One-month-ahead wind speed forecasting using hybrid AI model for coastal locations

• Main Authors: Bou-Rabee, M., Lodi, K.A., Ali, M., Ansari, M.F., Tariq, M., Sulaiman, S.A.
• Format: Article
• Published: Institute of Electrical and Electronics Engineers Inc. 2020
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102811302&doi=10.1109%2fACCESS.2020.3028259&partnerID=40&md5=611115f9851a805313b87237d98ffec7; http://eprints.utp.edu.my/23379/

Wind speed forecasts can boost the quality of wind energy generation by increasing the efficiency and enhancing the economic viability of this variable renewable resource. This work proposes a hybrid model for wind energy capacity for electrical power generation at coastal sites by utilizing wind-related variables� characteristics. The datasets of three coastal locations of Kuwait validate the proposed method. The hybrid model is a merger of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) and predicts one-month-ahead wind speed for wind power density calculation. The neural network starts its performance evaluation with a variable number of hidden-layer neurons to finally identify the optimal ANN topology. Comparisons of statistical indices with both expected and observed test results indicate that the ANN-PSO based hybrid model with the low root-mean-square-error and mean-square-error values outperforms ANN-based trivial models. The prediction model developed in this work is highly accurate with a Mean Absolute Percentage Error (MAPE) of approximately (3-6) for all the sites. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.