Pre-Evaluation of Wave Energy Converter Deployment in the Baltic Sea Through Site Limitations Using CMEMS Hindcast, Sentinel-1, and Wave Buoy Data

Nikon Vidjajev (), Sander Rikka () and Victor Alari ()
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Nikon Vidjajev: Department of Marine Systems, Tallinn University of Technology, 12618 Tallinn, Estonia
Sander Rikka: Department of Marine Systems, Tallinn University of Technology, 12618 Tallinn, Estonia
Victor Alari: Department of Marine Systems, Tallinn University of Technology, 12618 Tallinn, Estonia

Energies, 2025, vol. 18, issue 14, 1-22

Abstract: This study assesses the wave energy potential and spectral variability in the Väinameri—a semi-sheltered, island-filtered basin on Estonia’s west coast—by combining six months of high-resolution in situ wave spectra with deep learning-enhanced satellite retrievals. Directional spectra were recorded at Rohuküla Harbor using a wave-following LainePoiss buoy from June to December 2024. In parallel, one-dimensional wave spectra were reconstructed from Sentinel-1 SAR imagery using a long short-term memory (LSTM) neural network trained on more than 71,000 collocations with NORA3 WAM hindcasts. Spectral pairs matched within a ±1 h window exhibited strong agreement in the dominant 0.2–0.4 Hz frequency band, while systematic underestimation at higher frequencies reflected both the radar resolution limits and the short-period, wind–sea-dominated nature of the Baltic Sea. Our results confirm that LSTM-enhanced SAR retrievals enable robust bulk and spectral wave characterizations in data-sparse nearshore regions, and offer a practical basis for the site evaluation, device tuning, and survivability testing of pilot-scale wave energy converters under both typical and storm-driven forcing conditions.

Keywords: LSTM neural network; Sentinel-1 SAR; Baltic Sea; wave energy resource assessment; wave energy converter (WEC) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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