Energy Consumption Prediction Using Bands-Based Data Analytics

Kieran Greer, Y Bi

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

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Abstract

This paper describes a stochastic clustering method that is used for making predictions over energy data. The distinguishing feature of the method is discrete, localised optimisations based on similarity mea- surements, followed by a global aggregating layer, which can be compared with construction layers in deep neural networks. The developed model with the method is essentially a look-up table of the key energy bands that each appliance would use. Each band represents a level of consump- tion by the appliance. This table can replace disaggregation from more complicated methods, for instance constructed from probability theory. Experimental results show that the table can accurately disaggregate a single energy source to a set of appliances, because each appliance has quite a unique energy footprint. As part of predicting energy consump- tion, the model could possibly reduce costs by 50% and more than that if the proposed schedules are also included
Original languageEnglish
Title of host publicationKNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, KSEM 2022, PT III
EditorsGerard Memmi, Baijian Yang, Linghe Kong, Tianwei Zhang, Meikang Qiu
Pages631-643
Number of pages13
Volume13370
ISBN (Electronic)978-3-031-10989-8
DOIs
Publication statusPublished (in print/issue) - Aug 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13370 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Funding Information:
Acknowledgements. This work is supported by the project of “Novel Building Integration Designs for Increased Efficiencies in Advanced Climatically Tuneable Renewable Energy Systems (IDEAS)” (Grant ID: 815271), which is funded by the EU Horizon 2020 programme.

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Energy consumption prediction
  • Energy disaggregation
  • Stochastic clustering
  • Unsupervised machine learning

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