forecasting effects of scenarios of subsides removal on residential electricity consumption by artificial neural networks

the increasing consumption of electricity in iran is one of the greatest concerns of the government. using the subsidy-based pricing system is one of the main reasons of improper pattern of residential electricity consumption that has imposed great cost over the government due to the increased number of consumers and their improper way of consuming electricity. in this paper, we analyze the factors that affect residential electricity demand using artificial neural network (ann) and predict the amount of electricity consumption in 2006 (the end of the year in which subsides are being removed) by definition of five different price scenarios. the per-capita residential electricity consumption is considered as a dependent variable of the model .electricity price, gdp per capita, macroeconomic fluctuations and a variable representing weather temperatures are used as explanatory factors. the proposed model has a good explaining capability (r=0.996) and with predicting independent variables up to 2016, the dependent variable were predicted using procedures like time series and arima. the achieved results show that the price factors have limited role in defining the pattern of residential electricity consumption. so small changes in electricity price will not reduce the electricity consumption and committing scenarios with gradual changes in price will not lead to the reduction of electricity consumption. therefore, it is necessary for the government to commit scenarios with significant increase of prices in order to correct the pattern of residential electricity consumption; otherwise, the electricity demand will increase uncontrollably due to the increasing population and consumption.