E3S Web Conf.
Volume 173, 20202020 5th International Conference on Advances on Clean Energy Research (ICACER 2020)
|Number of page(s)||6|
|Section||Energy Utilization and Conversion Technology|
|Published online||09 June 2020|
Optimization of Artificial Neural Networks Based Models for Wave Height Prediction
Mechanical Engineering Department, Dunarea de Jos University of Galati Domneasca Street 111, Galati, Romania
* Corresponding author: firstname.lastname@example.org
For an efficient wave energy extraction, the evolution of some specific parameters must be known. These parameters, like significant wave height and period, are mainly determined by the wind speed and influenced by some sea environment characteristics. Their evolution in time is one of the basic information necessary for designing of an accurate energy conversion system. In many scientific works the benefits of artificial neural networks based modeling are presented. These models allow the prediction and optimization of the wave parameters starting from experimentally acquired data. Due to specific calculus method of the artificial neural networks, in order to obtain accurate results, a very important step is the appropriate neural model design. If the model is optimal correlated with the data processed, the results obtained can be more significant than those coming from the mathematical formulas. The main neural models parameters that must be taken into account for an optimal design are model structure, transfer function and training algorithm. This paper presents an investigation of the results obtained with different models, proving that for a specific dataset a specific neural model offers the best results. Several models are analyzed, for a dataset corresponding to specific point in Black Sea and a comparison of results is presented.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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