Entropy-Based Metrics for Occupancy Detection Using Energy Demand

Smart Meters provide detailed energy consumption data and rich contextual information that can be utilized to assist electricity providers and consumers in understanding and managing energy use. The detection of human activity in residential households is a valuable extension for applications, such as home automation, demand side management, or non-intrusive load monitoring, but it usually requires the installation of dedicated sensors. In this paper, we propose and evaluate two new metrics, namely the sliding window entropy and the interval entropy, inspired by Shannon's entropy in order to obtain information regarding human activity from smart meter readings. We emphasise on the application of the entropy and analyse the effect of input parameters, in order to lay the foundation for future work. We compare our method to other methods, including the Page-Hinkley test and geometric moving average, which have been used for occupancy detection on the same dataset by other authors. Our experimental results, using the power measurements of the publicly available ECO dataset, indicate that the accuracy and area under the curve of our method can keep up with other well-known statistical methods, stressing the practical relevance of our approach.