Kukunuri, RithwikRithwikKukunuriAglawe, AnupAnupAglaweChauhan, JainishJainishChauhanBhagtani, KratikaKratikaBhagtaniPatil, RohanRohanPatilWalia, SumitSumitWaliaBatra, NipunNipunBatra2025-08-312025-08-312020-11-18[9781450380614]10.1145/3408308.34279772-s2.0-85097206232https://d8.irins.org/handle/IITG2025/23900Non-intrusive load monitoring (NILM) or energy disaggregation refers to the task of estimating the appliance power consumption given the aggregate power consumption readings. Recent state-of-the-art neural networks based methods are computation and memory intensive, and thus not suitable to run on "edge devices". Recent research has proposed various methods to compress neural networks without significantly impacting accuracy. In this work, we study different neural network compression schemes and their efficacy on the state-of-the-art neural network NILM method. We additionally propose a multi-task learning-based architecture to compress models further. We perform an extensive evaluation of these techniques on two publicly available datasets and find that we can reduce the memory and compute footprint by a factor of up to 100 without significantly impacting predictive performance.falseEdge computing | Neural networks | Non-Intrusive Load MonitoringConference Paper90-9918 November 202042cpConference Proceeding40