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

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