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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/62970

    Título
    Optimized estimator of the output power of PV cells using EL images and I–V curves
    Autor
    Mateo Romero, Héctor FelipeAutoridad UVA Orcid
    Hernández Callejo, LuisAutoridad UVA Orcid
    González Rebollo, Miguel ÁngelAutoridad UVA
    Cardeñoso Payo, ValentínAutoridad UVA Orcid
    Alonso Gómez, VíctorAutoridad UVA Orcid
    Morales Aragones, José IgnacioAutoridad UVA Orcid
    Moyo, Ranganai Tawanda
    Año del Documento
    2023
    Editorial
    Elsevier
    Descripción
    Producción Científica
    Documento Fuente
    Solar Energy, 2023, vol. 265, 112089
    Résumé
    In this work, a method to predict the output power of Photovoltaic (PV) cells using their Electroluminescence (EL) images is presented. The data used includes Electroluminescence Images and the value of the Max Power Point computed from the Current–Voltage Curve of the cells. The method is used as follows: Firstly, the images are preprocessed to improve their quality. After that, a comparison between different Machine Learning methods from Traditional ones, such as Random Forest or Gradient Boosting, to Deep Learning methods, such as Recurrent Neural Networks or Convolutional Neural Networks is performed. Another significant contribution of this paper is that it analyzes the problem of unbalanced data, trying to solve it using Synthetic Images created by a Generative Adversarial Network. Our results show that the best model is the Gradient-Boosting based method using a pre-trained Resnet50 as a feature extraction method with a Mean Absolute Error (MAE) of 0.0341 and a Mean Squared Error (MSE) of 0.00211. The results also shows how the models trained with the unbalanced dataset are capable of obtaining results similar to the models trained with the balanced dataset.
    Materias (normalizadas)
    Renewable and Green Energy
    PV systems
    Materias Unesco
    3308 Ingeniería y Tecnología del Medio Ambiente
    Palabras Clave
    Electroluminescence image
    Gradient Boosting
    Recurrent Neural Network
    Imagen de electroluminiscencia
    Aumento de gradiente
    Red neuronal recurrente
    ISSN
    0038-092X
    Revisión por pares
    SI
    DOI
    10.1016/j.solener.2023.112089
    Patrocinador
    Ministerio de Ciencia e Innovación de España (PID2020-113533RB-C33)
    Version del Editor
    https://www.sciencedirect.com/science/article/pii/S0038092X23007235?via%3Dihub
    Propietario de los Derechos
    © 2023 The Authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/62970
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP42 - Artículos de revista [292]
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    Nombre:
    Optimized-estimator-of-the-output.pdf
    Tamaño:
    3.642Mo
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    Attribution-NonCommercial-NoDerivatives 4.0 InternacionalExcepté là où spécifié autrement, la license de ce document est décrite en tant que Attribution-NonCommercial-NoDerivatives 4.0 Internacional

    Universidad de Valladolid

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