用深度集成+校准，实现海浪高度高精度实时预测与可信不确定性评估。
AI-powered Digital Twin of the Ocean: Reliable Uncertainty Quantification for Real-time Wave Height Prediction with Deep Ensemble
- 融合LSTM与深度集成，兼顾时序预测与不确定性量化。
- 在济州实测数据上达R²>0.9，不确定性质量提升超50%。
- 适用于不同波长、振幅、周期场景，助力海洋数字孪生构建。
环境污染物排放和化石燃料枯竭促使可再生能源发电需求上升。然而，其稳定性常受能量密度低和非平稳条件制约。波浪能转换器（WEC）尤其需要可靠的实时波高预测，以应对不规则波形带来的效率低下与运行不稳定问题。本研究提出一种基于AI的实时波高预测模型，结合长短期记忆网络（LSTM）进行时序预测，以及深度集成（DE）实现鲁棒的不确定性量化（UQ），确保高精度与高可靠性。为增强可靠性，引入不确定性校准技术，显著提升量化不确定性质量。基于韩国济州岛振荡水柱式波浪能转换器（OWC-WEC）系统的实测数据，模型在实际运行中达到R² > 0.9的预测精度，且通过简单校准使不确定性质量提升超过50%。此外，开展了全面的参数敏感性分析，揭示关键超参数对模型性能的影响，为不同波长、振幅和周期条件下的多样化运行场景提供实用指导。结果表明，该模型具备可靠预测能力，可支撑海洋数字孪生系统建设。
原文摘要 · Abstract (English)
Environmental pollution and fossil fuel depletion have prompted the need for renewable energy-based power generation. However, its stability is often challenged by low energy density and non-stationary conditions. Wave energy converters (WECs), in particular, need reliable real-time wave height prediction to address these issues caused by irregular wave patterns, which can lead to the inefficient and unstable operation of WECs. In this study, we propose an AI-powered reliable real-time wave height prediction model that integrates long short-term memory (LSTM) networks for temporal prediction with deep ensemble (DE) for robust uncertainty quantification (UQ), ensuring high accuracy and reliability. To further enhance the reliability, uncertainty calibration is applied, which has proven to significantly improve the quality of the quantified uncertainty. Using real operational data from an oscillating water column-wave energy converter (OWC-WEC) system in Jeju, South Korea, the model achieves notable accuracy (R2 > 0.9), while increasing uncertainty quality by over 50% through simple calibration technique. Furthermore, a comprehensive parametric study is conducted to explore the effects of key model hyperparameters, offering valuable guidelines for diverse operational scenarios, characterized by differences in wavelength, amplitude, and period. These results demonstrate the model's capability to deliver reliable predictions, facilitating digital twin of the ocean.
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