Wind power probabilistic forecast in the reproducing Kernel Hilbert space

Gallego Castillo, Cristobal Jose and Cuerva Tejero, Alvaro and Bessa, Ricardo J. and Cavalcante, Laura (2016). Wind power probabilistic forecast in the reproducing Kernel Hilbert space. In: "Power Systems Computation Conference", 20-24 Jun 2016, Genoa. ISBN 978-88-941051-0-0. https://doi.org/10.1109/PSCC.2016.7540830.

Description

Title: Wind power probabilistic forecast in the reproducing Kernel Hilbert space
Author/s:
  • Gallego Castillo, Cristobal Jose
  • Cuerva Tejero, Alvaro
  • Bessa, Ricardo J.
  • Cavalcante, Laura
Item Type: Presentation at Congress or Conference (Article)
Event Title: Power Systems Computation Conference
Event Dates: 20-24 Jun 2016
Event Location: Genoa
Title of Book: Power Systems Computation Conference (PSCC) 2016
Título de Revista/Publicación: 2016 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC)
Date: June 2016
ISBN: 978-88-941051-0-0
Subjects:
Freetext Keywords: On-line, probabilistic forecast, quantile regression, Reproducing Kernel Hilbert Space (RKHS), wind power
Faculty: E.T.S.I. Aeronáuticos (UPM)
Department: Aeronaves y Vehículos Espaciales
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Wind power probabilistic forecast is a key input in decision-making problems under risk, such as stochastic unit commitment, operating reserve setting and electricity market bidding. While the majority of the probabilistic forecasting methods are based on quantile regression, the associated limitations call for new approaches. This paper described a new quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. In particular, two versions of the model, off-line and on-line, were implemented and tested for a real wind farm. Results showed the superiority of the on-line approach in terms of performance, robustness and computational cost. Additionally, it was observed that, in the presence of correlated data, the optimal on-line learning may cause unreliable modelling. Potential solutions to this effect are also described and implemented in the paper.

More information

Item ID: 48085
DC Identifier: http://oa.upm.es/48085/
OAI Identifier: oai:oa.upm.es:48085
DOI: 10.1109/PSCC.2016.7540830
Official URL: https://www.pscc-central.org/en/background/papers-from-previous-psccs/pscc-2016-genoa.html
Deposited by: Memoria Investigacion
Deposited on: 23 Jan 2018 11:14
Last Modified: 23 Jan 2018 11:14
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