An Empirical Study on Energy Disaggregation via Deep Learning

DOI

10.2991/aiie-16.2016.77How to use a DOI?

Keywords

energy disaggregation; neural networks; deep learning;NILM

Abstract

Energy disaggregation is the task of estimating power consumption of each individual appliance from the whole-house electric signals. In this paper, we study this task based on deep learning methods which have achieved a lot of success in various domains recently. We introduce the feature extraction method that uses multiple parallel convolutional layers of varying filter sizes and present an LSTM (Long Short Term Memory) based recurrent network model as well as an auto-encoder model for energy disaggregation. Then we evaluate the proposed methods using the largest dataset available. And experimental results show the superiority of our feature extraction method and the LSTM based model.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wan He
AU  - Ying Chai
PY  - 2016/11
DA  - 2016/11
TI  - An Empirical Study on Energy Disaggregation via Deep Learning
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 338
EP  - 342
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.77
DO  - 10.2991/aiie-16.2016.77
ID  - He2016/11
ER  -