Wave energy forecasting using artificial neural networks in the Caspian Sea

View/ Open
Author(s)
Hadadpour, Sanaz
Etemad-Shahidi, Amir
Kamranzad, Bahareh
Griffith University Author(s)
Year published
2014
Metadata
Show full item recordAbstract
Providing energy without unfavourable impact on the environment is an important issue that is considered by societies. This paper focuses on forecasting the wave energy over horizons of 1-12 h, in the southern part of the Caspian Sea. For this purpose, an artificial neural network was used to obtain the wave energy flux using two different methods. First, the components of wave energy flux, including the significant wave height and peak wave period were predicted separately and the wave energy flux was calculated by combining them; and second, the wave energy flux was forecast directly. The results showed that the prediction ...
View more >Providing energy without unfavourable impact on the environment is an important issue that is considered by societies. This paper focuses on forecasting the wave energy over horizons of 1-12 h, in the southern part of the Caspian Sea. For this purpose, an artificial neural network was used to obtain the wave energy flux using two different methods. First, the components of wave energy flux, including the significant wave height and peak wave period were predicted separately and the wave energy flux was calculated by combining them; and second, the wave energy flux was forecast directly. The results showed that the prediction of components separately yielded more accurate results. It was found that the longer the forecasting time horizon, the less accurate was the prediction. This is because in large time horizons, the previous wave characteristics have little influence on the wave energy flux. The forecast wave energy flux in both methods correlated well with observed data in short horizons.
View less >
View more >Providing energy without unfavourable impact on the environment is an important issue that is considered by societies. This paper focuses on forecasting the wave energy over horizons of 1-12 h, in the southern part of the Caspian Sea. For this purpose, an artificial neural network was used to obtain the wave energy flux using two different methods. First, the components of wave energy flux, including the significant wave height and peak wave period were predicted separately and the wave energy flux was calculated by combining them; and second, the wave energy flux was forecast directly. The results showed that the prediction of components separately yielded more accurate results. It was found that the longer the forecasting time horizon, the less accurate was the prediction. This is because in large time horizons, the previous wave characteristics have little influence on the wave energy flux. The forecast wave energy flux in both methods correlated well with observed data in short horizons.
View less >
Journal Title
Maritime Engineering
Volume
167
Issue
1
Copyright Statement
© 2013 Institution of Civil Engineers. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Subject
Civil engineering
Civil engineering not elsewhere classified
Maritime engineering