Data driven modeling for energy consumption prediction in smart buildings

Energy efficiency is in the interest of everyone, from individuals to governments, since it yields economical savings, reduces greenhouse gas emissions and alleviates energy poverty. Buildings are one of the largest consumers of primary energy and attaining their efficiency is, therefore, an important goal. The Internet of Things currently provides vast amounts of data that can be used to extract knowledge of all kinds, including that regarding energy prediction. This has motivated us to test wether the prior information on the physics of building heat transfer, that is currently available is now redundant owing to the completeness of the data from the system. We propose a machine learning approach and a grey-box model approach with which to test this hypothesis. The former is blind to the physiscs of the problem, while the latter is greatly influenced by it. The energy consumption prediction models were created with both approaches and then used to estimate energy consumption in a normal operation state and compare it with energy consumption when an energy efficiency campaign is run. Our black-box method, which is based on a combination of statistical and machine learning models and on a time series structurization of the data, shows better prediction accuracy than the so-called grey-box methods that include basic physical equations. This shows that also a data driven approach outperforms more informed methods in this, like other fields.