R imor

Abstract

Buildings across the world contribute about one-third of the total energy consumption. Studies report that anomalies in energy consumption caused by faults and abnormal appliance usage waste up to 20% of energy in buildings. Recent works leverage smart meter data to find such anomalies; however, such works do not identify the appliance causing the anomaly. Moreover, most of these works are not real-time and report the anomaly at the end of the day. In this paper, we propose a technique named Rimor that addresses these limitations. Rimor predicts the energy consumption of a home using historical energy data and contextual information and flags an anomaly when the actual energy consumption deviates significantly from the predicted consumption. Further, it identifies anomalous appliance(s) by using easy-to-collect appliance power ratings. We evaluated it on four real-world energy datasets containing 51 homes and found it to be 15% more accurate in detecting anomalies as compared to four other baseline approaches. Rimor reports an appliance identification accuracy of 82%. In addition, we also release an anomaly annotated energy dataset for the research community.