Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach

•Automatic system to detect energy efficiency anomalies in smart buildings.•Definition and testing of energy efficiency indicators to quantify energy savings.•Knowledge extraction from data and HVAC experts through Data Mining techniques.•In this study a full set of anomalous EE consumption patterns are detected.•During test period more than 10% of day presented a kind of EE anomaly.

The rapidly growing world energy use already has concerns over the exhaustion of energy resources and heavy environmental impacts. As a result of these concerns, a trend of green and smart cities has been increasing. To respond to this increasing trend of smart cities with buildings every time more complex, in this paper we have proposed a new method to solve energy inefficiencies detection problem in smart buildings. This solution is based on a rule-based system developed through data mining techniques and applying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is also proposed to detect anomalies. The data mining system is developed through the knowledge extracted by a full set of building sensors. So, the results of this process provide a set of rules that are used as a part of a decision support system for the optimisation of energy consumption and the detection of anomalies in smart buildings.