Multi-Target Energy Disaggregation using Convolutional Neural Networks

Mohammed Ayub, El Sayed M. El-Alfy

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Non-Intrusive Load Monitoring (NILM) has become popular for smart meters in recent years due to its low cost installation and maintenance. However, it requires efficient and robust machine learning models to disaggregate the respective electrical appliance energy from the mains. This study investigated NILM in the form of direct point-to-point multiple and single target regression models using convolutional neural networks. Two model architectures have been utilized and compared using five different metrics on two benchmarking datasets (ENERTALK and REDD). The experimental results showed that multi-target disaggregation setting is more complex than single-target disaggregation. For multi-target setting of ENERTALK dataset, the highest individual F1-score is 95.37% and the overall average F1-score is 75.00%. Better results were obtained for the multi-target setting of the other dataset with higher overall average F1-score of 83.32%. Additionally, the robustness and knowledge transfer capability of the models through cross-appliance and cross-domain disaggregation was demonstrated by training for a specific appliance on a specific data, and testing for a different appliance, house and dataset. The proposed models can also disaggregate simultaneous operating appliances with higher F1-scores.

Original languageEnglish
Pages (from-to)684-693
Number of pages10
JournalInternational Journal of Advanced Computer Science and Applications
Volume11
Issue number10
DOIs
StatePublished - 1 Oct 2020

Bibliographical note

Publisher Copyright:
© 2020 Science and Information Organization. All rights reserved.

Keywords

  • ENERTALK dataset
  • Energy disaggregation
  • Load monitoring
  • Multi-target disaggregation
  • Multi-target regression
  • NILM knowledge transfer
  • Smart meters

ASJC Scopus subject areas

  • General Computer Science

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