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Predicting Output Power for Nearshore Wave Energy Harvesting

Department of Information and Communications Engineering, Myongji University, 116 Myongji-ro, Yongin, Gyeonggi 17058, Korea
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(4), 566;
Received: 22 February 2018 / Revised: 15 March 2018 / Accepted: 3 April 2018 / Published: 5 April 2018
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Energy harvested from a Wave Energy Converter (WEC) varies greatly with the location of its installation. Determining an optimal location that can result in maximum output power is therefore critical. In this paper, we present a novel approach to predicting the output power of a nearshore WEC by characterizing ocean waves using floating buoys. We monitored the movement of the buoys using an Arduino-based data collection module, including a gyro-accelerometer sensor and a wireless transceiver. The collected data were utilized to train and test prediction models. The models were developed using machine learning algorithms: SVM, RF and ANN. The results of the experiments showed that measurements from the data collection module can yield a reliable predictor of output power. Furthermore, we found that the predictors work better when the regressors are combined with a classifier. The accuracy of the proposed prediction model suggests that it could be extremely useful in both locating optimal placement for wave energy harvesting plants and designing the shape of the buoys used by them. View Full-Text
Keywords: renewable energy; machine learning; wave energy converter; regression renewable energy; machine learning; wave energy converter; regression

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Deberneh, H.M.; Kim, I. Predicting Output Power for Nearshore Wave Energy Harvesting. Appl. Sci. 2018, 8, 566.

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