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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal processing Image processing Path planning Cellular automaton Drone
Content introduction
In recent years, photovoltaic power generation, as a clean and renewable energy form, has received widespread attention and application. The efficiency and stability of photovoltaic power generation are critical to the reliability and economy of energy systems. Therefore, accurate prediction of photovoltaic power generation is of great significance in energy planning and power dispatching. This article will introduce a photovoltaic power generation power prediction algorithm process based on Sparrow algorithm optimized variational mode decomposition (SSA-VMD) combined with long short-term memory network (LSTM).
First, let’s take a look at the Sparrow algorithm. The sparrow algorithm is an optimization algorithm based on swarm intelligence, which simulates the behavior of sparrows in the process of foraging. This algorithm simulates the foraging behavior of sparrows and continuously adjusts the search space to find the optimal solution. In photovoltaic power generation prediction, the Sparrow algorithm can be used to optimize the parameters of the model and improve the prediction accuracy.
Next, we introduce the concept of variational mode decomposition (VMD). VMD is a signal decomposition method that can decompose the original signal into multiple components with different frequencies and amplitudes. VMD decomposes the signal into a series of sub-signals with narrow bandwidth through iterative optimization. These sub-signals can better reflect the periodicity and trend of photovoltaic power generation.
Then, we introduce the long short-term memory network (LSTM). LSTM is a variant of Recurrent Neural Network (RNN) suitable for processing sequence data with long-term dependencies. In photovoltaic power generation prediction, LSTM can predict future photovoltaic power generation by learning patterns and trends in historical data.
In the algorithm proposed in this article, we combine SSA-VMD and LSTM to achieve accurate prediction of photovoltaic power generation. The algorithm flow is as follows:
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Collect historical data of photovoltaic power generation and perform preprocessing, including data cleaning, normalization, etc.
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Use SSA-VMD to decompose the raw data into multiple sub-signals.
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Use the decomposed sub-signals as input to train the LSTM model. LSTM models can learn patterns and trends of sub-signals.
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Use the trained LSTM model to predict future photovoltaic power generation.
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The parameters of the LSTM model are optimized using the Sparrow algorithm to improve prediction accuracy.
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Evaluate and verify prediction results, including calculating errors, drawing prediction curves, etc.
Through the above process, we can obtain a photovoltaic power generation power prediction algorithm based on the Sparrow algorithm optimized SSA-VMD-LSTM. This algorithm can make full use of the characteristics of photovoltaic power generation data to improve the accuracy and stability of prediction. In practical applications, this algorithm can provide an important reference for energy planning and power dispatching, helping to achieve reliable and efficient photovoltaic power generation systems.
To sum up, this article introduces a photovoltaic power generation power prediction algorithm process based on the Sparrow algorithm optimized SSA-VMD-LSTM. The algorithm combines signal decomposition and deep learning models to effectively predict photovoltaic power generation. In the future, we will further optimize the performance of the algorithm and verify and apply it in actual photovoltaic power generation systems. It is believed that this algorithm will bring new breakthroughs to the development and application of the photovoltaic power generation industry.
Part of the code
%% Clear environment variables</code><code>warning off % Close alarm information</code><code>close all % Close open figure window</code><code>clear % Clear variables</code><code>clc % clear command line</code><code>?</code><code>%% import data</code><code>res = xlsread('dataset.xlsx');</code><code>?</code><code>%% divide the training set and test set</code><code>temp = randperm(357);</code><code>?</code><code>P_train = res(temp(1: 240), 1: 12)';</code><code>T_train = res(temp(1: 240), 13)';</code><code>M = size(P_train, 2);</code><code>?</code><code>P_test = res(temp(241: end), 1: 12)';</code><code>T_test = res(temp(241: end), 13)';</code><code>N = size(P_test, 2);</code><code>?</code><code>%% data normalization化</code><code>[p_train, ps_input] = mapminmax(P_train, 0, 1);</code><code>p_test = mapminmax('apply', P_test, ps_input);</code><code>t_train = ind2vec(T_train);</code><code>t_test = ind2vec(T_test );
Operation results
References
[1] Cheng Rui, Li Sumin, Mao Jiaqi, et al. Research on VMD-SSA-LSTM mining area surface deformation prediction model based on time-series InSAR monitoring [J]. Chemical Minerals and Processing, 2023, 52(8):39-46.
[2] Yang Lingsheng, Li Wei. Research on photovoltaic power generation power prediction model based on multi-meteorological element dimensionality reduction and improved variational mode decomposition algorithm [J]. Renewable Energy, 2022(009):040.
[3] Zhang Zihua, Li Yan, Xu Tianqi, et al. Research on short-term wind power power of VMDCNNLSTM optimized by Sparrow algorithm [J]. Electrical Transmission, 2023, 53(5):77-83.
[4] Huang Dianling, Chi Xuebin, Xu Xu, et al. Photovoltaic power generation prediction based on long short-term memory network [J]. Scientific Research Information Technology and Application, 2019, 10(2):11.DOI:CNKI:SUN: KYXH.0.2019-02-004.