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.
Full text not available from this repository.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 |
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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 |
Depositing User: | Ms Sharifah Fahimah Saiyed Yeop |
Date Deposited: | 19 Aug 2021 07:23 |
Last Modified: | 19 Aug 2021 07:23 |
URI: | http://scholars.utp.edu.my/id/eprint/23379 |