Comparative study of PV power forecast using parametric and nonparametric PV models

Almeida, Marcelo Pinho, Muñoz, Mikel, Parra, Iñigo de la and Perpiñan Lamigueiro, Oscar ORCID: https://orcid.org/0000-0002-4134-7196 (2017). Comparative study of PV power forecast using parametric and nonparametric PV models. "Solar Energy", v. 155 ; pp. 854-866. ISSN 0038-092X. https://doi.org/10.1016/j.solener.2017.07.032.

Descripción

Título: Comparative study of PV power forecast using parametric and nonparametric PV models
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: Solar Energy
Fecha: Octubre 2017
ISSN: 0038-092X
Volumen: 155
Materias:
ODS:
Palabras Clave Informales: PV plant; PV power forecast; Quantile Regression Forests; PV system modeling
Escuela: E.T.S.I. Diseño Industrial (UPM)
Departamento: Ingeniería Eléctrica, Electrónica Automática y Física Aplicada
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Forecast procedures for large ground mounted PV plants or smaller BIPV or BAPV systems may use a parametric or a nonparametric model of the PV system. In this paper, both approaches are used independently to calculate the energy delivered to the grid on an hourly basis in forecast procedures that use meteorological variables from a Numerical Weather Prediction model as inputs, and their performances against real generation data from six PV plants are analyzed. The parametric approach relies on mathematical models with several parameters that describe the PV systems and it was implemented in MATLAB whereas the nonparametric approach is based on Quantile Regression Forests with training and forecast stages and its code was built in R. The parametric approach presented more significant bias on its results, mostly due to the input data and the transposition model of irradiance from a horizontal surface to the plane of the PV array.

Proyectos asociados

Tipo
Código
Acrónimo
Responsable
Título
FP7
308468
PVCROPS
UNIVERSIDAD POLITECNICA DE MADRID
PhotoVoltaic Cost reduction, Reliability, Operational performance, Prediction and Simulation

Más información

ID de Registro: 49898
Identificador DC: https://oa.upm.es/49898/
Identificador OAI: oai:oa.upm.es:49898
URL Portal Científico: https://portalcientifico.upm.es/es/ipublic/item/5495785
Identificador DOI: 10.1016/j.solener.2017.07.032
URL Oficial: https://www.sciencedirect.com/science/article/pii/...
Depositado por: Memoria Investigacion
Depositado el: 02 Abr 2018 09:07
Ultima Modificación: 12 Nov 2025 00:00