eprintid: 13608
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/36/08
datestamp: 2023-11-10 03:28:10
lastmod: 2023-11-10 03:28:10
status_changed: 2023-11-10 01:51:35
type: article
metadata_visibility: show
creators_name: Bou-Rabee, M.
creators_name: Lodi, K.A.
creators_name: Ali, M.
creators_name: Ansari, M.F.
creators_name: Tariq, M.
creators_name: Sulaiman, S.A.
title: One-month-ahead wind speed forecasting using hybrid AI model for coastal locations
ispublished: pub
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
note: cited By 14
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.
date: 2020
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102811302&doi=10.1109%2fACCESS.2020.3028259&partnerID=40&md5=611115f9851a805313b87237d98ffec7
id_number: 10.1109/ACCESS.2020.3028259
full_text_status: none
publication: IEEE Access
volume: 8
pagerange: 198482-198493
refereed: TRUE
issn: 21693536
citation:   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.  ISSN 21693536