Data science in wind energy: A case study for Norwegian offshore wind

Hao Chen*, Yngve Birkelund, Qixia Zhang

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

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Abstract

In the digital and green transitions, rapidly growing renewable energies are accumulating more and more data. Big data gives room to apply emerging data science to solve challenges in the energy sector. Offshore wind power receives accelerating attention due to its sufficient resources and cleanness. This paper uses data science, including statistical analysis and machine learning, to systematically analyse three coastal wind sites in Norway. The results show that although Norway possesses ample offshore resources, its development could be improved by natural, technical, and economic challenges that can be addressed with the help of data science. Technically, the statistical attributes and forecasting intricacy of offshore wind resources differ across various regions of Norway.

Original languageEnglish
Article number012013
JournalJournal of physics: Conference series
Volume2638
Issue number1
DOIs
Publication statusPublished - 2023
Externally publishedYes
EventInternational Conference on Applied Statistics, Modeling and Advanced Algorithms, ASMA 2023 - Qingdao, China
Duration: 28 Jul 202330 Jul 2023

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