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

Ni, Chenhua and Ma, Xiandong (2018) Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs. Energies, 11 (8). ISSN 1996-1073

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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 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.

Item Type:
Journal Article
Journal or Publication Title:
Energies
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700
Subjects:
ID Code:
126938
Deposited By:
Deposited On:
16 Aug 2018 09:34
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Sep 2020 04:43