NILMTK-Contrib: Towards reproducible state-of-the-art energy disaggregation


Kukunuri R, Batra N, Pandey A, Malakar R, Kumar R, Krystalakos O, Mingjun Zhong. NILMTK-Contrib: Towards reproducible state-of-the-art energy disaggregation, in AI for Social Good Workshop. ; 2020.


Research shows that providing an appliance-wise energy breakdown can help users save up to 15% of their energy bills. Non-intrusive load monitoring (NILM) or energy disaggregation is the task of estimating the household energy measured at the aggregate level for each constituent appliances in the household. The problem was first was introduced in the 1980s by Hart. Over the past three decades, NILM has been an extensively researched topic by researchers. NILMTK was introduced in 2014 to the NILM community in order to motivate reproducible research. Even after the introduction of the NILMTK toolkit to the community, there has been a little contribution of recent state-of-the-art algorithms back to the toolkit. In this paper, we propose a new disaggregation API, which further simplifies the process for the rapid comparison of different state-of-the-art algorithms across a wide range of datasets and algorithms. We also propose a new rewrite for writing the new disaggregation algorithms for NILMTK, which is similar to Scikitlearn. We demonstrate the power of the new API by conducting various complex experiments using the API.

Back to AI for Social Good event

Last updated on 07/01/2021