Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings

Abstract The energy management of buildings currently offers a powerful opportunity to enhance energy efficiency and reduce the mismatch between the actual and expected energy demand, which is often due to an anomalous operation of the equipment and control systems. In this context, the characterisation of energy consumption patterns over time is of fundamental importance. This paper proposes a novel methodology for the characterisation of energy time series in buildings and the identification of infrequent and unexpected energy patterns. The process is based on an enhanced Symbolic Aggregate approXimation (SAX) process, and it includes an optimised tuning of the time window width and of the symbol intervals according to the building energy behaviour. The methodology has been tested on the whole electrical load of buildings for two case studies, and its flexibility and robustness have been confirmed. In order to demonstrate the implications for a preliminary diagnosis, some unexpected trends of the total electrical load have also been discussed in a post-mining phase, using additional datasets related to heating and cooling electrical energy needs. The process can be used to support stakeholders in characterising building behaviour, to define appropriate energy management strategies, and to send timely alerts based on anomaly detection outcomes.