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

Almeida, Marcelo Pinho and Muñoz, Mikel and Parra, Iñigo de la and Perpiñan Lamigueiro, Oscar (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.

Description

Title: Comparative study of PV power forecast using parametric and nonparametric PV models
Author/s:
  • Almeida, Marcelo Pinho
  • Muñoz, Mikel
  • Parra, Iñigo de la
  • Perpiñan Lamigueiro, Oscar
Item Type: Article
Título de Revista/Publicación: Solar Energy
Date: October 2017
Volume: 155
Subjects:
Freetext Keywords: PV plant; PV power forecast; Quantile Regression Forests; PV system modeling
Faculty: E.T.S.I. Diseño Industrial (UPM)
Department: Ingeniería Eléctrica, Electrónica Automática y Física Aplicada
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7308468PVCROPSUNIVERSIDAD POLITECNICA DE MADRIDPhotoVoltaic Cost reduction, Reliability, Operational performance, Prediction and Simulation

More information

Item ID: 49898
DC Identifier: http://oa.upm.es/49898/
OAI Identifier: oai:oa.upm.es:49898
DOI: 10.1016/j.solener.2017.07.032
Official URL: https://www.sciencedirect.com/science/article/pii/S0038092X17306175
Deposited by: Memoria Investigacion
Deposited on: 02 Apr 2018 09:07
Last Modified: 02 Apr 2018 10:01
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