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- Publisher Website: 10.1109/TSG.2017.2753802
- Scopus: eid_2-s2.0-85030636120
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Article: Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
Title | Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network |
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Authors | |
Keywords | Load forecasting Forecasting Neural networks Machine learning Smart grids |
Issue Date | 2019 |
Publisher | Institute 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? |
Abstract | As 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 Identifier | http://hdl.handle.net/10722/279149 |
ISSN | 2021 Impact Factor: 10.275 2020 SCImago Journal Rankings: 3.571 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Kong, W | - |
dc.contributor.author | Dong, ZY | - |
dc.contributor.author | Jia, Y | - |
dc.contributor.author | Hill, DJ | - |
dc.contributor.author | Xu, Y | - |
dc.contributor.author | Zhang, Y | - |
dc.date.accessioned | 2019-10-21T02:20:27Z | - |
dc.date.available | 2019-10-21T02:20:27Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Transactions on Smart Grid, 2019, v. 10 n. 1, p. 841-851 | - |
dc.identifier.issn | 1949-3053 | - |
dc.identifier.uri | http://hdl.handle.net/10722/279149 | - |
dc.description.abstract | As 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.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=5165411 | - |
dc.relation.ispartof | IEEE Transactions on Smart Grid | - |
dc.rights | IEEE 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.subject | Load forecasting | - |
dc.subject | Forecasting | - |
dc.subject | Neural networks | - |
dc.subject | Machine learning | - |
dc.subject | Smart grids | - |
dc.title | Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network | - |
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/TSG.2017.2753802 | - |
dc.identifier.scopus | eid_2-s2.0-85030636120 | - |
dc.identifier.hkuros | 307218 | - |
dc.identifier.volume | 10 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 841 | - |
dc.identifier.epage | 851 | - |
dc.identifier.isi | WOS:000455180900077 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1949-3053 | - |