Improving the residential natural gas consumption forecasting models by using solar radiation

• Various model structures are compared for natural gas consumption forecasting.
• Data sets include local distribution company data and individual house data.
• Linear (ARX) and nonlinear (neural networks, SVM) forecasting models are applied.
• Impact of solar radiation in improving the forecasting accuracy is investigated.
• Results confirm that solar radiation clearly improves the forecasting accuracy.

Natural gas is known as a clean energy source used for space heating in residential buildings. Residential sector is a major natural gas consumer that usually demands significant amount of total natural gas supplied in distribution systems. Since demands of all consumers should be satisfied and distribution systems have limited capacity, accurate planning and forecasting in high seasons has become critical and important. In this paper, the influence of solar radiation on forecasting residential natural gas consumption was investigated. Solar radiation impact was tested on two data sets, namely on natural gas consumption data of a model house, and on natural gas consumption data of a local distribution company. Various forecasting models with one day ahead forecasting horizon were compared in this study, including linear models (auto-regressive model with exogenous inputs, stepwise regression) and nonlinear models (neural networks, support vector regression). Results confirmed that solar radiation clearly influences natural gas consumption, and included as input variable in the forecasting model improves the forecasting results. Consequently it is recommended to use solar radiation as input variable in building forecasting models.