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] Energy poverty prediction in the United Kingdom: A machine learning approach
Al Kez, Dlzar
Foley, Aoife
Abdul, Zrar Khald
Rio, Dylan Furszyfer Del
ENERGY POLICY, 2024, 184
[22] A Review of Machine Learning-Based Photovoltaic Output Power Forecasting: Nordic Context
Dimd, Berhane Darsene
Voller, Steve
Cali, Umit
Midtgard, Ole-Morten
IEEE ACCESS, 2022, 10 : 26404 - 26425
[23] A Univariate and multivariate machine learning approach for prediction of significant wave height
Domala, Vamshikrishna
Kim, Tae-Wan
2022 OCEANS HAMPTON ROADS, 2022,
[24] Wind Power Prediction Based on Machine Learning and Deep Learning Models
Tarek, Zahraa
Shams, Mahmoud Y.
Elshewey, Ahmed M.
El-kenawy, El-Sayed M.
Ibrahim, Abdelhameed
Abdelhamid, Abdelaziz A.
El-dosuky, Mohamed A.
CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 74 (01): : 715 - 732
[25] Advancing Solar Energy: Machine Learning Approaches for Predicting Photovoltaic Power Output
Nassereddine, Kawsar
Turzynski, Marek
Lukianov, Mykola
Strzelecki, Ryszard
18TH INTERNATIONAL CONFERENCE ON COMPATIBILITY, POWER ELECTRONICS AND POWER ENGINEERING, CPE-POWERENG 2024, 2024,
[26] A Wave Prediction Framework Based on Machine Learning and the Third Generation Wave Model
Liu, Shuai
Zhang, Xinshu
JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING-TRANSACTIONS OF THE ASME, 2022, 144 (01):
[27] Experimental Assessment of Miniature Wave-Energy-Converter Performance: Output Analysis in Simulated Wave Conditions
Rautmann, Louis Leon
Ahmed, Minhaz
Dankwort, Torben
Kosleck, Sascha
Gojdka, Bjoern
OCEANS 2024 - SINGAPORE, 2024,
[28] Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approach
Cornejo-Bueno, L.
Nieto-Borge, J. C.
Garcia-Diaz, P.
Rodriguez, G.
Salcedo-Sanz, S.
RENEWABLE ENERGY, 2016, 97 : 380 - 389
[29] Advanced automated machine learning framework for photovoltaic power output prediction using environmental parameters and SHAP interpretability
Bakht, Muhammad Paend
Mohd, Mohd Norzali Haji
Ibrahim, Babul Salam K. S. M. Kader
Khan, Nuzhat
Sheikh, Usman Ullah
Ab Rahman, Ab Al-Hadi
RESULTS IN ENGINEERING, 2025, 25
[30] Comparison and Analysis of Prediction Models for Locomotive Traction Energy Consumption Based on the Machine Learning
Liang, Huize
Zhang, Yuying
Yang, Peiyu
Wang, Lie
Gao, Chunlei
IEEE ACCESS, 2023, 11 : 38502 - 38513

← 1 2 3 4 5 →