Comparative study of nonparametric and parametric PV models to forecast AC power output of PV plants

Almeida, Marcelo Pinho and Muñoz, Mikel and Parra, Iñigo de la and Perpiñan Lamigueiro, Oscar and Narvarte Fernández, Luis (2015). Comparative study of nonparametric and parametric PV models to forecast AC power output of PV plants. In: "31st European Photovoltaic Solar Energy Conference and Exhibition: EUPVSEC 2015", 14/09/2015-18/09/2015, Hamburg, Germany. ISBN 3-936338-39-6. pp. 2230-2234.

Description

Title: Comparative study of nonparametric and parametric PV models to forecast AC power output of PV plants
Author/s:
  • Almeida, Marcelo Pinho
  • Muñoz, Mikel
  • Parra, Iñigo de la
  • Perpiñan Lamigueiro, Oscar
  • Narvarte Fernández, Luis
Item Type: Presentation at Congress or Conference (Poster)
Event Title: 31st European Photovoltaic Solar Energy Conference and Exhibition: EUPVSEC 2015
Event Dates: 14/09/2015-18/09/2015
Event Location: Hamburg, Germany
Title of Book: EU PVSEC Proceedings
Date: September 2015
ISBN: 3-936338-39-6
Subjects:
Freetext Keywords: PV output power forecast; Numerical weather prediction; Parametric PV model; Nonparametric PV model
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

In this paper, a comparison between two approaches to predict the AC power output of PV systems is carried out in terms of forecast performance. Each approach uses one of the two main types of PV modeling, parametric and nonparametric, and both use as inputs several forecasts of meteorological variables from a Numerical Weather Prediction model. Furthermore, actual AC power measurements of a PV plant are used to train the nonparametric model, to adjust the parameters of the different PV components models used in the parametric approach and to assess the quality of the forecasts. The approaches presented similar behavior, although the nonparametric approach, based on Quantile Regression Forests, showed smaller biased errors due to the machine learning tool used.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7308468PVCROPSUniversidad Politécnica de MadridPhotoVoltaic Cost reduction, Reliability, Operational performance, Prediction and Simulation

More information

Item ID: 42462
DC Identifier: http://oa.upm.es/42462/
OAI Identifier: oai:oa.upm.es:42462
Official URL: http://www.photovoltaic-conference.com/
Deposited by: Memoria Investigacion
Deposited on: 08 Jul 2016 08:12
Last Modified: 19 May 2017 12:32
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