Designing Customized Energy Services Based on Disaggregation of Heating Usage

The deployment of smart meters has made available high-frequency (minutes as opposed to monthly) measurements of electricity usage at individual households. Converting these measurements to knowledge that can improve energy efficiency in the residential sector is critical to attract further smart grid investments and engage electricity consumers in the path towards reducing global carbon footprint. Currently, in the Unites States residential heating and cooling accounts for a third of the annual residential electricity consumption. The goal of the reported research is to use smart meter measurement data to cost effectively design consumer energy services such as energy audit and demand response targeted towards improving an individual household's heating usage efficiency. We present a machine learning approach akin to Non-Intrusive Load Monitoring (NILM) to disaggregate heating usage from measurements of a household's total electricity usage. We use as input 15-minute interval meter data and hourly outdoor temperature measurements. Our approach does not require a manual set-up procedure at each house. The method uses a Hidden Markov Model to capture the dependence of heating usage on outdoor temperature. Compared to existing methods based on linear regression, the proposed method provides details on heating usage patterns and is more flexible to incorporate other system specific information. Preliminary results based on synthetic and real-world usage data demonstrate the feasibility of the proposed approach.