Buildings account for the highest share of primary energy usage and greenhouse gas emission in the E.U. and U.S. [1], and most of this energy is used for space and water heating. Being able to gain a broader understanding of the gap between predicted and in situ measured thermal performance of buildings may, in a lot of cases, help reducing the energy consumption and, therefore, alleviating our pressure on the environment [2]. The aim of this research is to further investigate this performance gap and to evaluate the possibility of using machine learning algorithms to effectively predict the energy demand of buildings. For this purpose, a group of residential buildings in the city of Turin, Italy, is taken as case study: an estimation of their yearly heating demand is made using different machine learning algorithms, and their results are evaluated and discussed. The research showed that the use of machine learning resulted in a performance gap in line, if not lower, with the current literature. The reasons for this outcome, as well as possible future research directions are finally discussed.