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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7273

    Título
    Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain
    Autor
    González Peña, DavidAutoridad UBU Orcid
    García Ruiz, Ignacio
    Diez Mediavilla, MontserratAutoridad UBU Orcid
    Dieste Velasco, Mª IsabelAutoridad UBU Orcid
    Alonso Tristán, CristinaAutoridad UBU Orcid
    Publicado en
    Applied sciences. 2021, V. 11, n. 11, 5025
    Editorial
    MDPI
    Fecha de publicación
    2021-05
    DOI
    10.3390/app11115025
    Resumen
    Prediction of energy production is crucial for the design and installation of PV plants. In this study, five free and commercial software tools to predict photovoltaic energy production are evaluated: RETScreen, Solar Advisor Model (SAM), PVGIS, PVSyst, and PV*SOL. The evaluation involves a comparison of monthly and annually predicted data on energy supplied to the national grid with real field data collected from three real PV plants. All the systems, located in Castile and Leon (Spain), have three different tilting systems: fixed mounting, horizontal-axis tracking, and dual-axis tracking. The last 12 years of operating data, from 2008 to 2020, are used in the evaluation. Although the commercial software tools were easier to use and their installations could be described in detail, their results were not appreciably superior. In annual global terms, the results hid poor estimations throughout the year, where overestimations were compensated by underestimated results. This fact was reflected in the monthly results: the software yielded overestimates during the colder months, while the models showed better estimates during the warmer months. In most studies, the deviation was below 10% when the annual results were analyzed. The accuracy of the software was also reduced when the complexity of the dual-axis solar tracking systems replaced the fixed installation.
    Palabras clave
    Photovoltaic
    RETScreen
    SAM
    PVGIS
    PVsyst
    PV*SOL
    Energy prediction
    Materia
    Electrotecnia
    Electrical engineering
    URI
    http://hdl.handle.net/10259/7273
    Versión del editor
    https://doi.org/10.3390/app11115025
    Aparece en las colecciones
    • Artículos SWIFT
    Atribución 4.0 Internacional
    Documento(s) sujeto(s) a una licencia Creative Commons Atribución 4.0 Internacional
    Ficheros en este ítem
    Nombre:
    González-as_2022.pdf
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