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A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast
conference contribution
posted on 2014-09-03, 00:00 authored by A Kavousi-Fard, Abbas KhosraviAbbas Khosravi, S NahavadiThe 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.
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Event
International Joint Conference on Neural Networks (2014 : Beijing, China)Pagination
1015 - 1019Publisher
IEEELocation
Beijing, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-07-06End date
2014-07-11ISBN-13
9781479914845Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
IJCNN 2014 : Proceedings of the 2014 International Joint Conference on Neural NetworksUsage metrics
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