Wavelet-Extreme Learning i-Machine for New Zealand Smart Meter Data

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dc.contributor.author Chopra, Abhinav Rakesh
dc.contributor.author Nair, Nirmal Kumar
dc.date.accessioned 2021-08-04T23:58:14Z
dc.date.available 2021-08-04T23:58:14Z
dc.date.issued 2021-4-2
dc.identifier.isbn 9781728186481
dc.identifier.uri https://hdl.handle.net/2292/55851
dc.description.abstract A wavelet decomposition based artificial neural network trained with an extreme learning machine is proposed for long-term load forecasting based on continuous learning. The proposed approach has been undertaken in pursuit of creating a software tool that can predict the impact of increased renewable distributed sources on load profiles. Several concerns and challenges arise as a result of this increase, as large transients are formed in these profiles causing a potential over-generation problem. As the number of smart meters and advanced metering infrastructure also increases, the demand for big data analytics systems becomes increasingly urgent. Pairing an intelligent hybrid neural network solution with an online cloud software makes the management of big data, in the form of smart meter load consumption data, more efficient and effective. The process of the proposed solution is that it first decomposes this data into components that represent more detailed periodic information within the time series. These components are then input into several extreme learning machine algorithms and the outputs are then recombined to make a load forecast and prediction of future load consumption data. The learning behaviour is vital and helps tuning with each new iteration increasing accuracy. A comparison of this paper's approach is benchmarked against the popular Levenberg-Marquardt training algorithm. The results obtained indicate the superiority of the wavelet-extreme learning machine hybrid compared to industry standard statistical methods and other hybrid neural network techniques.
dc.publisher IEEE
dc.relation.ispartof 2021 IEEE Power and Energy Conference at Illinois (PECI)
dc.relation.ispartofseries 2021 IEEE Power and Energy Conference at Illinois (PECI)
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
dc.title Wavelet-Extreme Learning i-Machine for New Zealand Smart Meter Data
dc.type Conference Item
dc.identifier.doi 10.1109/peci51586.2021.9435262
pubs.begin-page 1
pubs.volume 00
dc.date.updated 2021-07-05T10:13:51Z
dc.rights.holder Copyright: The author en
pubs.end-page 8
pubs.finish-date 2021-4-2
pubs.publication-status Published
pubs.start-date 2021-4-1
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.elements-id 857070

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