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Integrated deep learning model for predicting electrical power generation from wave energy converter

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Integrated deep learning model for predicting electrical power generation from wave energy converter. / Ni, Chenhua; Ma, Xiandong; Wang, Ji.

2019 25th IEEE International Conference on Automation & Computing (ICAC'19). IEEE, 2019.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Ni, C, Ma, X & Wang, J 2019, Integrated deep learning model for predicting electrical power generation from wave energy converter. in 2019 25th IEEE International Conference on Automation & Computing (ICAC'19). IEEE. https://doi.org/10.23919/IConAC.2019.8895237

APA

Ni, C., Ma, X., & Wang, J. (2019). Integrated deep learning model for predicting electrical power generation from wave energy converter. In 2019 25th IEEE International Conference on Automation & Computing (ICAC'19) IEEE. https://doi.org/10.23919/IConAC.2019.8895237

Vancouver

Ni C, Ma X, Wang J. Integrated deep learning model for predicting electrical power generation from wave energy converter. In 2019 25th IEEE International Conference on Automation & Computing (ICAC'19). IEEE. 2019 https://doi.org/10.23919/IConAC.2019.8895237

Author

Ni, Chenhua ; Ma, Xiandong ; Wang, Ji. / Integrated deep learning model for predicting electrical power generation from wave energy converter. 2019 25th IEEE International Conference on Automation & Computing (ICAC'19). IEEE, 2019.

Bibtex

@inproceedings{ea1c52997b594e4686f70b16e87e25bd,
title = "Integrated deep learning model for predicting electrical power generation from wave energy converter",
abstract = "The continuous improvement of wave and tidal energy technologies has widely boosted the development of marine energy plants. An accurate predication of the electrical power generation of marine energy not only saves costs for operation and maintenance but also improves manage the electricity consumption and reduce the uncertainty due to the intermittency of the ocean wave and tidal resources. This paper presents an integrated deep learning (DL) network comprising the long short term memory (LSTM) algorithm and the principal component analysis (PCA) to predict the electrical power generation from a wave energy converter (WEC). The results from this integrated data-driven model show the remarkable performance compared with the LSTM alone and other machine learning models. Furthermore, the experiments have shown that the high-frequency oscillating waves and the long term features in the wave have a significant impact on the model{\textquoteright}s accuracy. This finding demonstrates the superiority of the proposed model in its ability to deal with time sequence data and the effect of the high-frequency oscillating signals on the results over other machine learning methods.",
keywords = "Principal component analysis, Long short term, Deep learning, Wave energy converter, Marine energy, Power prediction",
author = "Chenhua Ni and Xiandong Ma and Ji Wang",
note = "{\textcopyright}2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2019",
month = nov,
day = "11",
doi = "10.23919/IConAC.2019.8895237",
language = "English",
isbn = "9781728125183",
booktitle = "2019 25th IEEE International Conference on Automation & Computing (ICAC'19)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Integrated deep learning model for predicting electrical power generation from wave energy converter

AU - Ni, Chenhua

AU - Ma, Xiandong

AU - Wang, Ji

N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2019/11/11

Y1 - 2019/11/11

N2 - The continuous improvement of wave and tidal energy technologies has widely boosted the development of marine energy plants. An accurate predication of the electrical power generation of marine energy not only saves costs for operation and maintenance but also improves manage the electricity consumption and reduce the uncertainty due to the intermittency of the ocean wave and tidal resources. This paper presents an integrated deep learning (DL) network comprising the long short term memory (LSTM) algorithm and the principal component analysis (PCA) to predict the electrical power generation from a wave energy converter (WEC). The results from this integrated data-driven model show the remarkable performance compared with the LSTM alone and other machine learning models. Furthermore, the experiments have shown that the high-frequency oscillating waves and the long term features in the wave have a significant impact on the model’s accuracy. This finding demonstrates the superiority of the proposed model in its ability to deal with time sequence data and the effect of the high-frequency oscillating signals on the results over other machine learning methods.

AB - The continuous improvement of wave and tidal energy technologies has widely boosted the development of marine energy plants. An accurate predication of the electrical power generation of marine energy not only saves costs for operation and maintenance but also improves manage the electricity consumption and reduce the uncertainty due to the intermittency of the ocean wave and tidal resources. This paper presents an integrated deep learning (DL) network comprising the long short term memory (LSTM) algorithm and the principal component analysis (PCA) to predict the electrical power generation from a wave energy converter (WEC). The results from this integrated data-driven model show the remarkable performance compared with the LSTM alone and other machine learning models. Furthermore, the experiments have shown that the high-frequency oscillating waves and the long term features in the wave have a significant impact on the model’s accuracy. This finding demonstrates the superiority of the proposed model in its ability to deal with time sequence data and the effect of the high-frequency oscillating signals on the results over other machine learning methods.

KW - Principal component analysis

KW - Long short term

KW - Deep learning

KW - Wave energy converter

KW - Marine energy

KW - Power prediction

UR - http://www.cacsuk.co.uk/application/files/5315/6657/8219/Parallel_Session.pdf

U2 - 10.23919/IConAC.2019.8895237

DO - 10.23919/IConAC.2019.8895237

M3 - Conference contribution/Paper

SN - 9781728125183

BT - 2019 25th IEEE International Conference on Automation & Computing (ICAC'19)

PB - IEEE

ER -