Mayo, M., & Omranian, S. (2016). Towards a new evolutionary subsampling technique for heuristic optimisation of load disaggregators. In H. Cao, J. Li, & R. Wang (Eds.), Trends and Applications in Knowledge Discovery and Data Mining, PAKDD 2016 Workshops, Revised Selected Papers (Vol. LNCS 9794, pp. 3–14). Conference held at Auckland, NZ: Springer. http://doi.org/10.1007/978-3-319-42996-0_1
Permanent Research Commons link: https://hdl.handle.net/10289/10560
In this paper we present some preliminary work towards the development of a new evolutionary subsampling technique for solving the non-intrusive load monitoring (NILM) problem. The NILM problem concerns using predictive algorithms to analyse whole-house energy usage measurements, so that individual appliance energy usages can be disaggregated. The motivation is to educate home owners about their energy usage. However, by their very nature, the datasets used in this research are massively imbalanced in their target value distributions. Consequently standard machine learning techniques, which often rely on optimising for root mean squared error (RMSE), typically fail. We therefore propose the target-weighted RMSE (TW-RMSE) metric as an alternative fitness function for optimising load disaggregators, and show in a simple initial study in which random search is utilised that TW-RMSE is a metric that can be optimised, and therefore has the potential to be included in a larger evolutionary subsampling-based solution to this problem.
This is an author’s accepted version of an article published in Trends and Applications in Knowledge Discovery and Data Mining, PAKDD 2016 Workshops, LNAI 9794. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-42996-0_1. © Springer International Publishing Switzerland 2016