@article{scholars13608,
            note = {cited By 14},
          volume = {8},
             doi = {10.1109/ACCESS.2020.3028259},
           title = {One-month-ahead wind speed forecasting using hybrid AI model for coastal locations},
            year = {2020},
         journal = {IEEE Access},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
           pages = {198482--198493},
          author = {Bou-Rabee, M. and Lodi, K. A. and Ali, M. and Ansari, M. F. and Tariq, M. and Sulaiman, S. A.},
            issn = {21693536},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102811302&doi=10.1109\%2fACCESS.2020.3028259&partnerID=40&md5=611115f9851a805313b87237d98ffec7},
        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},
        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{\^a}?? 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. {\^A}{\copyright} 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.}
}