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

by Chenhua Ni 1,2,* and Xiandong Ma 2
1
National Ocean Technology Center, Tianjin 300112, China
2
Engineering Department, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK
*
Author to whom correspondence should be addressed.
Energies 2018, 11(8), 2097; https://doi.org/10.3390/en11082097
Received: 26 May 2018 / Revised: 27 June 2018 / Accepted: 28 June 2018 / Published: 13 August 2018
(This article belongs to the Section Electrical Power and Energy System)
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 proposed approach 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. View Full-Text
Keywords: wave energy converter; power prediction; ocean energy; artificial neural network; deep learning; convolutional neural network wave energy converter; power prediction; ocean energy; artificial neural network; deep learning; convolutional neural network
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Ni, C.; Ma, X. Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs. Energies 2018, 11, 2097.

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