Edgenilm: Towards nilm on edge devices

Rithwik Kukunuri, Anup Aglawe, Jainish Chauhan, Kratika Bhagtani, Rohan Prashant Patil, Sumit Walia, Nipun Batra

Abstract:

Non-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.