Disaggregated End-Use Energy Sensing for the Smart Grid

Most utility installed energy meters are primarily intended to support a utility's billing function. They report only the aggregate energy consumption of a home or business over intervals as long as a month. In contrast, disaggregated energy usage data broken down by individual devices or appliances offers a much richer dataset that has the potential to inform and empower a wide variety of energy stakeholders from homeowners and building operators to utilities and policy makers. In this paper, we survey existing and emerging disaggregation techniques and highlight signal features that could be used to sense disaggregated data in a viable and cost-effective manner. We provide a summary of our new approach to electrical load disaggregation using voltage noise including a brief overview of our sensing hardware, classification algorithms, and evaluation in 14 homes. We end the paper with a discussion of current open research problems that will need to be addressed before disaggregate...

• Type: Journal
• Year: 2011
• Where: PERVASIVE
• Authors: Jon Froehlich, Eric Larson, Sidhant Gupta, Gabe Cohn, Matthew S. Reynolds, Shwetak Patel
• Keywords: Applied Computing, Classification Algorithms, Energy Consumption, PERVASIVE 2011, Voltage Noise