Prediction of Long Term Residential Natural Gas Consumption Using ANN

Prediction of residential natural gas consumption in 20 next years was performed in this paper. Artificial neural network (ANN) was used to predict the natural gas in the Kerman, the biggest province in the Iran. The Kerman is included with ten important cities where gas consumption was estimated in each city. The minimum temperature in each year, population growth rate and number of each year were considered as three input variables while the gas consumption in residential section was as an output. The ANN was trained and tested using the data from 2000 to 2008 and applied for prediction in 20 next years from 2008-2028. The predicted result show that the total gas consumption in residential section will increases approximately 1.8 time in 20 next years. residential heating energy requirement and fuel consumption in Istanbul by a degree-hours method. Sarak and Satman [4] used a degree-day method to forecast heating natural gas consumption in a certain areas of Turkey. Siemek et al. [5] used logistic curve interpretation to estimate the natural gas consumption in Poland. Aras and Aras [6] used auto-regression approach to forecast the natural gas demand for residential section in Turkey. Gutierrez et al. [7] presented Gompertz-type innovation diffusion process as a stochastic growth model of natural gas consumption in Spain. They compared their results with those obtained by stochastic logistic innovation model and stochastic lognormal growth model. Fco et al. [8] proposed a demand forecasting approach for the practical operation of a gas system, based on a decomposition approach to produce up to 3 year-ahead daily forecasts of industrial end-use natural gas consumption. Forouzanfar et al. [2] compared the NLP and GA methods for yearly and seasonal consumption from 1995 to 2008 for residential as well as commercial sectors in Iran. Xu and Wang [1] used polynomial curve and moving average combination projection (PCMACP) model to estimate the future natural gas consumption in China from 2009 to 2015. Li and et al. [9] used a comprehensive approach based on Scenario analysis to forecast the growth of China’s natural gas consumption from 1997 to 2029. Potocnik et al. [10] predicted future consumption of natural gas in Slovenia in 2005 and 2006 by using a statistics based machine forecasting model. Sabo et al. [11] presented several possible methods for forecasting the hourly natural gas consumption on the basis of the past natural gas consumption data in the area of the city of Osijek (Croatia) from the beginning of the year 2008. It should be noted that efficient use of energy resources require accurate prediction of future energy demand. Numerous researchers have analyzed various energy issues and focused on developing appropriate energy demand models to reduce forecasting errors [2]. Journal of Applied Mechanical Engineering J o u r n al o f A pp lied ical Eninee r i n g