Document Type
Article
Publication Date
11-28-2025
Abstract
Offshore wind farm projects are being promoted in the seas surrounding the Korean Peninsula to secure renewable energy. To support site selection, offshore wind resource maps were generated using deep neural networks trained on Sentinel-1 SAR imagery, numerical weather prediction data, offshore wind observations, sea surface temperature, and bathymetry. The deep neural network (DNN) framework consisted of six sub-models targeting eastward and northward wind components across three regions—the Yellow Sea, Korea Strait, and East Sea—to account for spatial heterogeneity. The proposed models outperformed existing approaches, achieving mean absolute errors (MAE) ranging from 1.31 to 1.69 m/s and correlation coefficients (CC) between 0.827 and 0.913. These DNN models were then applied to produce offshore wind energy maps at a 150 m resolution, effectively capturing seasonal and regional variability. The resulting high-resolution maps provide valuable insights for evaluating the suitability of existing wind farm sites and identifying potential new candidates.
Recommended Citation
Joh, J.S.-u.; Nghiem, S.V.; Kafatos, M.; Liu, J.; Kim, J.; Kim, S.H.; Lee, Y. AI-Based Mapping of Offshore Wind Energy Around the Korean Peninsula Using Sentinel-1 SAR and NumericalWeather Prediction Data. Energies 2025, 18, 6252. https://doi.org/10.3390/en18236252
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The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
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Comments
This article was originally published in Energies, volume 18, in 2025. https://doi.org/10.3390/en18236252