TY  - JOUR
Y1  - 2020///
A1  - Bou-Rabee, M.
A1  - Lodi, K.A.
A1  - Ali, M.
A1  - Ansari, M.F.
A1  - Tariq, M.
A1  - Sulaiman, S.A.
JF  - IEEE Access
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102811302&doi=10.1109%2fACCESS.2020.3028259&partnerID=40&md5=611115f9851a805313b87237d98ffec7
VL  - 8
N2  - 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 variablesâ?? 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.
ID  - scholars13608
KW  - Columns (structural); Electric power plants; Errors; Forecasting; Mean square error; Multilayer neural networks; Particle swarm optimization (PSO); Predictive analytics; Wind power
KW  -  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
KW  -  Wind
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 21693536
EP  - 198493
AV  - none
N1  - cited By 14
TI  - One-month-ahead wind speed forecasting using hybrid AI model for coastal locations
SP  - 198482
ER  -