EdgeNILM
| Autor: | Rithwik Kukunuri, Jainish Chauhan, Anup Aglawe, Nipun Batra, Sumit Walia, Rohan Patil, Kratika Bhagtani |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Edge device Computer science 020209 energy Computation Aggregate (data warehouse) 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Footprint Task (computing) 0202 electrical engineering electronic engineering information engineering Data mining computer Edge computing Energy (signal processing) 0105 earth and related environmental sciences |
| Zdroj: | BuildSys@SenSys |
| DOI: | 10.1145/3408308.3427977 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|