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

共 50 条

[21] Analysis of Power Generation for Direct Wave Energy Converter
Park, Joon Sung
Kim, Jin-Hong
Hyon, Byong Jo
Choi, Jun-Hyuk
2017 14TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING/ELECTRONICS, COMPUTER, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGY (ECTI-CON), 2017, : 714 - 717
[22] Kalmen estimator as a robust solution for output power maximization of wave energy converter
Kassem, Ahmed M.
Besheer, Ahmad H.
Atawi, Ibrahem E.
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2015, 10 (04) : 390 - 395
[23] Preliminary laboratorial determination of the REEFS novel wave energy converter power output
Lopes de Almeida, J. P. P. G.
Mujtaba, B.
Oliveira Fernandes, A. M.
RENEWABLE ENERGY, 2018, 122 : 654 - 664
[24] OPTIMIZING POWER OUTPUT OF A WAVE ENERGY CONVERTER BY EMPLOYING SUPERPOSED HYDRODYNAMIC MODEL
Kathyayani, Nandakumar
Samad, Abdus
PROCEEDINGS OF ASME 2021 INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION (IMECE2021), VOL 8B, 2021,
[25] Analysis and prediction of power distribution network loss based on machine learning
Liu, Jie
Cao, Yijia
Li, Yong
Guo, Yixiu
Deng, Wei
INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS, 2023, 36 (04)
[26] Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression
Yadav, Ojaswa
Kannan, Ramani
Meraj, Sheikh T.
Masaoud, Ammar
MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
[27] Application of machine learning methods in photovoltaic output power prediction: A review
Zhang, Wenyong
Li, Qingwei
He, Qifeng
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2022, 14 (02)
[28] Wind power output prediction: a comparative study of extreme learning machine
Wang, Zheng-Chuang
Niu, Jin-Cai
FRONTIERS IN ENERGY RESEARCH, 2023, 11
[29] Machine Learning approach for Prediction of residual energy in batteries
Jayakumar, T.
Gowda, Natesh M.
Sujatha, R.
Bhukya, Shankar Nayak
Padmapriya, G.
Radhika, S.
Mohanavel, V.
Sudhakar, M.
Sathyamurthy, Ravishankar
ENERGY REPORTS, 2022, 8 : 756 - 764
[30] Prediction of binding energy using machine learning approach
Pandey, Bishnu
Giri, Subash
Pant, Rajan Dev
Jalan, Muskan
Chaudhary, Ashok
Adhikari, Narayan Prasad
AIP ADVANCES, 2024, 14 (10)

← 1 2 3 4 5 →