Title: Active Collaborative Sensing for Household Energy Breakdown
Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing an appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches to produce an 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-meter readings in uninstrumented homes while minimizing deployment costs becomes necessary and promising. In this work, we develop a 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 completed tensor's prediction accuracy with minimum sensor deployment. We empirically evaluate our approach on the largest public energy dataset collected in Austin, 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 shown by our theoretical analysis.
Hongning Wang (Advisor), Jack Stankovic (Chair), Madhur Behl, Michael Albert