Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence: A case study
International Journal of Energy Sector Management
ISSN: 1750-6220
Article publication date: 5 June 2019
Issue publication date: 16 September 2019
Abstract
Purpose
The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.
Design/methodology/approach
The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.
Findings
The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.
Originality/value
Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.
Keywords
Citation
Jafarian-Namin, S., Goli, A., Qolipour, M., Mostafaeipour, A. and Golmohammadi, A.-M. (2019), "Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence: A case study", International Journal of Energy Sector Management, Vol. 13 No. 4, pp. 1038-1062. https://doi.org/10.1108/IJESM-06-2018-0002
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited