Detecting Household Activity Patterns from Smart Meter Data

In an age where there is a strong dependency on electrical appliances for domestic routines, this paper proposes an algorithm for identifying domestic activities from non-intrusive smart meter aggregate data. We distinguish two types of activities: Type I activities are those that can be recognized using only smart meter data and Type II activities are recognized by combining smart meter data with basic environmental sensing (temperature and humidity). For both types of activities, we start by disaggregating the total power usage down to individual electrical appliances. Then, we build an indicative activity model to reason four domestic activities using the Dempster-Shafer theory of evidence. To validate our algorithms, we use real energy and environmental data collected in an actual UK household over a period of three months, benchmarked on a time-stamped log of activities. The results show that it is possible to detect four tested domestic daily activities with high accuracy based on the aggregate energy usage.