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Forecasting the wave energy flux in the China sea using a blend of physics and artificial intelligence

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  • Dai, Chao
  • Li, Xiang-yang
  • Qin, Yi-yi
  • Jiang, Song
  • Yan, Ting-yu
  • Luo, Min
  • Jin, Xin

Abstract

Estimations of wave energy flux are critical for perennial/stable marine renewable energy utilization. This study proposes combining physics with artificial intelligence to predict three key wave parameters—significant wave height, mean wave period, and the wave energy flux. The forecasts are run over the China Seas and adjacent waters using ERA5 reanalysis data. The performance of the combined model is evaluated against two alternatives, a convolutional LSTM and a data driven version. Forecasts are run at horizons of 1–18 h. In addition, the power output of wave energy converters at three selected nearshore sites is analyzed. In nearly all scenarios, the combined model is more accurate than pure machine learning approaches. Validation against satellite altimeter observations further confirms the model's generalizability. These findings support the idea of incorporating physics-based methods in machine learning models.

Suggested Citation

  • Dai, Chao & Li, Xiang-yang & Qin, Yi-yi & Jiang, Song & Yan, Ting-yu & Luo, Min & Jin, Xin, 2026. "Forecasting the wave energy flux in the China sea using a blend of physics and artificial intelligence," Renewable Energy, Elsevier, vol. 272(C).
    • Handle: RePEc:eee:renene:v:272:y:2026:i:c:s0960148126009109
    DOI: 10.1016/j.renene.2026.126084
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