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Household electricity load disaggregation based on low-resolution smart meter data

Author(s):Hiroshi Chin, Kenji Tanaka, Abe Rikiya
Department of Technology Management for Innovation, Graduate School of Engineering, The University of Tokyo, 214-3,7-3-1 Hongo, Bunkyo, Tokyo, Japan, 113-8656
International Journal of Smart Grid and Clean Energy, vol. 5, no. 3, July 2016: pp. 188-195
ISSN: 2315-4462 (Print)
ISSN: 2373-3594 (Online)
Digital Object Identifier: 10.12720/sgce.5.3.188-195

Abstract:The Japanese government is proceeding to have the electricity companies to install smart meters in residential sectors. Although power companies are installing smart meters in residential areas, electricity data analysis methods for smart meters are not developed in Japan enough. This research shows an analysis method of electricity disaggregation on low-resolution smart meter data to reveal the point of energy saving for the residents. Specifically, we provided that three kinds of disaggregation methods for the disaggregation of Low-resolution data. 1) Two-states-disaggregation, which can separate the active and the inactive state. 2) Three-states-disaggregation, which can separate the load for active, mid-active and inactive states, which can reveal the life pattern specifically. 3) Temperature-sensitive load disaggregation that can separate the total load and temperature-sensitive load. Finally, we also demonstrated an example of how to take advantage of the disaggregation that helps the users to analyse and enhance energy saving.
Keywords:Electricity load disaggregation, smart meter data, hidden markov model, energy saving, temperature-sensitive load disaggregation

Full Paper.pdf

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