Active collaborative sensing for energy breakdown

Show simple item record

dc.contributor.author Jia,Yiling
dc.contributor.author Batra,Nipun
dc.contributor.author Wang,Hongning
dc.date.accessioned 2019-09-12T10:10:09Z
dc.date.available 2019-09-12T10:10:09Z
dc.date.issued 2019-09
dc.identifier.citation Jia,Yiling; Batra,Nipun and Wang,Hongning , �Active collaborative sensing for energy breakdown�, arXiv, Cornell University Library, DOI: arXiv: 1909.00525, Sep. 2019. en_US
dc.identifier.uri https://arxiv.org/abs/1909.00525
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/4823
dc.description.abstract Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits. As a result, strategically deploying sensing hardware to maximize the reconstruction accuracy of sub-metered readings in non-instrumented homes while minimizing deployment costs becomes necessary and promising. In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown. We propose to actively deploy energy sensors to appliances from selected homes, with a goal to improve the prediction accuracy of the completed tensor with minimum sensor deployment cost. We empirically evaluate our approach on the largest public energy dataset collected in Austin, Texas, USA, from 2013 to 2017. The results show that our approach gives better performance with a fixed number of sensors installed when compared to the state-of-the-art, which is also proven by our theoretical analysis.
dc.description.statementofresponsibility by Yiling Jia, Nipun Batra, Hongning Wang and Kamin Whitehouse
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.subject Machine Learning en_US
dc.title Active collaborative sensing for energy breakdown en_US
dc.type Preprint en_US


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search Digital Repository


Browse

My Account