Zobrazeno 1 - 10
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pro vyhledávání: '"Warder"'
Autor:
Warder, Simon C, Piggott, Matthew D
In this work, we assess global offshore wind energy resources, wake-induced losses, array layout optimisation potential and climate change impacts. We first map global offshore ambient wind resource from reanalysis data. We estimate wake-induced loss
Externí odkaz:
http://arxiv.org/abs/2408.15028
Autor:
Warder, Simon C, Piggott, Matthew D
Publikováno v:
Applied Energy 380 (2025)
Rapid deployment of offshore wind is expected within the coming decades to help meet climate goals. With offshore wind turbine lifetimes of 25-30 years, and new offshore leases spanning 60 years, it is vital to consider long-term changes in potential
Externí odkaz:
http://arxiv.org/abs/2408.14963
Wind energy resource assessment typically requires numerical models, but such models are too computationally intensive to consider multi-year timescales. Increasingly, unsupervised machine learning techniques are used to identify a small number of re
Externí odkaz:
http://arxiv.org/abs/2302.05886
Autor:
Benmoufok, Ellyess F., Warder, Simon C., Zhu, Elizabeth, Bhaskaran, B., Staffell, Iain, Piggott, Matthew D.
Publikováno v:
In Energy 30 December 2024 313
Autor:
Warder, Simon C., Piggott, Matthew D.
Publikováno v:
In Applied Energy 15 February 2025 380
Publikováno v:
Natural Hazards and Earth System Sciences, Vol 24, Pp 737-755 (2024)
The Maldives face the threat of tsunamis from a multitude of sources. However, the limited availability of critical data, such as bathymetry (a recurrent problem for many island nations), has meant that the impact of these threats has not been studie
Externí odkaz:
https://doaj.org/article/2035f232376f49d78dc231c7a8db4c2e
Publikováno v:
In Current Biology 9 September 2024 34(17):3917-3930
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 16, Iss 2, Pp n/a-n/a (2024)
Abstract Wind energy resource assessment typically requires numerical modeling at fine resolutions, which is computationally expensive for multi‐year timescales. Increasingly, unsupervised machine learning techniques are used to identify representa
Externí odkaz:
https://doaj.org/article/c008f3e4b63246c29e5be5da6adbde7b