Forecasting natural gas consumption in İstanbul using neural networks and multivariate time series methods

Authors: ÖMER FAHRETTİN DEMİREL, SELİM ZAİM, AHMET ÇALIŞKAN, PINAR ÖZUYAR

Abstract: The fast changes and developments in the world's economy have substantially increased energy consumption. Consequently, energy planning has become more critical and important. Forecasting is one of the main tools utilized in energy planning. Recently developed computational techniques such as genetic algorithms have led to easily produced and accurate forecasts. In this paper, a natural gas consumption forecasting methodology is developed and implemented with state-of-the-art techniques. We show that our forecasts are quite close to real consumption values. Accurate forecasting of natural gas consumption is extremely critical as the majority of purchasing agreements made are based on predictions. As a result, if the forecasts are not done correctly, either unused natural gas amounts must be paid or there will be shortages of natural gas in the planning periods.

Keywords: Forecasting, neural networks, natural gas, time series

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