Day-ahead natural gas forecasting using nonseasonal exponential smoothing methods

Natural gas is one of the most commonly used energy sources. In real life, natural gas consumption values and the amount of natural gas extracted are required to be equal. Thus, problems with respect to supply and demand are reduced. Problems in the supply side arise from the fact that the demand can not be determined correctly. Therefore, the imbalance in the system should be reduced by correctly determining the demand. In this study, day ahead demand forecast for the natural gas sector is examined. In the day ahead approach, demand estimations are performed using over four years of daily data and applying simple, double, linear, damped trend exponential smoothing methods at different data sizes. The effect of using different sizes of dataset on the demand estimation is tried to be identified. While the results showed that the simple exponential smoothing method gave the best result, the estimations made with the 6-week and extended datasets forecasted more accurate results. In addition, it is observed that the increase in the number of data in the day ahead demand forecast, allows prediction where exponential smoothing methods are used, with a lower error. In this research, the lowest mean absolute percent error (MAPE) for four years is determined as 14.1%, while the coefficient of determination (R
²
) is 0.917 with the SES method.