On forecasting non-renewable energy production with uncertainty quantification: A case study of the Italian energy market

Nowadays the introduction of energy marketplaces in several countries pushed the development of machine learning approaches for devising effective predictions about both energy needs and energy productions. In this paper we address the problem of predicting the amount of electrical power produced using non-renewable sources, as getting an estimate of the amount of electrical power produced using the various kinds of non-renewable sources yields a big competitive advantage for energy market investors. Specifically, we devise a forecasting technique obtained by trying and combining various machine learning techniques which is able to provide energy production estimates with a remarkably low error. Finally, since the input data available for predictions are in general not sufficient to determine the amounts of produced energy for the various source types, we provide an estimate of the impact of unknown latent variable on the amounts of produced energy, by devising a prediction model which is capable of estimating the prediction error for the specific data at hand. These informations can be exploited by investors to get an idea of the risk levels of their investments.