@inproceedings{405,
  author = {Yiling Jia and Nipun Batra and Hongning Wang and Kamin Whitehouse},
  title = {Active Collaborative Sensing for Energy Breakdown},
  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 fixed number of sensors installed, when compared to the state-of-the-art, which is also proven by our theoretical analysis.},
  year = {2019},
  journal = {Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
  chapter = {1943},
  pages = {10},
  month = {11},
  url = {https://par.nsf.gov/biblio/10211264},
  doi = {10.1145/3357384.3357929},
}