Energy Consumption Prediction Using Bands-Based Data Analytics

Kieran Greer, Y Bi

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

10 Downloads (Pure)

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 - 15th International Conference, KSEM 2022, Proceedings
EditorsGerard Memmi, Baijian Yang, Linghe Kong, Tianwei Zhang, Meikang Qiu
Pages631-643
Number of pages13
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.

Fingerprint

Dive into the research topics of 'Energy Consumption Prediction Using Bands-Based Data Analytics'. Together they form a unique fingerprint.

Cite this