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