Self-Learning Load Characteristic Models for Smart Appliances

Abstract

It is generally accepted that if dynamic electricity pricing tariffs were to be introduced, their effectiveness in controlling domestic loads will be curtailed if consumers were relied on to respond in their own interests. The complexities of relating behavior to load to price are so burdensome that at least some degree of automation would be required to take advantage of pricing signals. However, a major issue with home automation is fitting in with the lifestyles of individual consumers. Truly smart appliances that can learn the details of their routine operation may be several years away from widespread adoption making integrated home energy management systems unfeasible. Similarly, usage patterns of these same appliances may be substantially different from household to household. The contribution of this paper is the proposal and demonstration of a set of probabilistic models that act in a framework to reduce appliance usage data into contextual knowledge that accounts for variability in patterns in usage. Using sub-metered load data from various domestic wet appliances, the proposed technique is demonstrated learning the appliance operating likelihood surfaces from no prior knowledge.