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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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TALOS Wave Energy Converter Power Output Prediction Analysis Based on a Machine Learning Approach. / Wu, Yueqi; Sheng, Wanan; Taylor, James et al.
In: International Journal of Offshore and Polar Engineering , Vol. 34, No. 3, ISOPE-24-34-3-306, 09.09.2024, p. 306–313.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wu, Y, Sheng, W, Taylor, J, Aggidis, G & Ma, X 2024, 'TALOS Wave Energy Converter Power Output Prediction Analysis Based on a Machine Learning Approach', International Journal of Offshore and Polar Engineering , vol. 34, no. 3, ISOPE-24-34-3-306, pp. 306–313. https://doi.org/10.17736/ijope.2024.jc918

APA

Wu, Y., Sheng, W., Taylor, J., Aggidis, G., & Ma, X. (2024). TALOS Wave Energy Converter Power Output Prediction Analysis Based on a Machine Learning Approach. International Journal of Offshore and Polar Engineering , 34(3), 306–313. Article ISOPE-24-34-3-306. https://doi.org/10.17736/ijope.2024.jc918

Vancouver

Wu Y, Sheng W, Taylor J, Aggidis G, Ma X. TALOS Wave Energy Converter Power Output Prediction Analysis Based on a Machine Learning Approach. International Journal of Offshore and Polar Engineering . 2024 Sept 9;34(3):306–313. ISOPE-24-34-3-306. doi: 10.17736/ijope.2024.jc918

Author

Wu, Yueqi ; Sheng, Wanan ; Taylor, James et al. / TALOS Wave Energy Converter Power Output Prediction Analysis Based on a Machine Learning Approach. In: International Journal of Offshore and Polar Engineering . 2024 ; Vol. 34, No. 3. pp. 306–313.

Bibtex

@article{aae2b45026fe47c4a0454af88bba6731,
title = "TALOS Wave Energy Converter Power Output Prediction Analysis Based on a Machine Learning Approach",
abstract = "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.",
keywords = "TALOS, WEC, Power prediction, Machine learning, LSTM",
author = "Yueqi Wu and Wanan Sheng and James Taylor and George Aggidis and Xiandong Ma",
year = "2024",
month = sep,
day = "9",
doi = "10.17736/ijope.2024.jc918",
language = "English",
volume = "34",
pages = "306–313",
journal = "International Journal of Offshore and Polar Engineering ",
issn = "1053-5381",
publisher = "ISOPE",
number = "3",

}

RIS

TY - JOUR

T1 - TALOS Wave Energy Converter Power Output Prediction Analysis Based on a Machine Learning Approach

AU - Wu, Yueqi

AU - Sheng, Wanan

AU - Taylor, James

AU - Aggidis, George

AU - Ma, Xiandong

PY - 2024/9/9

Y1 - 2024/9/9

N2 - 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.

AB - 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.

KW - TALOS

KW - WEC

KW - Power prediction

KW - Machine learning

KW - LSTM

U2 - 10.17736/ijope.2024.jc918

DO - 10.17736/ijope.2024.jc918

M3 - Journal article

VL - 34

SP - 306

EP - 313

JO - International Journal of Offshore and Polar Engineering

JF - International Journal of Offshore and Polar Engineering

SN - 1053-5381

IS - 3

M1 - ISOPE-24-34-3-306

ER -