A Comparison of Ocean Wave Height Forecasting Methods for Ocean Wave Energy Conversion Systems

Ocean wave height plays an important role in the operation status of ocean wave energy conversion systems. In this paper, the future continuous ocean wave height within 2~3 s is forecasted by three methods, the autoregressive moving average model (ARMA) method, backpropagation (BP) neural network me...

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Bibliographic Details
Published inWater (Basel) Vol. 15; no. 18; p. 3256
Main Authors Guodong, Qin, Zhongxian, Chen
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2023
Subjects
Comparative analysis
Efficiency
Energy
error comparison
Forecasting
forecasting method
Forecasts and trends
Methods
Neural networks
Ocean
ocean wave energy
ocean wave height
Ocean waves
Optimization
Sailors
Time series
Water waves
Wave power
China
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Summary:Ocean wave height plays an important role in the operation status of ocean wave energy conversion systems. In this paper, the future continuous ocean wave height within 2~3 s is forecasted by three methods, the autoregressive moving average model (ARMA) method, backpropagation (BP) neural network method, and radial basis function (RBF) neural network method. Then, the error between suggested forecast results and corresponding measured results are compared by the root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient R. The comparison result indicates that the RBF neural network method is preferred to the other two methods, having the advantage of high accuracy. Lastly, the reasons for the errors of the three forecasting methods are analyzed. This study signifies that the RBF neural network method is beneficial to the operation control and efficiency improvement of ocean wave energy conversion systems.
ISSN:2073-4441
2073-4441
DOI:10.3390/w15183256