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- Publisher Website: 10.1109/TPWRS.2017.2688178
- Scopus: eid_2-s2.0-85046692362
- WOS: WOS:000418776400102
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Article: Short-Term Residential Load Forecasting Based on Resident Behaviour Learning
Title | Short-Term Residential Load Forecasting Based on Resident Behaviour Learning |
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Authors | |
Keywords | Deep learning Meter-level load forecasting Recurrent neural network short-term load forecasting |
Issue Date | 2018 |
Publisher | Institute 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? |
Abstract | Residential 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 Identifier | http://hdl.handle.net/10722/263373 |
ISSN | 2023 Impact Factor: 6.5 2023 SCImago Journal Rankings: 3.827 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Dong, ZY | - |
dc.contributor.author | Kong, W | - |
dc.contributor.author | Hill, DJ | - |
dc.contributor.author | Luo, F | - |
dc.contributor.author | Xu, Y | - |
dc.date.accessioned | 2018-10-22T07:37:53Z | - |
dc.date.available | 2018-10-22T07:37:53Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE Transactions on Power Systems, 2018, v. 33 n. 1, p. 1087-1088 | - |
dc.identifier.issn | 0885-8950 | - |
dc.identifier.uri | http://hdl.handle.net/10722/263373 | - |
dc.description.abstract | Residential 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.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59 | - |
dc.relation.ispartof | IEEE Transactions on Power Systems | - |
dc.rights | IEEE 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.subject | Deep learning | - |
dc.subject | Meter-level load forecasting | - |
dc.subject | Recurrent neural network | - |
dc.subject | short-term load forecasting | - |
dc.title | Short-Term Residential Load Forecasting Based on Resident Behaviour Learning | - |
dc.type | Article | - |
dc.identifier.email | Hill, DJ: dhill@eee.hku.hk | - |
dc.identifier.authority | Hill, DJ=rp01669 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TPWRS.2017.2688178 | - |
dc.identifier.scopus | eid_2-s2.0-85046692362 | - |
dc.identifier.hkuros | 293629 | - |
dc.identifier.volume | 33 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 1087 | - |
dc.identifier.epage | 1088 | - |
dc.identifier.isi | WOS:000418776400102 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0885-8950 | - |