Comparison neural networks models for short term forecasting of natural gas consumption in Istanbul

The aim of this study is to find a suitable natural gas energy forecasting model for daily and weekly values of Istanbul by using artificial neural networks(ANN). As it is known, accurate forecasting is important for both gas distributors and consumers. On the view point of distributors, with accurate forecasting the number of false alarms would be significantly decreased and transship limits would be scheduled. On the view point of consumers, there will be no disconnect and breakdown etc. In this study, a wide factor analyzing is done in order to find the factors that effect the gas consumptions. Found results were applied to ANN feed forward back propagation algorithms. The reasons behind choosing ANN are the ability of forecasting future values of more than one variable at the same time and to model the nonlinear relation in the data structure. Performance comparisons of seven different algorithms were done.