Open AccessUnderstanding the performance gap: a machine learning approach on residential buildings in Turin, Italy
Author(s) -
Roberto Boghetti,
Fabio Fantozzi,
Jérôme Henri Kämpf,
Giacomo Salvadori
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1343/1/012042
Subject(s) - energy consumption , greenhouse gas , architectural engineering , energy (signal processing) , estimation , space (punctuation) , energy demand , computer science , consumption (sociology) , greenhouse , energy performance , environmental science , machine learning , environmental economics , engineering , statistics , mathematics , ecology , social science , systems engineering , sociology , horticulture , electrical engineering , economics , biology , operating system
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.