@inproceedings{477, author = {Nipun Batra and Hongning Wang and Amarjeet Singh and Kamin. Whitehouse}, title = {Matrix Factorisation for Scalable Energy Breakdown}, abstract = {Homes constitute a large fraction of the total energy consumption. Producing an energy breakdown for a home has been shown to reduce household energy consumption by up to 15%, among other benefits. However, existing approaches to produce an energy breakdown require hardware to be installed in each home and are thus prohibitively expensive. In this paper, we propose a novel application of feature-based matrix factorisation that does not require any additional hardware installation. The basic premise of our approach is that common design and construction patterns for homes create a repeating structure in their energy data. Thus, a sparse basis can be used to represent energy data from a broad range of homes. We evaluate our approach on 516 homes from a publicly available data set and find it to be better than five baseline approaches that either require sensing in each home, or a very rigorous survey across a large number of homes coupled with complex modelling. We also present a deployment of our system as a live web application that can potentially provide energy breakdown to millions of homes.}, year = {2017}, journal = {Proceedings of the ... AAAI Conference on Artificial Intelligence}, month = {02}, issn = {2159-5399}, url = {https://par.nsf.gov/biblio/10039453}, }