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
收藏
页码：306 / 313
页数：8
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