Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods

It is widely accepted that natural gas is a clean energy source that can be used to meet energy demand for heating and industrial purpose among the fossil fuels and its usage remarkably increases in order to maintain a clean environment in many countries in the world. It is fact that this makes energy investment planning in a country or region highly important for suitable economic development as well as environmental aspect. Therefore, energy demand for various sectors should be estimated in the frame of short-term energy policy. For accurate estimation of short-term energy demand a limited number of computational methods are employed by using the 4 yearly measured natural gas consumption values. Among these methods, the ANN and time series are widely used for short-term estimation of natural gas consumption in Turkey's certain regions. In this study, multilayer perceptron the ANNs with time series approach is proposed to forecast short-term natural gas consumption. Meteorological data (moisture, atmospheric pressure, wind speed and ambient temperature) obtained from the regional gas distribution company and the local meteorology office in last 4 years to construct well-tuned algorithm. Although the number of data was small, the proposed algorithm works well to forecast the short-term natural gas consumption and produces encouraging and meaningful outcomes for future energy investment policy.

► We modeled residential natural gas consumption regional basis various computational methods.
► We used both natural gas consumption and meteorological data including ambient temperature, etc.
► We developed a SARIMAX and two ANN models forecasting daily gas consumption in a city in Turkey.
► Our models can be easily used by province managers and decision makers for future planning.