A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast

Kavousi-Fard,A, Khosravi,A and Nahavadi,S 2014, A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast, in IJCNN 2014 : Proceedings of the 2014 International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 1015-1019, doi: 10.1109/IJCNN.2014.6889459.

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Title A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast
Author(s) Kavousi-Fard,A
Khosravi,AORCID iD for Khosravi,A orcid.org/0000-0001-6927-0744
Nahavadi,SORCID iD for Nahavadi,S orcid.org/0000-0002-0360-5270
Conference name International Joint Conference on Neural Networks (2014 : Beijing, China)
Conference location Beijing, China
Conference dates 6-11 July 2014
Title of proceedings IJCNN 2014 : Proceedings of the 2014 International Joint Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2014
Conference series International Joint Conference on Neural Networks
Start page 1015
End page 1019
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) combined LUBE
interactive fuzzy satisfying method
uncertainty
wind power forecast
Summary The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.
ISBN 9781479914845
Language eng
DOI 10.1109/IJCNN.2014.6889459
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850509 Wind Energy
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070565

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
2018 ERA Submission
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