This study evaluates the potential of applying echo state networks (ESN) and autoregression (AR) for dynamic time series prediction of free surface elevation for use in wave energy converters (WECs). The performance of these models is evaluated on time series data at different water depths and wave conditions, including both measured and simulated data with a focus on real-time prediction of ocean waves at a given location without resolving for the surrounding ocean surface, in other words, short-time single-point forecasting. The work presented includes training the models on historical wave data and testing their ability to predict phase-resolved future surface wave patterns for short-time forecasts. Additionally, this study discusses the feasibility of deploying these models for extended time intervals. It provides valuable insights into the trade-offs between accuracy and practicality in the real-time implementation of predictive models for wave elevation, which are needed in wave energy converters to optimise the control algorithm.

中文翻译：

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使用自回归和回波状态网络进行时域中的实时海浪预测


本研究评估了应用回波状态网络 （ESN） 和自回归 （AR） 对自由表面高程进行动态时间序列预测以用于波浪能转换器 （WEC） 的潜力。这些模型的性能是根据不同水深和波浪条件的时间序列数据进行评估的，包括测量和模拟数据，重点是实时预测给定位置的海浪，而不解决周围的海洋表面，换句话说，短时单点预报。所展示的工作包括在历史波数据上训练模型，并测试它们预测相位分辨的未来表面波模式以进行短时预报的能力。此外，本研究还讨论了在更长的时间间隔内部署这些模型的可行性。它为实时实施波浪海拔预测模型的准确性和实用性之间的权衡提供了宝贵的见解，这是波浪能转换器优化控制算法所必需的。