TALOS Wave Energy Converter Power Output Prediction Analysis Based on a Machine Learning Approach

被引：0

作者：

Wu, Yueqi ^{[1
]}

Sheng, Wanan ^{[1
]}

Taylor, C. James ^{[1
]}

Aggidis, George ^{[1
]}

Mai, Xiandong ^{[1
]}

机构：

[1] Univ Lancaster, Sch Engn, Lancaster, England

来源：

INTERNATIONAL JOURNAL OF OFFSHORE AND POLAR ENGINEERING | 2024年 / 34卷 / 03期

基金：

英国工程与自然科学研究理事会;

关键词：

TALOS; WEC; power prediction; machine learning; LSTM; WIND;

D O I：

10.17736/ijope.2024.jc918

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Wave energy shows potential to provide electricity in a renewable manner. The TALOS WEC (Wave Energy Converter) is a unique design with six PTO (Power Take-Off) elements to provide six degrees of freedom (DOFs). It is potentially able to harvest energy more efficiently than traditional single-DOF devices. As a step towards its optimisation and control, a power prediction model is developed, using the wave elevation and motions of the WEC to predict the power output of each PTO. The results show that using LSTM (Long-Short Term Memory) has a higher prediction accuracy than the other approaches considered.

引用

页码：306 / 313

页数：8

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