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 language | English |
|---|---|
| Article number | 012013 |
| Journal | Journal of physics: Conference series |
| Volume | 2638 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2023 |
| Externally published | Yes |
| Event | International Conference on Applied Statistics, Modeling and Advanced Algorithms, ASMA 2023 - Qingdao, China Duration: 28 Jul 2023 → 30 Jul 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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