A Partially Observable Markov Decision Process Approach to Residential Home Energy Management

Abstract

Real-time pricing (RTP) is a utility-offered dynamic pricing program to incentivize customers to make changes in their energy usage. A home energy management system (HEMS) automates the energy usage in a smart home in response to utility pricing signals. We present three new HEMS techniques - one myopic approach and two non-myopic partially observable Markov decision process (POMDP) approaches - for minimizing the household electricity bill in such a RTP market. In a simulation study, we compare the performance of the new HEMS methods with a mathematical lower bound and the status quo. We show that the non-myopic POMDP approach can provide a 10%-30% saving over the status quo.