Open Access

ARTICLE

Optimization Ensemble Weights Model for Wind Forecasting System

by Amel Ali Alhussan¹, El-Sayed M. El-kenawy^2,3, Hussah Nasser AlEisa^1,*, M. El-SAID^4,5, Sayed A. Ward^6,7, Doaa Sami Khafaga¹

1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
2 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
3 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt
4 Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt
5 Delta Higher Institute of Engineering and Technology (DHIET), Mansoura, 35111, Egypt
6 Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, 11629, Egypt
7 Faculty of Engineering, Delta University for Science and Technology, Gamasa, Mansoura, 11152, Egypt

* Corresponding Author: Hussah Nasser AlEisa. Email:

Computers, Materials & Continua 2022, 73(2), 2619-2635. https://doi.org/10.32604/cmc.2022.030445

Received 26 March 2022; Accepted 26 April 2022; Issue published 16 June 2022

Abstract

Effective technology for wind direction forecasting can be realized using the recent advances in machine learning. Consequently, the stability and safety of power systems are expected to be significantly improved. However, the unstable and unpredictable qualities of the wind predict the wind direction a challenging problem. This paper proposes a practical forecasting approach based on the weighted ensemble of machine learning models. This weighted ensemble is optimized using a whale optimization algorithm guided by particle swarm optimization (PSO-Guided WOA). The proposed optimized weighted ensemble predicts the wind direction given a set of input features. The conducted experiments employed the wind power forecasting dataset, freely available on Kaggle and developed to predict the regular power generation at seven wind farms over forty-eight hours. The recorded results of the conducted experiments emphasize the effectiveness of the proposed ensemble in achieving accurate predictions of the wind direction. In addition, a comparison is established between the proposed optimized ensemble and other competing optimized ensembles to prove its superiority. Moreover, statistical analysis using one-way analysis of variance (ANOVA) and Wilcoxon’s rank-sum are provided based on the recorded results to confirm the excellent accuracy achieved by the proposed optimized weighted ensemble.

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Keywords

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