Iranian Journal of Economic Research

COMPARATIVE STUDY OF ARIMA AND ARTIFICIAL NEURAL NETWORK METHODS FOR IRAN ELECTRICITY FORECASTING

Authors

Ali Mohamad Ahmadi 1
Mahdi Zolfaghari 2
Aidin Ghafar Nejad Mehrabani 3

1 Assistant Professor Tarbiat Modares University

2 Master student of Energy Economics, University of Water and Power Industry

3 Master student of Energy Mechanics, University of Water and Power Industry

Abstract
Electricity demand is growing very fast in Iran and it is important to forecast its future demand and its monthly variation accurately.
Artificial Neural Network (ANN) is a powerful tool for nonlinear models for forecasting and it was used to estimate monthly electricity demand in this study. In this paper, we compared the Non-linear ANN model with ARIMA linear model to estimate monthly electricity demand for a priod of 3 years. Using MSE, RMSE, NMSE, MHE, MAPE and R2 indicatorss, our results show that ANN forecasting model is superior to ARIMA in terms of less error coefficient and high explanatory ability.



 



 

Keywords

Neural Network
ARIMA
FORECAST
ELECTRICITY DOMESTIC REQUIREMENT

  • Receive Date 01 May 2016
  • First Publish Date 01 May 2016

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