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

Bou-Rabee, M. and Lodi, K.A. and Ali, M. and Ansari, M.F. and Tariq, M. and Sulaiman, S.A. (2020) One-month-ahead wind speed forecasting using hybrid AI model for coastal locations. IEEE Access, 8 . pp. 198482-198493.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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.

Item Type:Article
Impact Factor:cited By 0
Uncontrolled Keywords:Columns (structural); Electric power plants; Errors; Forecasting; Mean square error; Multilayer neural networks; Particle swarm optimization (PSO); Predictive analytics; Wind power, Electrical power generation; Hidden layer neurons; Mean absolute percentage error; Root mean square errors; Statistical indices; Wind energy capacity; Wind energy generation; Wind speed forecasting, Wind
ID Code:23379
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 07:23
Last Modified:19 Aug 2021 07:23

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