Ensembling methods for countrywide short-term forecasting of gas demand

Gas demand is made of three components: residential, industrial, and thermoelectric gas demand. Herein, the one-day-ahead prediction of each component is studied, using Italian data as a case study. Statistical properties and relationships with temperature are discussed, as a preliminary step for an effective feature selection. Nine ‘base forecasters’ are implemented and compared: ridge regression, gaussian processes, nearest neighbours, artificial neural networks, torus model, LASSO, elastic net, random forest, and support vector regression (SVR). Based on them, four ensemble predictors are crafted: simple average, weighted average, subset average, and SVR aggregation. We found that ensemble predictors perform consistently better than base ones. Moreover, our models outperformed transmission system operator (TSO) predictions in a two-year out-of-sample validation. Such results suggest that combining predictors may lead to significant performance improvements in gas demand forecasting.