On April 30th the paper “Forecasting of wave energy in Canary Islands based on Artificial Intelligence” was accepted for publication in the Journal “Applied Ocean Research” (Elsevier), developed by the INGEMAR research group, belonging to the University of La Laguna, ULL.
The paper describes that a key element in the development of renewable energies based on waves is the prediction of such energy resource.
In this sense, island regions will be very interested in taking advantage of this resource, especially if the characteristics of the wave resources are convenient for the production of a considerable amount of energy. This is the case of the Canary and other islands or archipelagos where these resources are a promising source of energy. Currently, there is not much research based on soft computing techniques for wave height prediction or wave energy forecasting in the North Atlantic Ocean, especially in the Macaronesian region. For this reason, the main objective of this research is to determine the behaviour of the intelligent systems Fuzzy Inference System (FIS) and Artificial Neural Network (ANN) for wave energy prediction, in order to demonstrate the advantages entailed by the use of soft computing methods rather than numerical models. As a starting point, the research was done taking wave data buoys belonging to Puertos del Estado (Spain’s State Ports), located in deep waters near the Canary Islands.
This document can be considered as the first stage of research for the application of soft computing in regions with wave energy conditions similar to the Canarian Archipelago. The renewable energy produced can be injected directly into the electrical network or can be used in isolated desalination systems.
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.
This research has been co-funded by FEDER funds, INTERREG MAC 2014-2020 Programme of the European Union, within the DESAL+ project (MAC/1.1a/094) and the E5DES project (MAC2/1.1a/309).
More information: https://www.sciencedirect.com/science/article/pii/S0141118719306522
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