Impact of Normalization on BiLSTM Based Models for Energy Disaggregation

Mohammed Ayub, El Sayed M. El-Alfy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Non-Intrusive Load Monitoring is the decomposition of each appliance energy from the total energy recorded at a central smart meter. The success of machine learning based disaggregation models immensely depends on the quality of the input data. This paper evaluates different normalization techniques and their impact on the performance of a Bi-directional Long Short-Term Memory (BiLSTM) model for energy disaggregation. The group of appliances consists of a non-linear device (television), three multi-sate devices (washing machine, rice cooker and microwave) and two continuously operating devices (refrigerator and Kimchi refrigerator). The comparison was conducted on two publicly available datasets and the results showed that applying normalization to the input samples rather than feature values can improve the performance significantly. The best results have been obtained for normalization with L2-norm.

Original languageEnglish
Title of host publication2020 International Conference on Data Analytics for Business and Industry
Subtitle of host publicationWay Towards a Sustainable Economy, ICDABI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728196756
DOIs
StatePublished - 26 Oct 2020

Publication series

Name2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Energy disaggregation
  • data preprocessing
  • deep learning
  • load monitoring
  • machine learning
  • multi-target disaggregation
  • simultaneous disaggregation

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Computer Networks and Communications
  • Information Systems and Management
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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