Short-term forecasting of Italian residential gas demand

Natural gas is the most important energy source in Italy: it fuels thermoelectric power plants, industrial facilities and domestic heating. Gas demand forecasting is a critical task for any energy provider as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of Italian daily residential gas demand is studied. Five predictors are developed and compared: Ridge Regression, Gaussian Process, k-Nearest Neighbour, Artificial Neural Network, and Torus Model. Preprocessing and feature selection are also discussed in detail. Concerning the prediction error, a theoretical bound on the best achievable root mean square error is worked out assuming ideal conditions, except for the inaccuracy of meteorological temperature forecasts, whose effects are properly propagated. The best predictors, namely the Artificial Neural Network and the Gaussian Process, achieve an RMSE which is twice the performance limit, suggesting that precise predictions of residential gas demand can be achieved at country level.