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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/8249

    Título
    A Hybrid Intelligent Modeling approach for predicting the solar thermal panel energy production
    Autor
    Arroyo Puente, ÁngelUBU authority Orcid
    Basurto Hornillos, NuñoUBU authority Orcid
    Casado Vara, Roberto CarlosUBU authority Orcid
    Timiraos, Míriam
    Calvo-Rolle, José Luis
    Publicado en
    Neurocomputing. 2024, V. 565, 126997
    Editorial
    Elsevier
    Fecha de publicación
    2024-01
    ISSN
    0925-2312
    DOI
    10.1016/j.neucom.2023.126997
    Abstract
    There is no doubt that the European Union is undergoing an ecological transition, with renewable energies accounting for an increasing share of energy consumption in the Member States. In Spain, solar energy is one of these rapidly expanding renewable sources. This study analyzes the solar energy production of a panel in the Spanish region of Galicia. It has been demonstrated that the solar energy produced by this panel can be predicted using a hybrid stepwise system. The missing value imputation is a key step in the process. This involves combining regression and clustering techniques on different subdivisions of the complete dataset, starting with a smaller and less complete dataset and performing appropriate imputations to create a larger and more complete collection. Finally, the dataset is divided into more relevant subsets for regression analysis to calculate the amount of solar energy generated. The imputing missing values using an Artificial Neural Network resulted in a more valid dataset for further processing than eliminating rows with corrupted or empty values. Also, properly applying clustering techniques gives better results than working on the whole dataset.
    Palabras clave
    Regression
    Neural network
    Solar Energy
    Renewable energy
    Clustering
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/8249
    Versión del editor
    https://doi.org/10.1016/j.neucom.2023.126997
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