ORCID
- Matthew Craven: 0000-0001-9522-6173
- Daniel Conley: 0000-0001-6822-5386
Abstract
The increasing demand for offshore wind energy underscores the need for accurate wind speed estimation to support the design and operation of offshore wind farms. High-Frequency Radar (HFR), a widely used remote sensing technology in oceanographic research, offers promising potential for wind resource assessment, particularly in areas where conventional measurements are limited. This study explores the application of artificial neural networks (ANNs) for offshore wind speed prediction using HFR-derived data, addressing key challenges in model development and training. A key feature of this approach is the use of a decade-long dataset from the Celtic Sea, off the southwest UK coast, incorporating the full Doppler spectrum and sea surface radial velocity. Model performance was assessed over full-year and seasonally segmented four-month periods, with RMSE values ranging from 1.99 to 2.78 m/s and NRMSE values between 12 % and 20 %, demonstrating the feasibility of HFR-informed ANN models for supporting offshore wind applications.
DOI Link
Publication Date
2025-09-29
Publication Title
Renewable Energy
Volume
256
Issue
Part G
ISSN
0960-1481
Acceptance Date
2025-09-28
Deposit Date
2025-10-02
Funding
This study has been produced under the resources of the Cornwall FLOW Accelerator Project, which was led by Celtic Sea Power and delivered in partnership with the University of Plymouth, the University of Exeter and the Offshore Renewable Energy (ORE) Catapult. The Cornwall FLOW Accelerator Project has been supported by a grant from the European Regional Development Fund, project number O5R19P03188. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.
Keywords
High-frequency radar, Artificial neural networks, Wind speed prediction, Offshore wind, Doppler spectrum
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Martzikos, N., Craven, M., Walker, D., & Conley, D. (2025) 'Enhancing offshore wind resource assessment through neural network-based HF radar data analysis', Renewable Energy, 256(Part G). Available at: 10.1016/j.renene.2025.124547
