Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux

Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux

Meteorological data are extensively used to perform environmental learning. Soft Computing (SC) and Machine Learning (ML) techniques represent a valuable support in many research areas, but require datasets containing information related to the topic under study. Such datasets are not always availab...

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Bibliographic Details
Main Authors: Antonio Manuel Gómez-Orellana, Juan Carlos Fernández, Manuel Dorado-Moreno, Pedro Antonio Gutiérrez, César Hervás-Martínez
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Energies
Subjects:
environmental prediction
renewable energy resource evaluation
meteorological data
reanalysis data
marine energy
soft computing
Online Access:https://www.mdpi.com/1996-1073/14/2/468

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https://www.mdpi.com/1996-1073/14/2/468

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