File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Short-Term Residential Load Forecasting Based on Resident Behaviour Learning

TitleShort-Term Residential Load Forecasting Based on Resident Behaviour Learning
Authors
KeywordsDeep learning
Meter-level load forecasting
Recurrent neural network
short-term load forecasting
Issue Date2018
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59
Citation
IEEE Transactions on Power Systems, 2018, v. 33 n. 1, p. 1087-1088 How to Cite?
AbstractResidential load forecasting has been playing an increasingly important role in modern smart grids. Due to the variability of residents' activities, individual residential loads are usually too volatile to forecast accurately. A long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address such volatile problem. It is shown that the forecasting accuracy can be notably improved by including appliance measurements in the training data. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset.
Persistent Identifierhttp://hdl.handle.net/10722/263373
ISSN
2019 Impact Factor: 6.074
2015 SCImago Journal Rankings: 4.126
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorDong, ZY-
dc.contributor.authorKong, W-
dc.contributor.authorHill, DJ-
dc.contributor.authorLuo, F-
dc.contributor.authorXu, Y-
dc.date.accessioned2018-10-22T07:37:53Z-
dc.date.available2018-10-22T07:37:53Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Power Systems, 2018, v. 33 n. 1, p. 1087-1088-
dc.identifier.issn0885-8950-
dc.identifier.urihttp://hdl.handle.net/10722/263373-
dc.description.abstractResidential load forecasting has been playing an increasingly important role in modern smart grids. Due to the variability of residents' activities, individual residential loads are usually too volatile to forecast accurately. A long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address such volatile problem. It is shown that the forecasting accuracy can be notably improved by including appliance measurements in the training data. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59-
dc.relation.ispartofIEEE Transactions on Power Systems-
dc.rightsIEEE Transactions on Power Systems. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx 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.subjectDeep learning-
dc.subjectMeter-level load forecasting-
dc.subjectRecurrent neural network-
dc.subjectshort-term load forecasting-
dc.titleShort-Term Residential Load Forecasting Based on Resident Behaviour Learning-
dc.typeArticle-
dc.identifier.emailHill, DJ: dhill@eee.hku.hk-
dc.identifier.authorityHill, DJ=rp01669-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TPWRS.2017.2688178-
dc.identifier.hkuros293629-
dc.identifier.volume33-
dc.identifier.issue1-
dc.identifier.spage1087-
dc.identifier.epage1088-
dc.identifier.isiWOS:000418776400102-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats