%K 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
%A M. Bou-Rabee
%A K.A. Lodi
%A M. Ali
%A M.F. Ansari
%A M. Tariq
%A S.A. Sulaiman
%L scholars13608
%I Institute of Electrical and Electronics Engineers Inc.
%P 198482-198493
%X 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.
%J IEEE Access
%R 10.1109/ACCESS.2020.3028259
%D 2020
%V 8
%T One-month-ahead wind speed forecasting using hybrid AI model for coastal locations
%O cited By 14