population, is the population size, For the The fitness of a dung beetle at the tth iteration, is the average fitness of all dung beetles in the population at the tth iteration, The ratio of reproduction and foraging functions to the number of iterations; The output module is configured to input the acquired real-time meteorological sub-data into the photovoltaic power generation prediction model, and the photovoltaic power generation prediction model outputs the photovoltaic power generation power prediction 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 photovoltaic power prediction method based on the improved DBO-LSTM neural network according to 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 photovoltaic power generation prediction method based on an improved DBO-LSTM neural network according to any embodiment of the present invention.

[0009] The photovoltaic power prediction method and system based on the improved DBO-LSTM neural network of the present application perform correlation analysis on photovoltaic data, and select at least one target photovoltaic data whose correlation coefficient is greater than a preset threshold value according to the analysis result; input the at least one target photovoltaic data into the improved LSTM neural network, iteratively train the improved LSTM neural network to obtain a photovoltaic power prediction model, input the acquired real-time meteorological sub-data into the photovoltaic power prediction model, and the photovoltaic power prediction model outputs a photovoltaic power prediction result, thereby improving the accuracy of photovoltaic power prediction, which is beneficial to power system scheduling and control, power market transactions, optimization of energy storage systems, and operation and maintenance of photovoltaic power stations. 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 photovoltaic power generation prediction method based on an improved DBO-LSTM neural network provided by an embodiment of the present invention; Figure 2 A structural block diagram of a photovoltaic power generation prediction system based on an improved DBO-LSTM neural network 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 photovoltaic power prediction method based on an improved DBO-LSTM neural network of the present application.

[0014] like Figure 1 As shown, the photovoltaic power generation prediction method based on the improved DBO-LSTM neural network specifically includes the following steps: Step S101, acquiring photovoltaic data and performing data preprocessing on the photovoltaic data, wherein the photovoltaic data includes meteorological sub-data and photovoltaic power generation sub-data corresponding to the meteorological sub-data.

[0015] In this step, the meteorological sub-data and the photovoltaic power generation electronic data are cleaned and converted, wherein the data cleaning is specifically: the abnormal values ​​in the meteorological sub-data and the photovoltaic power generation electronic data are processed according to a multidimensional self-adjusting convolution filter, and the expression is: , , In the formula, is the data after outlier processing. is the sliding process of the filter over the time period, is the original input data, is the time-integrated variable, is the filter, is the adaptive factor, is the time variable, is the degree of nonlinearity, is the attenuation rate of the filter, is the standard deviation of the Gaussian component, To adjust the factor of the sine wave amplitude, is the frequency of the sine wave; The data conversion is specifically as follows: normalizing the meteorological sub-data and the photovoltaic sub-data according to an adaptive dynamic nonlinear normalization strategy, wherein the expression of the adaptive dynamic nonlinear normalization strategy is: , In the formula, For the The normalized value of the feature, For the The weight coefficient of the feature, The original data eigenvalues, For the The minimum value of the features, For the The maximum value of the features, Enter data for the eigenvalues; , In the formula, For the The scaling factor of the features, For the The bias factor of each feature; , In the formula, For the The smoothing coefficient of the feature; , In the formula, is the output of the dynamic nonlinear characteristic regulator.

[0016] Step S102, performing correlation analysis on the preprocessed photovoltaic data, and selecting at least one target photovoltaic data having a correlation coefficient greater than a preset threshold value according to the analysis result, wherein the target photovoltaic data includes target meteorological sub-data and target photovoltaic sub-data corresponding to the target meteorological sub-data.

[0017] In this step, in photovoltaic prediction, it is crucial to conduct correlation analysis on meteorological data. Meteorological factors such as temperature, solar radiation intensity, humidity, wind speed, etc. directly affect the power generation efficiency and stability of the photovoltaic system. By deeply analyzing the correlation between these meteorological data and photovoltaic power generation, photovoltaic power can be predicted more accurately. The weighted multivariate interactive correlation analysis method is introduced to analyze the correlation between each meteorological factor and photovoltaic power generation. Specifically, the correlation coefficient between the meteorological sub-data and the photovoltaic sub-data is calculated according to the preset nonlinear interaction term. The expression is: , , In the formula, To consider the correlation coefficient of nonlinear interaction terms, is the correlation coefficient between the input feature and the output variable, is the number of nonlinear features, is the nonlinear term weight coefficient, For the Input feature values, For variables The average value of For the output variables, For variables The average value of is the standard deviation of the input features, is the standard deviation of the output features, is the frequency parameter of the characteristic, is the weight of the feature, For the Input feature values, For the output variables, is the sample size; At least one meteorological sub-data having a correlation coefficient of a nonlinear interaction term greater than a preset threshold and photovoltaic power generation electronic data corresponding to the at least one meteorological sub-data are selected.

[0018] Step S103: input the at least one target photovoltaic data into an improved LSTM neural network, perform iterative training on the improved LSTM neural network, and obtain a photovoltaic power generation prediction model.

[0019] In a specific embodiment, before inputting the at least one target photovoltaic data into the improved LSTM neural network and iteratively training the improved LSTM neural network to obtain a photovoltaic power generation prediction model, the method further includes: The hyperparameters of the LSTM neural network are optimized according to the improved dung beetle optimization algorithm to obtain an improved LSTM neural network, specifically: During the rolling process of the dung beetle, the first mathematical model for updating the position of the dung beetle is expressed as: , , In the formula, is the updated position of the dung beetle in the t+1th iteration, is the updated position of the dung beetle in the tth iteration, It is the degree of deviation from -1 or 1, 1 means no deviation from the original direction, -1 means deviation from the original direction. is the offset coefficient, is the updated position of the dung beetle in the t-1th iteration, is a constant, To simulate the change of sunlight intensity, is the worst position of the species in the world at present; When the individual cannot move further, the tangent function is used to determine the new rolling direction. The expression of the second mathematical model for updating the position of the dung beetle is: , In the formula, is the deflection angle; Model the dung beetle egg-laying area, the expression is: , , In the formula, is the lower limit of the spawning area, is the current best position, , is the maximum number of iterations, is the current iteration number, is the lower bound of the optimization problem, is the upper limit of the spawning area, is the upper limit of the optimization problem; In each iteration cycle, the spawning position keeps changing, and the mathematical model of the spawning position is expressed as: , In the formula, For the The position of the hatching ball at the t+1th iteration, For the The position of the hatching ball at the tth iteration, , are all random vectors; Adult dung beetles forage to determine the optimal foraging area boundary and update individual positions. The mathematical model of the optimal foraging area boundary is expressed as: , , In the formula, is the lower boundary of the optimal foraging area, is the upper boundary of the optimal foraging area, is the local optimal position of the previous population; The mathematical model for updating individual positions is expressed as: , In the formula, is a random number that conforms to the normal distribution. is a random vector in the range (0,1); In the dung beetle algorithm, there is a behavior of stealing food from other dung beetles, which is called a stealing dung beetle. Therefore, assuming The surrounding area is the best location to compete for food. The mathematical model used to update the position of the dung beetle during the iteration process is expressed as: , In the formula, is a randomly selected D-dimensional vector that conforms to the normal distribution, is a constant; The traditional dung beetle algorithm has the problem of fixed role allocation and difficulty in adapting to dynamic environments. It is often unable to effectively balance global exploration and local development at different evolutionary stages, which makes it easy to fall into local optimal solutions or converge prematurely. Therefore, a dung beetle role mapping strategy based on fitness is used for global solution. The expression of the dung beetle role mapping strategy is: , , , , , In the formula, is the probability that a dung beetle is assigned as a rolling ball dung beetle, is the probability of a dung beetle being assigned as a breeding dung beetle, is the probability that a dung beetle is assigned as a foraging dung beetle, is the probability that a dung beetle is assigned as a thieving dung beetle, For the The fitness of a dung beetle at the tth iteration, is the minimum fitness of the current population, is the maximum fitness of the current population, is the population size, For the The fitness of a dung beetle at the tth iteration, is the average fitness of all dung beetles in the population at the tth iteration, The ratio of reproduction and foraging functions to the number of iterations; This strategy can adjust the allocation of functions in real time according to the performance of individuals at different evolutionary stages, thereby balancing the relationship between global exploration and local development. By considering individual fitness, group distribution and current iteration status, it effectively avoids the local optimal trap caused by the fixed pattern of function allocation. At the same time, it can dynamically adjust the function ratio according to population diversity to improve the overall search efficiency of the group, so that dung beetle individuals can adaptively adjust according to the current group status when dividing labor and cooperating, thereby maximizing resource utilization in the optimization process and improving the robustness and convergence speed of the algorithm.

[0020] The improved dung beetle algorithm is used to iteratively update the hyperparameter combination of the LSTM neural network to maximize fitness and gradually find the optimal hyperparameter configuration. The expression of the optimal hyperparameter configuration is: , In the formula, is the hyperparameter combination for the tth iteration, For the The number of iterations of the LSTM neural network for a dung beetle at the tth iteration, For the The number of neurons in the hidden layer of the LSTM neural network for each dung beetle individual at the tth iteration, For the The learning rate of the LSTM neural network for each dung beetle individual at the tth iteration, is the hyperparameter combination for the t+1th iteration, is the update amount, is the loss function value under the current hyperparameter combination, For the The true value of the samples, For the The predicted value of samples, is the number of samples in the validation set, The goal of fitness optimization.

[0021] It should be noted that in the process of updating the gates of the traditional LSTM neural network, the interaction between the gates is relatively simple and only depends on the input and hidden state, making it difficult to capture complex timing features. Therefore, the improved LSTM neural network includes: a high-order interactive gating mechanism for the interaction between the forget gate, input gate, and output gate, as well as a time-dependent attenuation layer normalization strategy.

[0022] The expression of the higher-order interaction gating mechanism is; , , , , In the formula, is the interaction function between the hidden gate and the input gate, is the interaction function between the hidden gate and the output gate, is the interaction function between the input gate and the input gate, is the learnable weight matrix, is the activation value of the forget gate at the tth time step, is the activation value of the input gate at the tth time step, is the activation value of the output gate at the tth time step, For current input The weight matrix of For the previous moment The weight matrix of the hidden state, is the input data of the current time step, is the hidden state of the previous time step, , are scalar parameters, is the dot product, To multiply; The high-order interactive gating mechanism enables each gate to not only interact more deeply with other gates, but also finely regulate the flow of information through the gating matrix. The introduction of high-order nonlinear interactions enables more complex relationships between gates, thereby enhancing the model's ability to capture long-range dependencies and fine-grained patterns. In addition, this complex interactive structure helps avoid the gradient vanishing problem and enables the model to show higher accuracy and robustness when processing long sequence data. Through these enhancements, the improved LSTM neural network can perform better when facing highly nonlinear and complex time series tasks.

[0023] In order to accelerate the training process of the neural network and improve the generalization performance of the model, a time-dependent attenuation layer normalization strategy is added after the memory unit is updated. The expression is: , , , , , , In the formula, is the memory cell state of the LSTM neural network at time step t, is the dynamic mean of the memory unit at time step t, is the dynamic standard deviation of the memory unit at time step t, is the time-integrated variable, is the fluctuation frequency, is the memory cell state of the LSTM neural network at time step i, is a nonlinear normalization function, is the attenuation transformation function, For time The state of the memory unit at the moment, is the normalized memory cell state, is the normalized equilibrium impact factor, is the decay rate, is the time-dependent normalization function.

[0024] Step S104, inputting the acquired real-time meteorological sub-data into the photovoltaic power generation prediction model, and the photovoltaic power generation prediction model outputs a photovoltaic power generation power prediction result.

[0025] In summary, the method of the present application performs correlation analysis on photovoltaic data, and selects at least one target photovoltaic data whose correlation coefficient is greater than a preset threshold value according to the analysis results; inputs at least one target photovoltaic data into an improved LSTM neural network, iteratively trains the improved LSTM neural network to obtain a photovoltaic power generation prediction model, inputs the acquired real-time meteorological sub-data into the photovoltaic power generation prediction model, and the photovoltaic power generation prediction model outputs a photovoltaic power generation power prediction result, which improves the accuracy of photovoltaic power generation power prediction, and is beneficial to power system scheduling and control, power market transactions, optimization of energy storage systems, and operation and maintenance of photovoltaic power stations.

[0026] See also Figure 2 , which shows a structural block diagram of a photovoltaic power generation prediction system based on an improved DBO-LSTM neural network of the present application.

[0027] like Figure 2 As shown, the photovoltaic power generation prediction system 200 includes a processing module 210 , a selection module 220 , a training module 230 and an output module 240 .

[0028] Among them, the processing module 210 is configured to obtain photovoltaic data and perform data preprocessing on the photovoltaic data, wherein the photovoltaic data includes meteorological sub-data and photovoltaic power generation electronic data corresponding to the meteorological sub-data; the selection module 220 is configured to perform correlation analysis on the preprocessed photovoltaic data, and select at least one target photovoltaic data whose correlation coefficient is greater than a preset threshold according to the analysis result, wherein the target photovoltaic data includes target meteorological sub-data and target photovoltaic power generation electronic data corresponding to the target meteorological sub-data; the training module 230 is configured to input the at least one target photovoltaic data into the improved LSTM neural network, iteratively train the improved LSTM neural network, and obtain a photovoltaic power generation prediction model; the output module 240 is configured to input the acquired real-time meteorological sub-data into the photovoltaic power generation prediction model, and the photovoltaic power generation prediction model outputs the photovoltaic power generation power prediction result.

[0029] 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.

[0030] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, and when the program instructions are executed by a processor, the processor executes the photovoltaic power generation prediction method based on the improved DBO-LSTM neural network 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: Acquire photovoltaic data and perform data preprocessing on the photovoltaic data, wherein the photovoltaic data includes meteorological sub-data and photovoltaic power generation data corresponding to the meteorological sub-data; Performing correlation analysis on the preprocessed photovoltaic data, and selecting at least one target photovoltaic data having a correlation coefficient greater than a preset threshold value according to the analysis result, wherein the target photovoltaic data includes target meteorological sub-data and target photovoltaic data corresponding to the target meteorological sub-data; Inputting the at least one target photovoltaic data into an improved LSTM neural network, iteratively training the improved LSTM neural network, and obtaining a photovoltaic power generation prediction model; The acquired real-time meteorological sub-data is input into the photovoltaic power generation prediction model, and the photovoltaic power generation prediction model outputs a photovoltaic power generation power prediction result.

[0031] 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, an application required for at least one function; the data storage area may store data created based on the use of a photovoltaic power generation prediction system based on an improved DBO-LSTM neural network, 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 disposed relative to the processor, and these remote memories may be connected to the photovoltaic power generation prediction system based on the improved DBO-LSTM neural network 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.

[0032] 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 3In the example, the bus connection is used. 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 photovoltaic power prediction method based on the improved DBO-LSTM neural network 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 photovoltaic power prediction system based on the improved DBO-LSTM neural network. The output device 340 may include display devices such as display screens.

[0033] 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.

[0034] As an implementation mode, the electronic device is applied to a photovoltaic power generation prediction system based on an improved DBO-LSTM neural network, 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: Acquire photovoltaic data and perform data preprocessing on the photovoltaic data, wherein the photovoltaic data includes meteorological sub-data and photovoltaic power generation data corresponding to the meteorological sub-data; Performing correlation analysis on the preprocessed photovoltaic data, and selecting at least one target photovoltaic data having a correlation coefficient greater than a preset threshold value according to the analysis result, wherein the target photovoltaic data includes target meteorological sub-data and target photovoltaic data corresponding to the target meteorological sub-data; Inputting the at least one target photovoltaic data into an improved LSTM neural network, iteratively training the improved LSTM neural network, and obtaining a photovoltaic power generation prediction model; The acquired real-time meteorological sub-data is input into the photovoltaic power generation prediction model, and the photovoltaic power generation prediction model outputs a photovoltaic power generation power prediction result.

[0035] 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.

[0036] 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 photovoltaic power generation prediction method based on an improved DBO-LSTM neural network, characterized in that: include: Acquire photovoltaic data and perform data preprocessing on the photovoltaic data, wherein the photovoltaic data includes meteorological sub-data and photovoltaic power generation data corresponding to the meteorological sub-data; Performing correlation analysis on the preprocessed photovoltaic data, and selecting at least one target photovoltaic data having a correlation coefficient greater than a preset threshold value according to the analysis result, wherein the target photovoltaic data includes target meteorological sub-data and target photovoltaic data corresponding to the target meteorological sub-data; The at least one target photovoltaic data is input into the improved LSTM neural network, and the improved LSTM neural network is iteratively trained to obtain a photovoltaic power generation prediction model, wherein the improved LSTM neural network is obtained by optimizing the hyperparameters of the LSTM neural network according to the improved dung beetle optimization algorithm, and the expression of the dung beetle function mapping strategy in the improved dung beetle optimization algorithm is: ， ， ， ， ， In the formula, is the probability that a dung beetle is assigned as a rolling ball dung beetle, is the probability of a dung beetle being assigned as a breeding dung beetle, is the probability that a dung beetle is assigned as a foraging dung beetle, is the probability that a dung beetle is assigned as a thieving dung beetle, For the The fitness of a dung beetle at the tth iteration, is the minimum fitness of the current population, is the maximum fitness of the current population, is the population size, For the The fitness of a dung beetle at the tth iteration, is the average fitness of all dung beetles in the population at the tth iteration, The ratio of reproduction and foraging functions to the number of iterations; The acquired real-time meteorological sub-data is input into the photovoltaic power generation prediction model, and the photovoltaic power generation prediction model outputs a photovoltaic power generation power prediction result.

2. According to claim 1, a photovoltaic power prediction method based on an improved DBO-LSTM neural network is characterized in that: The data preprocessing of photovoltaic data includes: The meteorological sub-data and the photovoltaic power generation electronic data are cleaned and converted, wherein the data cleaning is specifically: processing the abnormal values ​​in the meteorological sub-data and the photovoltaic power generation electronic data according to a multi-dimensional self-adjusting convolution filter, and the expression is: ， ， In the formula, is the data after outlier processing. is the sliding process of the filter over the time period, is the original input data, is the time-integrated variable, is the filter, is the adaptive factor, is the time variable, is the degree of nonlinearity, is the attenuation rate of the filter, is the standard deviation of the Gaussian component, To adjust the factor of the sine wave amplitude, is the frequency of the sine wave; The data conversion is specifically as follows: normalizing the meteorological sub-data and the photovoltaic sub-data according to an adaptive dynamic nonlinear normalization strategy, wherein the expression of the adaptive dynamic nonlinear normalization strategy is: ， In the formula, For the The normalized value of the feature, For the The weight coefficient of the feature, The original data eigenvalues, For the The minimum value of the features, For the The maximum value of the features, Enter data for the eigenvalues; ， In the formula, For the The scaling factor of the features, For the The bias factor of each feature; ， In the formula, For the The smoothing coefficient of the feature; ， In the formula, is the output of the dynamic nonlinear characteristic regulator.

3. The photovoltaic power generation prediction method based on the improved DBO-LSTM neural network according to claim 1 is characterized in that: The performing correlation analysis on the preprocessed photovoltaic data and selecting at least one target photovoltaic data having a correlation coefficient greater than a preset threshold according to the analysis result comprises: The correlation coefficient between the meteorological sub-data and the photovoltaic power generation sub-data is calculated according to the preset nonlinear interaction term, and the expression is: ， ， In the formula, To consider the correlation coefficient of nonlinear interaction terms, is the correlation coefficient between the input feature and the output variable, is the number of nonlinear features, is the nonlinear term weight coefficient, For the Input feature values, For variables The average value of For the output variables, For variables The average value of is the standard deviation of the input features, is the standard deviation of the output features, is the frequency parameter of the characteristic, is the weight of the feature, For the Input feature values, For the output variables, is the sample size; At least one meteorological sub-data having a correlation coefficient of a nonlinear interaction term greater than a preset threshold and photovoltaic power generation electronic data corresponding to the at least one meteorological sub-data are selected.

4. The photovoltaic power generation prediction method based on the improved DBO-LSTM neural network according to claim 1 is characterized in that: Before inputting the at least one target photovoltaic data into the improved LSTM neural network and iteratively training the improved LSTM neural network to obtain a photovoltaic power generation prediction model, the method further includes: The hyperparameters of the LSTM neural network are optimized according to the improved dung beetle optimization algorithm to obtain an improved LSTM neural network, specifically: During the rolling process of the dung beetle, the first mathematical model for updating the position of the dung beetle is expressed as: ， ， In the formula, is the updated position of the dung beetle in the t+1th iteration, is the updated position of the dung beetle in the tth iteration, It is the degree of deviation from -1 or 1, 1 means no deviation from the original direction, -1 means deviation from the original direction. is the offset coefficient, is the updated position of the dung beetle in the t-1th iteration, is a constant, To simulate the change of sunlight intensity, is the worst position of the species in the world at present; When the individual cannot move further, the tangent function is used to determine the new rolling direction. The expression of the second mathematical model for updating the position of the dung beetle is: ， In the formula, is the deflection angle; Model the dung beetle egg-laying area, the expression is: ， ， In the formula, is the lower limit of the spawning area, is the current best position, , is the maximum number of iterations, is the current iteration number, is the lower bound of the optimization problem, is the upper limit of the spawning area, is the upper limit of the optimization problem; In each iteration cycle, the spawning position keeps changing, and the mathematical model of the spawning position is expressed as: ， In the formula, For the The position of the hatching ball at the t+1th iteration, For the The position of the hatching ball at the tth iteration, , are all random vectors; Adult dung beetles forage to determine the optimal foraging area boundary and update individual positions. The mathematical model of the optimal foraging area boundary is expressed as: ， ， In the formula, is the lower boundary of the optimal foraging area, is the upper boundary of the optimal foraging area, is the local optimal position of the previous population; The mathematical model for updating individual positions is expressed as: ， In the formula, is a random number that conforms to the normal distribution. is a random vector in the range (0,1); In the dung beetle algorithm, there is a behavior of stealing food from other dung beetles, which is called a stealing dung beetle. Therefore, assuming The surrounding area is the best location to compete for food. The mathematical model used to update the position of the dung beetle during the iteration process is expressed as: ， In the formula, is a randomly selected D-dimensional vector that conforms to the normal distribution, is a constant; The global solution is obtained by mapping the functions of dung beetles based on fitness; The improved dung beetle algorithm is used to iteratively update the hyperparameter combination of the LSTM neural network to maximize fitness and gradually find the optimal hyperparameter configuration. The expression of the optimal hyperparameter configuration is: ， In the formula, is the hyperparameter combination for the tth iteration, For the The number of iterations of the LSTM neural network for a dung beetle at the tth iteration, For the The number of neurons in the hidden layer of the LSTM neural network for each dung beetle individual at the tth iteration, For the The learning rate of the LSTM neural network for each dung beetle individual at the tth iteration, is the hyperparameter combination for the t+1th iteration, is the update amount, is the loss function value under the current hyperparameter combination, For the The true value of the samples, For the The predicted value of samples, is the number of samples in the validation set, The goal of fitness optimization.

5. The photovoltaic power generation prediction method based on the improved DBO-LSTM neural network according to claim 1 is characterized in that: The improved LSTM neural network includes: a high-order interactive gating mechanism for the interaction between the forget gate, the input gate, and the output gate, and a time-dependent attenuation layer normalization strategy; The expression of the high-order interactive gating mechanism is: ， ， ， ， In the formula, is the interaction function between the hidden gate and the input gate, is the interaction function between the hidden gate and the output gate, is the interaction function between the input gate and the input gate, is the learnable weight matrix, is the activation value of the forget gate at the tth time step, is the activation value of the input gate at the tth time step, is the activation value of the output gate at the tth time step, For current input The weight matrix of For the previous moment The weight matrix of the hidden state, is the input data of the current time step, is the hidden state of the previous time step, , are scalar parameters, is the dot product, To multiply; The expression of the time-dependent attenuation layer normalization strategy is: ， ， ， ， ， ， In the formula, is the memory cell state of the LSTM neural network at time step t, is the dynamic mean of the memory unit at time step t, is the dynamic standard deviation of the memory unit at time step t, is the time-integrated variable, is the fluctuation frequency, is the memory cell state of the LSTM neural network at time step i, is a nonlinear normalization function, is the attenuation transformation function, For time The state of the memory unit at the moment, is the normalized memory cell state, is the normalized equilibrium impact factor, is the decay rate, is the time-dependent normalization function.

6. A photovoltaic power generation prediction system based on an improved DBO-LSTM neural network, characterized in that: include: A processing module configured to obtain photovoltaic data and perform data preprocessing on the photovoltaic data, wherein the photovoltaic data includes meteorological sub-data and photovoltaic power generation data corresponding to the meteorological sub-data; A selection module is configured to perform a correlation analysis on the preprocessed photovoltaic data, and select at least one target photovoltaic data having a correlation coefficient greater than a preset threshold value according to the analysis result, wherein the target photovoltaic data includes target meteorological sub-data and target photovoltaic data corresponding to the target meteorological sub-data; The training module is configured to input the at least one target photovoltaic data into an improved LSTM neural network, iteratively train the improved LSTM neural network, and obtain a photovoltaic power generation prediction model, wherein the improved LSTM neural network is obtained by optimizing the hyperparameters of the LSTM neural network according to an improved dung beetle optimization algorithm, and the expression of the dung beetle function mapping strategy in the improved dung beetle optimization algorithm is: ， ， ， ， ， In the formula, is the probability that a dung beetle is assigned as a rolling ball dung beetle, is the probability of a dung beetle being assigned as a breeding dung beetle, is the probability that a dung beetle is assigned as a foraging dung beetle, is the probability that a dung beetle is assigned as a thieving dung beetle, For the The fitness of a dung beetle at the tth iteration, is the minimum fitness of the current population, is the maximum fitness of the current population, is the population size, For the The fitness of a dung beetle at the tth iteration, is the average fitness of all dung beetles in the population at the tth iteration, The ratio of reproduction and foraging functions to the number of iterations; The output module is configured to input the acquired real-time meteorological sub-data into the photovoltaic power generation prediction model, and the photovoltaic power generation prediction model outputs the photovoltaic power generation power prediction result.

7. 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 5.

8. 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 5 is implemented.

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