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Sequence-to-point learning with neural networks for non-intrusive load monitoring

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationProceedings for Thirty-Second AAAI Conference on Artificial Intelligence
Place of PublicationNew Orleans, Louisiana, USA
PublisherAAAI Press
Pages2604-2611
Number of pages8
Publication statusPublished - 7 Feb 2018
EventThirty-Second AAAI Conference on Artificial Intelligence - Hilton New Orleans Riverside, New Orleans, United States
Duration: 2 Feb 20187 Feb 2018
https://aaai.org/Conferences/AAAI-18/
https://aaai.org/Conferences/AAAI-18/

Conference

ConferenceThirty-Second AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-18
CountryUnited States
CityNew Orleans
Period2/02/187/02/18
Internet address

Abstract

Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a single-channel blind source separation problem, aims to decompose the mains which records the whole house electricity consumption into appliance-wise readings. This problem is difficult because it is inherently unidentifiable. Recent approaches have shown that the identifiability problem could be reduced by introducing domain knowledge into the model. Deep neural networks have been shown to be a promising approach for these problems, but sliding windows are necessary to handle the long sequences which arise in signal processing problems, which raises issues about how to combine predictions from different sliding windows. In this paper, we propose sequence-to-point learning, where the input is a window of the mains and the output is a single point of the target appliance.We use convolutional neural networks to train the model. Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem. We applied the proposed neural network approaches to real-world household energy data, and show that the methods achieve state-of-the-art performance, improving two standard error measures by 84% and 92%.

Event

Thirty-Second AAAI Conference on Artificial Intelligence

2/02/187/02/18

New Orleans, United States

Event: Conference

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