Open Access

ARTICLE

Wind Power Prediction Based on Machine Learning and Deep Learning Models

Zahraa Tarek¹, Mahmoud Y. Shams^2,*, Ahmed M. Elshewey³, El-Sayed M. El-kenawy^4,5, Abdelhameed Ibrahim⁶, Abdelaziz A. Abdelhamid^7,8, Mohamed A. El-dosuky^1,9

1 Faculty of Computers and Information, Computer Science Department, Mansoura University, Mansoura, 35561, Egypt
2 Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, 33511, Egypt
3 Faculty of Computers and Information, Computer Science Department, Suez University, Suez, Egypt
4 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
5 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt
6 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt
7 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt
8 Department of Computer Science, College of Computing and Information Technology, Shaqra University, 11961, Saudi Arabia
9 Department of Computer Science, Arab East Colleges, Riyadh, 13544, Saudi Arabia

* Corresponding Author: Mahmoud Y. Shams. Email:

Computers, Materials & Continua 2023, 74(1), 715-732. https://doi.org/10.32604/cmc.2023.032533

Received 21 May 2022; Accepted 23 June 2022; Issue published 22 September 2022

Abstract

Wind power is one of the sustainable ways to generate renewable energy. In recent years, some countries have set renewables to meet future energy needs, with the primary goal of reducing emissions and promoting sustainable growth, primarily the use of wind and solar power. To achieve the prediction of wind power generation, several deep and machine learning models are constructed in this article as base models. These regression models are Deep neural network (DNN), k-nearest neighbor (KNN) regressor, long short-term memory (LSTM), averaging model, random forest (RF) regressor, bagging regressor, and gradient boosting (GB) regressor. In addition, data cleaning and data preprocessing were performed to the data. The dataset used in this study includes 4 features and 50530 instances. To accurately predict the wind power values, we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization (SFS-PSO) to optimize the parameters of LSTM network. Five evaluation criteria were utilized to estimate the efficiency of the regression models, namely, mean absolute error (MAE), Nash Sutcliffe Efficiency (NSE), mean square error (MSE), coefficient of determination (R²), root mean squared error (RMSE). The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R² equals 99.99% in predicting the wind power values.

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Keywords

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