Non-Intrusive Appliance Load Monitoring using Genetic Algorithms
dc.contributor.author | Hock, D | |
dc.contributor.author | Kappes, M | |
dc.contributor.author | Ghita, B | |
dc.date.accessioned | 2019-03-04T11:38:44Z | |
dc.date.available | 2019-03-04T11:38:44Z | |
dc.date.issued | 2018-06 | |
dc.identifier.issn | 1757-8981 | |
dc.identifier.issn | 1757-899X | |
dc.identifier.uri | http://hdl.handle.net/10026.1/13396 | |
dc.description.abstract |
Smart Meters provide detailed energy consumption data and rich contextual information which can be utilized to assist energy providers and consumers in understanding and managing energy use. Here, we present a novel approach using genetic algorithms to infer appliance level data from aggregate load curves without a-priori information. We introduce a theoretical framework to encode load data in a chromosomal representation, to reconstruct individual appliance loads and propose several fitness functions for the evaluation. Our results, using artificial and real world data, confirm the practical relevance and feasibility of our approach. | |
dc.format.extent | 012003-012003 | |
dc.language.iso | en | |
dc.publisher | IOP Publishing | |
dc.title | Non-Intrusive Appliance Load Monitoring using Genetic Algorithms | |
dc.type | conference | |
dc.type | Conference Proceeding | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000446123600003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 1 | |
plymouth.volume | 366 | |
plymouth.publication-status | Published | |
plymouth.journal | IOP Conference Series: Materials Science and Engineering | |
dc.identifier.doi | 10.1088/1757-899x/366/1/012003 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dc.identifier.eissn | 1757-899X | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1088/1757-899x/366/1/012003 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Conference Paper/Proceeding/Abstract |