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  • Enhancing Long-Term Predictive Accuracy in Wave Energy Converters through a Dual-Model Approach

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Enhancing Long-Term Predictive Accuracy in Wave Energy Converters through a Dual-Model Approach

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Enhancing Long-Term Predictive Accuracy in Wave Energy Converters through a Dual-Model Approach. / Wu, Yueqi; Sheng, Wanan; Taylor, C. James et al.
Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference. 2024. p. 633-640 193-2024-TPC-0881.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Wu, Y, Sheng, W, Taylor, CJ, Aggidis, G & Ma, X 2024, Enhancing Long-Term Predictive Accuracy in Wave Energy Converters through a Dual-Model Approach. in Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference., 193-2024-TPC-0881, pp. 633-640.

APA

Wu, Y., Sheng, W., Taylor, C. J., Aggidis, G., & Ma, X. (2024). Enhancing Long-Term Predictive Accuracy in Wave Energy Converters through a Dual-Model Approach. In Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference (pp. 633-640). Article 193-2024-TPC-0881

Vancouver

Wu Y, Sheng W, Taylor CJ, Aggidis G, Ma X. Enhancing Long-Term Predictive Accuracy in Wave Energy Converters through a Dual-Model Approach. In Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference. 2024. p. 633-640. 193-2024-TPC-0881

Author

Wu, Yueqi ; Sheng, Wanan ; Taylor, C. James et al. / Enhancing Long-Term Predictive Accuracy in Wave Energy Converters through a Dual-Model Approach. Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference. 2024. pp. 633-640

Bibtex

@inproceedings{bf2f2a67bf1142309fafb690c77ffbee,
title = "Enhancing Long-Term Predictive Accuracy in Wave Energy Converters through a Dual-Model Approach",
abstract = "This paper presents the challenges and advancements in wave energy converters (WECs), focusing on the TALOS WEC, which utilises machine learning to predict power output. While this model shows high short-term accuracy, its long-term predictions suffer from increasing errors. To address this, the paper proposes a dual-model approach, where a supplementary error prediction model is incorporated alongside the primary model to monitor and correct long-term prediction errors. This approach has shown promising results in improving the accuracy of long-term predictions, marking a significant step in WEC research and applications.",
keywords = "WEC, Long-term prediction, Machine learning, Long- Short Term Memory (LSTM), Artificial Neural Network (ANN)",
author = "Yueqi Wu and Wanan Sheng and Taylor, {C. James} and George Aggidis and Xiandong Ma",
year = "2024",
month = jun,
day = "17",
language = "English",
pages = "633--640",
booktitle = "Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference",

}

RIS

TY - GEN

T1 - Enhancing Long-Term Predictive Accuracy in Wave Energy Converters through a Dual-Model Approach

AU - Wu, Yueqi

AU - Sheng, Wanan

AU - Taylor, C. James

AU - Aggidis, George

AU - Ma, Xiandong

PY - 2024/6/17

Y1 - 2024/6/17

N2 - This paper presents the challenges and advancements in wave energy converters (WECs), focusing on the TALOS WEC, which utilises machine learning to predict power output. While this model shows high short-term accuracy, its long-term predictions suffer from increasing errors. To address this, the paper proposes a dual-model approach, where a supplementary error prediction model is incorporated alongside the primary model to monitor and correct long-term prediction errors. This approach has shown promising results in improving the accuracy of long-term predictions, marking a significant step in WEC research and applications.

AB - This paper presents the challenges and advancements in wave energy converters (WECs), focusing on the TALOS WEC, which utilises machine learning to predict power output. While this model shows high short-term accuracy, its long-term predictions suffer from increasing errors. To address this, the paper proposes a dual-model approach, where a supplementary error prediction model is incorporated alongside the primary model to monitor and correct long-term prediction errors. This approach has shown promising results in improving the accuracy of long-term predictions, marking a significant step in WEC research and applications.

KW - WEC

KW - Long-term prediction

KW - Machine learning

KW - Long- Short Term Memory (LSTM)

KW - Artificial Neural Network (ANN)

M3 - Conference contribution/Paper

SP - 633

EP - 640

BT - Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference

ER -