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Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence

Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence 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/approachThe Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.FindingsThe 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/valuePerformance 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Energy Sector Management Emerald Publishing

Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence

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References (52)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1750-6220
DOI
10.1108/ijesm-06-2018-0002
Publisher site
See Article on Publisher Site

Abstract

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/approachThe Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.FindingsThe 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/valuePerformance 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.

Journal

International Journal of Energy Sector Management – Emerald Publishing

Published: Sep 16, 2019

Keywords: Forecasting; Genetic algorithm; Particle swarm optimization; Artificial intelligence; ARIMA; Wind power

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