Forecasting the Natural Gas Consumption in Residential and Commercial Sectors of Isfahan for Five Years Using Artificial Neural Networks

The largest share of natural gas consumption in the country is allocated to the residential and commercial sectors. Therefore, the prediction of the consumption rate of these two sectors is very important for planning in the National Iranian Gas Company. This paper develops an artificial neural network model to forecast the natural gas consumption for residential and commercial sectors in the city of Isfahan. In order to find an appropriate architecture, three different methods named dynamic method, radial basis function network method, and exhaustive prune method are investigated. The actual gas consumption data for the previous 10 years are used to predict consumption of the next five years. Factors of population, climate, total number of clients and gas prices are included in the prediction model. In this study, the neural network structures are compared with each other and with other traditional methods such as regression and time series methods. To evaluate the proposed model, we compare the results of three different architectures of neural network considering training times and accuracy of neural networks on the test data set. In addition, the neural networks have been compared with other well-known prediction methods such as auto-regressive integrated moving average and regression. The results indicate that the artificial neural network with exhaustive prune architecture is the most efficient and accurate model. The generated model is applied to predict residential and commercial gas consumption for five years. To the best of our knowledge, this method has not been used in the literature for predicting gas consumption in Esfahan.