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A Transfer Learning Approach to Create Energy Forecasting Models for Building Fleets
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-5704-4667
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-3489-7429
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-8561-7963
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2024 (English)In: 2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, 2024, p. 438-444Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2024. p. 438-444
Series
IEEE International Conference on Smart Grid Communications, ISSN 2373-6836, E-ISSN 2474-2902
Keywords [en]
Building fleet, Energy consumption, Transfer learning, LSTM, DTW, Hierarchical clustering, Time series forecasting
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-110870DOI: 10.1109/SmartGridComm60555.2024.10738094OAI: oai:DiVA.org:ltu-110870DiVA, id: diva2:1916837
Conference
2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), September 17-20, 2024, Oslo, Norway
Note

ISBN for host publication: 979-8-3503-1855-5;

Funder: European Commission (grant number 610619-EPP-1-2019-1-FREPPKA1-JMD-MOB), (EMJMD GENIAL Project);

Available from: 2024-11-28 Created: 2024-11-28 Last updated: 2024-11-28Bibliographically approved

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Shahid, ZahraaMitra, KaranSaguna, SagunaÅhlund, Christer

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Vasquez Torres, MonicaShahid, ZahraaMitra, KaranSaguna, SagunaÅhlund, Christer
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