Forecasting of wave energy in Canary Islands based on Artificial Intelligence
| Autor: | Ramón Quiza, G. Nicolás Marichal, Deivis Avila, Ángela Hernández, Isidro Padrón |
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| Rok vydání: | 2020 |
| Předmět: |
Soft computing
Fuzzy inference Mathematical model Meteorology Artificial neural network Intelligent decision support system 020101 civil engineering Ocean Engineering 02 engineering and technology 01 natural sciences 010305 fluids & plasmas 0201 civil engineering Power (physics) 0103 physical sciences Point (geometry) Physics::Atmospheric and Oceanic Physics Geology Energy (signal processing) |
| Zdroj: | Applied Ocean Research. 101:102189 |
| ISSN: | 0141-1187 |
| DOI: | 10.1016/j.apor.2020.102189 |
| Popis: | In this work two mathematical models based on soft computing techniques for the forecasting of the wave energy in the Macaronesian region are exposed. The intelligent systems proposed for the wave energy prediction are Fuzzy Inference Systems (FIS) and Artificial Neural Networks (ANN). The models were implemented and validated thanks to the dataset of deep waters buoys belonging to Spain's State Ports, in several places near the Canary Islands. The buoys dataset covered a total period of 18 years. Once this research finished, it was possible to conclude that there is an excellent correspondence between annual wave energy predicted by ANN- and FIS-based models with respect to both buoys. These models constitute an effective tool to compute the wave power quickly and accurately at any point in oceanic deep waters, which allows for an optimal use of the dataset from the buoys even with only a few months of measurements. |
| Databáze: | OpenAIRE |
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