Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22755
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dc.contributor.authorXu, FY-
dc.contributor.authorWang, X-
dc.contributor.authorLai, LL-
dc.contributor.authorLai, CS-
dc.date.accessioned2021-05-24T11:46:35Z-
dc.date.available2013-12-01-
dc.date.available2021-05-24T11:46:35Z-
dc.date.issued2013-
dc.identifier.citationProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, 2013, pp. 1312 - 1316en_US
dc.identifier.isbn9780769551548-
dc.identifier.issn1062-922X-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/22755-
dc.description.abstractIn this paper, both bottom-up and top-down models for demand response with agent-base approach and neural networks have been investigated. Simulations have been carried out with practical load data from the UK and Canada. Results show that each approach has its advantages and disadvantages depending on difference application scenarios. © 2013 IEEE.en_US
dc.format.extent1312 - 1316-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.  -
dc.subjectResidential customeren_US
dc.subjectdemand responseen_US
dc.subjectagenten_US
dc.subjectneural networken_US
dc.subjectdecision makingen_US
dc.titleAgent-based modeling and neural network for residential customer demand responseen_US
dc.typeConference Paperen_US
dc.identifier.doihttp://dx.doi.org/10.1109/SMC.2013.227-
dc.relation.isPartOfProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013-
pubs.publication-statusPublished-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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