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.