Forecast of Iran's Electricity Consumption Using a Combined Approach of Neural Networks and Econometrics

Electricity cannot be stored and needs huge amount of capital so producers and consumers pay special attention to predict electricity consumption. besides, time series data of the electricity market are chaotic and complicated. nonlinear methods such as neural networks have shown better performance for predicting such kind of data. we also need to analyze other variables affecting electricity consumption so as to estimate their quantitative effects. this paper presents a new approach for forecasting: a combined method of neural networks (ann) and econometrics methods which can also explain the effect of raising the electricity prices on consumption after subsidies reform plan. data is from 1988-2008, and the method is compared with neural network and arima based on the rmse performance function that shows the advantage of the combinned approach. the provident prediction is done for 2009- 2014 and indicated that after decreasing subsidy, electricity consumption would increase slightly until 2014.