基于注意力的深度学习模型,用于相位分辨波预测

Jialun Chen, David Gunawan, Paul Taylor, Yunzhuo Chen, Ian A. Milne, Wenhua Zhao
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引用次数: 0

摘要

相位分辨波浪预测能力(即使只能提前预测两个波浪周期)对波浪能转换器(WECs)的优化控制具有重要价值,可显著提高发电效率。以往关于逐波预测的研究表明,在使用合成波浪数据时,人工神经网络(ANN)模型在预测精度和预测范围方面都优于基于线性波浪理论的传统模型。然而,由于现场波浪条件和浮标位置的变化,人工神经网络模型的预测性能大打折扣。为了克服这些局限性,我们开发了一种基于神经网络和注意力机制的新型波浪预测方法。本研究利用海上测得的波浪数据对新模型进行了验证。该模型利用位于上波位置的三个 Sofar Spotter 波浪浮标过去的时间历史来预测位于下波位置的 Datawell Waverider-4 的垂直运动。结果表明,基于注意力的神经网络模型能够捕捉浮标位移的缓慢变化,与标准方差网络和长短期记忆(LSTM)模型相比,可减少预测误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An attention-based deep learning model for phase-resolved wave prediction
Phase-resolved wave prediction capability, even if only over two wave periods in advance, is of value for optimal control of wave energy converters (WECs), resulting in a dramatic increase in power generation efficiency. Previous studies on wave-by-wave predictions have shown that an Artificial Neural Network (ANN) model can outperform the traditional linear wave theory-based model in terms of both prediction accuracy and prediction horizon when using synthetic wave data. However, the prediction performance of ANN models is significantly reduced by the varying wave conditions and buoy positions that occur in the field. To overcome these limitations, a novel wave prediction method is developed based on the neural network with an attention mechanism. This study validates the new model using wave data measured at sea. The model utilizes past time histories of three Sofar Spotter wave buoys at upwave locations to predict the vertical motion of a Datawell Waverider-4 at a downwave location. The results show that the attention-based neural network model is capable of capturing the slow variation in the displacement of the buoys, which reduces the prediction error compared to a standard ANN and Long Short-Term Memory (LSTM) model.
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