To read this content please select one of the options below:

Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence: A case study

Samrad Jafarian-Namin (Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran)
Alireza Goli (Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran)
Mojtaba Qolipour (Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran)
Ali Mostafaeipour (Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran)
Amir-Mohammad Golmohammadi (Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran)

International Journal of Energy Sector Management

ISSN: 1750-6220

Article publication date: 5 June 2019

Issue publication date: 16 September 2019

314

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

Related articles