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DC Field | Value | Language |
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dc.contributor.author | Tao, Y | - |
dc.contributor.author | Zhao, F | - |
dc.contributor.author | Yuan, H | - |
dc.contributor.author | Lai, CS | - |
dc.contributor.author | Xu, Z | - |
dc.contributor.author | Ng, W | - |
dc.contributor.author | Li, R | - |
dc.contributor.author | Li, X | - |
dc.contributor.author | Lai, LL | - |
dc.date.accessioned | 2021-05-24T08:39:00Z | - |
dc.date.available | 2019-12-01 | - |
dc.date.available | 2021-05-24T08:39:00Z | - |
dc.date.issued | 2020-04-16 | - |
dc.identifier.citation | Tao, Y., Zhao, F., Yuan, H., Lai, C.S., Xu, Z., Ng, W., Li, R., Li, X. and Lai, L.L. (2020) 'Revisit Neural Network based Load Forecasting', Proceedings of the 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP 2019), New Delhi, India, 10-14 Dec., pp. 1-5, doi: 10.1109/ISAP48318.2019.9065930. | en_US |
dc.identifier.isbn | 978-1-7281-3192-4 | - |
dc.identifier.isbn | 978-1-7281-3193-1 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/22747 | - |
dc.description.sponsorship | Department of Finance and Education of Guangdong Province 2016 [202]: Key Discipline Construction Program, China; Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022]. | en_US |
dc.format.extent | 1 - 5 (5) | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | IEEE | en_US |
dc.rights | © 2019 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 | en_US |
dc.subject | neural networks | en_US |
dc.subject | back propagation | en_US |
dc.subject | Elman network | en_US |
dc.subject | radial basis function | en_US |
dc.subject | long-short term memory | en_US |
dc.title | Revisit Neural Network based Load Forecasting | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.doi | https://doi.org/10.1109/ISAP48318.2019.9065930 | - |
dc.relation.isPartOf | 2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019 | - |
pubs.publication-status | Published | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | © 2019 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. | 773.68 kB | Adobe PDF | View/Open |
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