Temporal association rules for electrical activity detection in residential homes

Attaining energy efficiency requires understanding human behaviors triggering energy consumption within households. In conjunction to providing appliance-level feedback, targeting human activities that involve the usage of electrical appliances can provide a higher abstraction level to bring awareness to the electricity wastage. In this paper, we make use of a large dataset with appliance- and circuit-level power data and provide a framework for determining temporal sequential association rules. Sequences of time intervals where the appliances are in usage can vary in their order, duration and the time elapsed between these events. Our contribution consists in providing a full pipeline for mining frequent sequential itemsets and a novel way to discover the time windows during which these sequences of events occur and to capture their variance in terms of duration and order. Our method is data-driven and relies on the data's statistical properties and allows us to avoid an exhaustive search for the time windows' sizes, by relying instead on machine learning techniques to identify and predict those time windows.