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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22762
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dc.contributor.authorHuang, B-
dc.contributor.authorWu, D-
dc.contributor.authorLai, CS-
dc.contributor.authorCun, X-
dc.contributor.authorYuan, H-
dc.contributor.authorXu, F-
dc.contributor.authorLai, LL-
dc.contributor.authorTsang, KF-
dc.date.accessioned2021-05-24T18:08:31Z-
dc.date.available2018-09-24-
dc.date.available2021-05-24T18:08:31Z-
dc.date.issued2018-09-27-
dc.identifier.citationHuang, B., Wu, D., Lai, C.S., Cun, X., Yuan, H., Xu, F., Lai, L.L. and Tsang, K.F. (2018) 'Load Forecasting based on Deep Long Short-term Memory with Consideration of Costing Correlated Factor,' Proceedings of the 16th International Conference on Industrial Informatics (INDIN 2018), Porto, Portugal, 18-20 July, pp. 496 - 501. doi: 10.1109/INDIN.2018.8472040.en_US
dc.identifier.isbn978-1-5386-4829-2-
dc.identifier.issn1935-4576-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/22762-
dc.description.sponsorshipGuangdong University of Technology, Guangzhou, China, Grant from the Financial and Education Department of Guangdong Province 2016[202]: Key Discipline Construction Programme; Education Department of Guangdong Province: New and integrated energy system theory and technology research group, project number 2016KCXTD022; National Science Foundation of China: A Time-Based-Demand- Response Program of Compensated Multiple-Shape Pricing Scheme, Grant No. 51707041; State Grid Technology Project: the Smart Monitoring Techniques Research in Self- Correlated Framework for Power Utility (Grant No. 5211011600RJ); Education Department of Guangdong Province: The Power Market Advanced Service for Load Monitoring Technologies, 2016KQNCX047.en_US
dc.format.extent496 - 501-
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.subjectrecurrent neural networken_US
dc.subjectpower marketen_US
dc.subjectload forecasten_US
dc.subjectsmart griden_US
dc.subjectmachine learningen_US
dc.subjectdemand responseen_US
dc.subjectmarket deregulationen_US
dc.titleLoad Forecasting based on Deep Long Short-term Memory with Consideration of Costing Correlated Factoren_US
dc.typeConference Paperen_US
dc.identifier.doihttps://doi.org/10.1109/INDIN.2018.8472040-
dc.relation.isPartOfProceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018-
pubs.publication-statusPublished-
dc.identifier.eissn2378-363X-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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