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

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.