Wind power prediction in new stations based on knowledge of existing stations: a cluster based multi source domain adaptation approach Tasnim, Sumaira, Rahman, Ashfaqur, Maung Than Oo, Amanullah and Haque, Md Enamul 2018, Wind power prediction in new stations based on knowledge of existing stations: a cluster based multi source domain adaptation approach, Knowledge-based systems, vol. 145, pp. 15-24, doi: 10.1016/j.knosys.2017.12.036. Attached Files Name Description MIMEType Size Downloads Title Wind power prediction in new stations based on knowledge of existing stations: a cluster based multi source domain adaptation approach Author(s) Tasnim, Sumaira Rahman, Ashfaqur Maung Than Oo, Amanullah orcid.org/0000-0002-6914-2272 Haque, Md Enamul orcid.org/0000-0002-8893-2181 Journal name Knowledge-based systems Volume number 145 Start page 15 End page 24 Total pages 10 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2018-04-01 ISSN 0950-7051 Keyword(s) wind power prediction transfer learning cluster based data distribution domain adaptation science & technology technology computer science, artificial intelligence computer science ensemble models speed classifiers Language eng DOI 10.1016/j.knosys.2017.12.036 Field of Research 090608 Renewable Power and Energy Systems Engineering (excl Solar Cells) 08 Information And Computing Sciences 15 Commerce, Management, Tourism And Services 17 Psychology And Cognitive Sciences Socio Economic Objective 850509 Wind Energy HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Grant ID N/A Copyright notice ©2018, Elsevier B.V. Persistent URL http://hdl.handle.net/10536/DRO/DU:30106160 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Engineering Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 06 Feb 2018, 22:45:06 EST Wed, 07 Feb 2018, 04:01:02 EST Sat, 14 Apr 2018, 02:30:10 EST Tue, 12 Jun 2018, 13:47:31 EST Wed, 13 Jun 2018, 06:03:06 EST Wed, 13 Jun 2018, 09:54:23 EST Wed, 13 Jun 2018, 09:56:53 EST Wed, 13 Jun 2018, 09:57:31 EST Filtered Full Citation counts:   Cited 10 times in TR Web of Science Cited 11 times in Scopus Search Google Scholar Access Statistics: 349 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Tue, 12 Jun 2018, 13:47:31 EST

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