Data-driven and physics-informed wind prediction in offshore wind farms

Bibliographic Details
Main Author: Elshafei, Basem
Format: Thesis (University of Nottingham only)
Language:English
Published: 2023
Subjects:
Online Access:https://eprints.nottingham.ac.uk/76157/
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author Elshafei, Basem
author_facet Elshafei, Basem
author_sort Elshafei, Basem
building Nottingham Research Data Repository
collection Online Access
first_indexed 2025-11-14T20:58:49Z
format Thesis (University of Nottingham only)
id nottingham-76157
institution University of Nottingham Malaysia Campus
institution_category Local University
language English
last_indexed 2025-11-14T20:58:49Z
publishDate 2023
recordtype eprints
repository_type Digital Repository
spelling nottingham-761572023-12-31T04:40:17Z https://eprints.nottingham.ac.uk/76157/ Data-driven and physics-informed wind prediction in offshore wind farms Elshafei, Basem 2023-12-31 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/76157/1/University_of_Nottingham_PhD_Thesis_with_corrections.pdf Elshafei, Basem (2023) Data-driven and physics-informed wind prediction in offshore wind farms. PhD thesis, University of Nottingham. Offshore wind power plants; Machine learning; Winds Speed; Multisensor data fusion; Wind power
spellingShingle Offshore wind power plants; Machine learning; Winds
Speed; Multisensor data fusion; Wind power
Elshafei, Basem
Data-driven and physics-informed wind prediction in offshore wind farms
title Data-driven and physics-informed wind prediction in offshore wind farms
title_full Data-driven and physics-informed wind prediction in offshore wind farms
title_fullStr Data-driven and physics-informed wind prediction in offshore wind farms
title_full_unstemmed Data-driven and physics-informed wind prediction in offshore wind farms
title_short Data-driven and physics-informed wind prediction in offshore wind farms
title_sort data-driven and physics-informed wind prediction in offshore wind farms
topic Offshore wind power plants; Machine learning; Winds
Speed; Multisensor data fusion; Wind power
url https://eprints.nottingham.ac.uk/76157/