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Issue No.01 - January-March (2011 vol.10)
pp: 28-39
Jon Froehlich , University of Washington
Eric Larson , University of Washington
Sidhant Gupta , University of Washington
Gabe Cohn , University of Washington
Matthew S. Reynolds , Duke University
Shwetak N. Patel , University of Washington
Most energy meters installed by utilities are intended primarily to support billing functions. Meters report only the aggregate energy consumption of a home or business over intervals as long as a month. In contrast, disaggregated energy usage data identified by individual devices or appliances offers a much more descriptive 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 article, the authors survey existing and emerging disaggregation techniques and highlight signal features that might be used to sense disaggregated data in a viable and cost-effective manner. They provide a summary of a new approach to electrical load disaggregation that uses voltage noise, including a brief overview of their sensing hardware, classification algorithms, and evaluation in 14 homes. The article concludes with a discussion of current open research problems that must be addressed before disaggregated energy sensing can be widely deployed.
Disaggregated energy sensing, Sustainability, Electricity, Water, Gas, Smart grid
Jon Froehlich, Eric Larson, Sidhant Gupta, Gabe Cohn, Matthew S. Reynolds, Shwetak N. Patel, "Disaggregated End-Use Energy Sensing for the Smart Grid", IEEE Pervasive Computing, vol.10, no. 1, pp. 28-39, January-March 2011, doi:10.1109/MPRV.2010.74
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