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Article: Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

TitleShort-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
Authors
KeywordsLoad forecasting
Forecasting
Neural networks
Machine learning
Smart grids
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411
Citation
IEEE Transactions on Smart Grid, 2019, v. 10 n. 1, p. 841-851 How to Cite?
AbstractAs the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.
Persistent Identifierhttp://hdl.handle.net/10722/279149
ISSN
2019 Impact Factor: 8.267
2015 SCImago Journal Rankings: 4.784
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorKong, W-
dc.contributor.authorDong, ZY-
dc.contributor.authorJia, Y-
dc.contributor.authorHill, DJ-
dc.contributor.authorXu, Y-
dc.contributor.authorZhang, Y-
dc.date.accessioned2019-10-21T02:20:27Z-
dc.date.available2019-10-21T02:20:27Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Smart Grid, 2019, v. 10 n. 1, p. 841-851-
dc.identifier.issn1949-3053-
dc.identifier.urihttp://hdl.handle.net/10722/279149-
dc.description.abstractAs the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.-
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=5165411-
dc.relation.ispartofIEEE Transactions on Smart Grid-
dc.rightsIEEE Transactions on Smart Grid. 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.subjectLoad forecasting-
dc.subjectForecasting-
dc.subjectNeural networks-
dc.subjectMachine learning-
dc.subjectSmart grids-
dc.titleShort-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network-
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/TSG.2017.2753802-
dc.identifier.scopuseid_2-s2.0-85030636120-
dc.identifier.hkuros307218-
dc.identifier.volume10-
dc.identifier.issue1-
dc.identifier.spage841-
dc.identifier.epage851-
dc.identifier.isiWOS:000455180900077-
dc.publisher.placeUnited States-

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