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Amir-Mohammad Golmohammadi

Amir-Mohammad Golmohammadi

Academic rank: Assistant Professor
ORCID:
Education: PhD.
ScopusId:
Faculty: Engineering
Address: Arak University
Phone:

Research

Title
Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence
Type
JournalPaper
Keywords
Forecasting, Genetic algorithm, Particle swarm optimization, Artificial intelligence, ARIMA, Wind power
Year
2018
Journal International Journal of Energy Sector Management
DOI
Researchers Ali Mostafaeipour ، Alireza Goli ، Samrad Jafarian ، Amir-Mohammad Golmohammadi

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.