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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 Nahavadi
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.

History

Event

International Joint Conference on Neural Networks (2014 : Beijing, China)

Pagination

1015 - 1019

Publisher

IEEE

Location

Beijing, China

Place of publication

Piscataway, N.J.

Start date

2014-07-06

End date

2014-07-11

ISBN-13

9781479914845

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2014, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

IJCNN 2014 : Proceedings of the 2014 International Joint Conference on Neural Networks