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Static and adaptive models in daily natural gas consumption forecasting

Performance of static and adaptive models for natural gas load forecasting has been investigated in this paper. The study is based on two sets of data, i.e. natural gas consumption data for an individual model house, and natural gas consumption data for a local distribution company. Several different forecasting models including linear models, neural network models, and support vector regression models, were constructed for the one day ahead forecasting of natural gas demand. All these models were examined in their static versions, and in adaptive versions. A cross- validation approach was applied in order to estimate the generalization performance of the examined forecasting models. Compared to the static model performance, the results confirmed the significantly improved forecasting performance of adaptive models in the case of the local distribution company, whereas, as was expected, the forecasts made in the case of the individual house were not improved by the adaptive models, due to its stationary heating regime. The results also revealed that nonlinear models do not outperform linear models in terms of generalization performance. In summary, if the relevant inputs are properly selected, adaptive linear models are recommended for use as basis in online application for daily natural gas consumption forecasting.

daily natural gas demand ; adaptive forecasting models ; linear forecasting models ; nonlinear forecasting models.

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