Performance of ARIMA and Neural Network GMDH approaches in prediction of natural gas demand in various sectors (Iran-1380-1389)

Natural gas because of its advantages plays a key role in economy of Iran. Given the continuously rising natural gas consumption, planning in the gas sector by taking into account predicted demand is critical for ensuring sustainable development. In this study we develop models for predicting natural gas demand in residential-commercial, industrial and power plant sectors in Iran using ARIMA (Autoregressive Integrated Moving Average) and Neural Network GMDH(Group Method of Data Handling) approaches. The time series data of natural gas demand, natural gas price and air temperature are used as the model variables. the RMSE, MSE and Prediction Error Percentage indexes are used for comparing this models. The prediction accuracy percentage index for residential-commercial, industrial and power plant sectors in ARIMA method are 93.8, 98.3 and 87 percent and those in Network GMDH method are 96.4, 99 and 98.2 percent respectively. The results indicate better performance and accuracy for the GMDH approach compared to the ARIMA model in predicting natural gas demand.