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Content introduction
In the past few years, with the rapid development of renewable energy, wind power generation has become an increasingly popular form of clean energy. However, due to the uncertainty and instability of wind power generation, accurate prediction of wind power power has become an important issue. To solve this problem, researchers have proposed various prediction models and algorithms.
In this article, we will introduce an algorithm process based on a convolutional neural network combined with a long and short memory network to achieve multi-input single-output regression prediction of wind power power. This algorithm combines a convolutional neural network (CNN) and a long short memory network (LSTM), which can effectively capture the spatiotemporal characteristics in time series data and improve the accuracy of prediction.
First, we need to collect relevant data about the wind farm. These data include multiple input variables such as wind speed, wind direction, temperature, etc., and the corresponding wind power power as the output variable. These data can be collected in real time through devices such as sensors, or obtained from historical records.
Next, we need to preprocess the data. This includes steps such as data cleaning, missing value processing, and data smoothing. We can also perform feature engineering to extract some features related to wind power power to further improve prediction performance.
Then, we divide the data set into training set and test set. Normally, we divide the data set in chronological order to ensure that the data in the test set will not be used during the training process.
Next, we build the CNN-LSTM model. Convolutional neural networks (CNN) are used to extract spatial features in input data, while long short memory networks (LSTM) are used to capture time series features in input data. The two networks are combined with each other to better handle the spatiotemporal relationship in wind power prediction.
During the model building process, we need to choose appropriate network structure and hyperparameters. This can be determined through methods such as cross-validation. We can also use regularization techniques such as L1 or L2 regularization to prevent overfitting.
Next, we train the model using the training set. During the training process, we use an error function (such as the mean square error) to measure the difference between the predicted value and the true value, and use the backpropagation algorithm to update the parameters of the model.
After training is complete, we evaluate the model using the test set. We can calculate various evaluation indicators, such as root mean square error (RMSE), mean absolute error (MAE), etc., to evaluate the performance of the model.
Finally, we can use the trained model to predict future wind power power. By inputting relevant variables, the model can output the corresponding wind power power value. In this way, we can use this algorithm to predict wind power power in practical applications.
In short, the wind power power prediction algorithm process based on convolutional neural network combined with long and short memory network can effectively capture spatiotemporal characteristics and improve the accuracy of prediction. Through the collection, preprocessing and model training of wind farm data, we can achieve accurate prediction of wind power power and provide strong support for the operation and management of wind power generation. This algorithm has broad application prospects in the field of renewable energy and deserves further research and exploration.
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] Zhang Zihua, Li Yan, Xu Tianqi, et al. Research on short-term wind power power prediction based on VMD-CNN-LSTM [J]. Journal of Yunnan University for Nationalities: Natural Science Edition, 2023.
[2] Li Zhuo, Ye Lin, Dai Binhua, et al. Ultra-short-term wind power power prediction method based on IDSCNN-AM-LSTM combined neural network [J]. High Voltage Technology, 2022(6):2117-2127.
[3] Yao Yue, Liu Da. Short-term wind power power prediction based on attention mechanism-based convolutional neural network-long short-term memory network [J]. Modern Electric Power, 2022(002):039.