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Article

Optimizing Wave Overtopping Energy Converters by ANN Modelling: Evaluating the Overtopping Rate Forecasting as the First Step

1
CIOPU SL, 12004 Castelló de la Plana, Spain
2
Grupo de Investigación de Medio Marino, Costero y Portuario, y Otras Áreas Sensibles, Universidad Politécnica de Madrid, 28040 Madrid, Spain
3
Laboratorio Nacional de Engenharia Civil, 1700-066 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Gregorio Iglesias Rodriguez
Sustainability 2021, 13(3), 1483; https://doi.org/10.3390/su13031483
Received: 7 November 2020 / Revised: 19 January 2021 / Accepted: 21 January 2021 / Published: 1 February 2021
(This article belongs to the Special Issue Renewable Energies for Sustainable Development)
Artificial neural networks (ANN) are extremely powerful analytical, parallel processing elements that can successfully approximate any complex non-linear process, and which form a key piece in Artificial Intelligence models. Its field of application, being very wide, is especially suitable for the field of prediction. In this article, its application for the prediction of the overtopping rate is presented, as part of a strategy for the sustainable optimization of coastal or harbor defense structures and their conversion into Waves Energy Converters (WEC). This would allow, among others benefits, reducing their initial high capital expenditure. For the construction of the predictive model, classical multivariate statistical techniques such as Principal Component Analysis (PCA), or unsupervised clustering methods like Self Organized Maps (SOM), are used, demonstrating that this close alliance is always methodologically beneficial. The specific application carried out, based on the data provided by the CLASH and EurOtop 2018 databases, involves the creation of a useful application to predict overtopping rates in both sloping breakwaters and seawalls, with good results both in terms of prediction error, such as correlation of the estimated variable. View Full-Text
Keywords: artificial neural network; principal component analysis; wave energy converters; wave overtopping rate artificial neural network; principal component analysis; wave energy converters; wave overtopping rate
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MDPI and ACS Style

Oliver, J.M.; Esteban, M.D.; López-Gutiérrez, J.-S.; Negro, V.; Neves, M.G. Optimizing Wave Overtopping Energy Converters by ANN Modelling: Evaluating the Overtopping Rate Forecasting as the First Step. Sustainability 2021, 13, 1483. https://doi.org/10.3390/su13031483

AMA Style

Oliver JM, Esteban MD, López-Gutiérrez J-S, Negro V, Neves MG. Optimizing Wave Overtopping Energy Converters by ANN Modelling: Evaluating the Overtopping Rate Forecasting as the First Step. Sustainability. 2021; 13(3):1483. https://doi.org/10.3390/su13031483

Chicago/Turabian Style

Oliver, José M., Maria D. Esteban, José-Santos López-Gutiérrez, Vicente Negro, and Maria G. Neves 2021. "Optimizing Wave Overtopping Energy Converters by ANN Modelling: Evaluating the Overtopping Rate Forecasting as the First Step" Sustainability 13, no. 3: 1483. https://doi.org/10.3390/su13031483

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