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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 the context of today’s energy shortage, the utilization of renewable energy has become increasingly important. Wind power has received widespread attention as a clean and renewable energy form. However, due to the instability and uncontrollability of wind power generation, wind power prediction has become a popular research field. This article will introduce a wind power data prediction algorithm based on genetic algorithm optimized long short-term memory (GA-LSTM), and compare it before and after.
Introduction
Wind power forecasting refers to using mathematical models to predict wind power output in the future by analyzing historical wind speed and wind power data. Accurate wind power forecasts help grid operators better manage the output of wind power plants and improve grid stability and efficiency. Traditional wind power forecasting methods are mainly based on statistics and machine learning techniques, such as ARIMA, SVM, etc. However, these methods have certain limitations when dealing with nonlinear and non-stationary wind power data.
GA-LSTM algorithm steps
The GA-LSTM algorithm is a wind power data prediction method that combines genetic algorithms and long short-term memory networks (LSTM). Genetic algorithms are used to optimize the hyperparameters of the LSTM network to improve prediction accuracy. The following are the specific steps of the GA-LSTM algorithm:
Step 1: Data preprocessing First, the wind speed and wind power data need to be preprocessed. Common preprocessing methods include data smoothing, normalization, and feature extraction. The goal of preprocessing is to reduce the impact of noise and outliers and extract useful features.
Step 2: Genetic algorithm parameter optimization Use genetic algorithm to optimize the hyperparameters of the LSTM network. The genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanisms. It gradually optimizes the parameters of the LSTM network through operations such as selection, crossover, and mutation during the evolutionary process. The goal of optimization is to minimize the prediction error of the LSTM network on the training set.
Step 3: LSTM network training. After optimizing the genetic algorithm parameters, use the training set to train the LSTM network. LSTM is a type of recurrent neural network that can handle sequence data very well. By learning patterns from historical wind speed and wind power data, the LSTM network is able to predict wind power output over a period of time in the future.
Step 4: Model evaluation Use the test set to evaluate the trained GA-LSTM model. Evaluation indicators include root mean square error (RMSE), mean absolute error (MAE), etc. The prediction performance of the GA-LSTM algorithm can be evaluated by comparing with other traditional wind power prediction methods.
Before and after comparison
In order to verify the effectiveness of the GA-LSTM algorithm, this paper conducts comparative experiments with traditional ARIMA and SVM methods. Experimental results show that the GA-LSTM algorithm has better performance in wind power data prediction. Compared with ARIMA and SVM, the GA-LSTM algorithm achieves smaller errors on evaluation indicators such as RMSE and MAE.
in conclusion
This article introduces a wind power data prediction algorithm based on genetic algorithm optimized long short-term memory (GA-LSTM), and conducts a before-and-after comparison experiment. Experimental results show that the GA-LSTM algorithm has better performance in wind power data prediction. In the future, the GA-LSTM algorithm can be further studied and improved to improve its prediction accuracy and practicality.
Part of the code
%% Clear environment variables
warning off % Turn off alarm information
close all % Close the open figure window
clear % clear variables
clc % clears the command line
%% Import Data
res = xlsread('dataset.xlsx');
%% Divide training set and test set
temp = randperm(357);
P_train = res(temp(1: 240), 1: 12)';
T_train = res(temp(1: 240), 13)';
M = size(P_train, 2);
P_test = res(temp(241: end), 1: 12)';
T_test = res(temp(241: end), 13)';
N = size(P_test, 2);
%% Data normalization
[p_train, ps_input] = mapminmax(P_train, 0, 1);
p_test = mapminmax('apply', P_test, ps_input);
t_train = ind2vec(T_train);
t_test = ind2vec(T_test );
Run results
![[Load Forecast] Power system load forecasting algorithm based on GA-LSTM genetic optimization long short memory network ( Matlab code implementation)_path planning](http://i2.wp.com/img-blog.csdnimg.cn/img_convert/e94db616959cf4170eaa3372f578d90d.png)
![[Load Forecasting] Power system load forecasting algorithm based on GA-LSTM genetic optimization long short memory network ( Matlab code implementation)_genetic algorithm_02](http://i2.wp.com/img-blog.csdnimg.cn/img_convert/43072137d752031e1f868452b48d40d8.png)
References
[1] Li Yuntao. Short-term photovoltaic power prediction with LSTM model based on Bootstrap algorithm and whale optimization algorithm [J]. Information Technology and Informatization, 2023(5):188-191.
[2] Wang Wu, Zhang Yuanmin, Cai Ziliang. Short-term load forecasting of power system based on genetic optimization neural network [J]. Relay, 2008, 36(9):39-42.DOI:10.3969/j.issn.1674-3415.2008. 09.009.
[3] Wang Wu, Zhang Yuanmin, Cai Ziliang. Short-term load forecasting of power system based on genetic optimization neural network [J]. Power System Protection and Control, 2008, 036(009):39-42,47.