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Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs

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Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs. / Ni, Chenhua; Ma, Xiandong.
In: Energies, Vol. 11, No. 8, 2097, 13.08.2018.

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

Harvard

Ni, C & Ma, X 2018, 'Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs', Energies, vol. 11, no. 8, 2097. https://doi.org/10.3390/en11082097

APA

Ni, C., & Ma, X. (2018). Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs. Energies, 11(8), Article 2097. https://doi.org/10.3390/en11082097

Vancouver

Ni C, Ma X. Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs. Energies. 2018 Aug 13;11(8):2097. doi: 10.3390/en11082097

Author

Ni, Chenhua ; Ma, Xiandong. / Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs. In: Energies. 2018 ; Vol. 11, No. 8.

Bibtex

@article{956c68cee8064905891dd3147c9eb088,
title = "Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs",
abstract = "Successful development of a marine wave energy converter (WEC) relies strongly on the development of the power generation device, which needs to be efficient and cost-effective. An innovative multi-input approach based on the Convolutional Neural Network (CNN) is investigated to predict the power generation of a WEC system using a double-buoy oscillating body device (OBD). The results from the experimental data show that the proposed multi-input CNN performs much better at predicting results compared with the conventional artificial network and regression models. Through the power generation analysis of this double-buoy OBD, it shows that the power output has a positive correlation with the wave height when it is higher than 0.2 m, which becomes even stronger if the wave height is higher than 0.6 m. Furthermore, the proposedapproach associated with the CNN algorithm in this study can potentially detect the changes that could be due to presence of anomalies and therefore be used for condition monitoring and fault diagnosis of marine energy converters. The results are also able to facilitate controlling of the electricity balance among energy conversion, wave power produced and storage.",
keywords = "Wave energy converter, Power prediction, Ocean energy, Artificial neural network, Deep learning, Convolutional neural network",
author = "Chenhua Ni and Xiandong Ma",
year = "2018",
month = aug,
day = "13",
doi = "10.3390/en11082097",
language = "English",
volume = "11",
journal = "Energies",
issn = "1996-1073",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "8",

}

RIS

TY - JOUR

T1 - Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs

AU - Ni, Chenhua

AU - Ma, Xiandong

PY - 2018/8/13

Y1 - 2018/8/13

N2 - Successful development of a marine wave energy converter (WEC) relies strongly on the development of the power generation device, which needs to be efficient and cost-effective. An innovative multi-input approach based on the Convolutional Neural Network (CNN) is investigated to predict the power generation of a WEC system using a double-buoy oscillating body device (OBD). The results from the experimental data show that the proposed multi-input CNN performs much better at predicting results compared with the conventional artificial network and regression models. Through the power generation analysis of this double-buoy OBD, it shows that the power output has a positive correlation with the wave height when it is higher than 0.2 m, which becomes even stronger if the wave height is higher than 0.6 m. Furthermore, the proposedapproach associated with the CNN algorithm in this study can potentially detect the changes that could be due to presence of anomalies and therefore be used for condition monitoring and fault diagnosis of marine energy converters. The results are also able to facilitate controlling of the electricity balance among energy conversion, wave power produced and storage.

AB - Successful development of a marine wave energy converter (WEC) relies strongly on the development of the power generation device, which needs to be efficient and cost-effective. An innovative multi-input approach based on the Convolutional Neural Network (CNN) is investigated to predict the power generation of a WEC system using a double-buoy oscillating body device (OBD). The results from the experimental data show that the proposed multi-input CNN performs much better at predicting results compared with the conventional artificial network and regression models. Through the power generation analysis of this double-buoy OBD, it shows that the power output has a positive correlation with the wave height when it is higher than 0.2 m, which becomes even stronger if the wave height is higher than 0.6 m. Furthermore, the proposedapproach associated with the CNN algorithm in this study can potentially detect the changes that could be due to presence of anomalies and therefore be used for condition monitoring and fault diagnosis of marine energy converters. The results are also able to facilitate controlling of the electricity balance among energy conversion, wave power produced and storage.

KW - Wave energy converter

KW - Power prediction

KW - Ocean energy

KW - Artificial neural network

KW - Deep learning

KW - Convolutional neural network

U2 - 10.3390/en11082097

DO - 10.3390/en11082097

M3 - Journal article

VL - 11

JO - Energies

JF - Energies

SN - 1996-1073

IS - 8

M1 - 2097

ER -