Energy disaggregation via clustered regression models: A case study in the convenience store

Hsiao Hui Chen, Ping Feng Wang, Ching Tien Sung, Yi Ren Yeh, Yuh-Jye Lee

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

Global warming and the depletion of natural resources are two of the most difficult problems we have ever faced. To address this problem, people have begun paying more attention to carbon emission reduction and energy saving. For the residential electricity use, many studies have demonstrated that feedbacks, such as energy consumption of each appliance in the home, can help consumers reduce electricity consumption usage. In this article, we propose a novel framework for the disaggregation of energy consumption, which is looking forward to reaching reducing the number of smart meters installed and providing usage statistics as a feedback for consumers to decrease their energy cost. In our proposed framework, we have a chief meter which measures total energy consumption, and install smart meters at few key appliances. Based the energy consumption from these meters, we proposed a clustered regression models for energy disaggregation. More specifically, we first cluster appliances by the correlation between the using behavior of appliances, and select one of them as the key appliance in each cluster. By using the appliance with installed meter, we apply regression model to estimate the energy consumption for other appliances within each cluster. Our experimental results confirmed our proposed framework can achieve high accuracy for energy disaggregation while reducing the number of smart meters.

Original languageEnglish
Pages37-42
Number of pages6
DOIs
StatePublished - 1 Jan 2013
Event2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013 - Taipei, Taiwan
Duration: 6 Dec 20138 Dec 2013

Conference

Conference2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013
CountryTaiwan
CityTaipei
Period6/12/138/12/13

Keywords

  • clustering
  • energy disaggregation
  • support vector regression

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