Short-term forecasting of Italian residential gas demand
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Natural gas is one of the most important energy sources in Italy: it fuels thermoelectric power plants, industrial facilities and domestic heating. Forecasting gas demand is a critical process for each energy provider, as it enables pipe reservation and stock planning. In this paper, we address the problem of short-term forecasting of residential gas demand by comparing several statistical learning models, including Ridge regression, Gaussian processes, and neural networks. We also present the preliminary steps of preprocessing and feature engineering. To the best of our knowledge, no benchmark is available for the task we performed, thus we derive a theoretical performance limit, based on the inaccuracy of meteorological forecasts. Our best model, a deep neural network, achieves an RMSE which is double with respect to the performance limit.
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