Combined nonparametric prediction intervals for wind power generation

Khosravi, Abbas and Nahavandi, Saeid 2013, Combined nonparametric prediction intervals for wind power generation, IEEE transactions on sustainable energy, vol. 4, no. 4, pp. 849-856, doi: 10.1109/TSTE.2013.2253140.

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Title Combined nonparametric prediction intervals for wind power generation
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Journal name IEEE transactions on sustainable energy
Volume number 4
Issue number 4
Start page 849
End page 856
Total pages 8
Publisher IEEE
Place of publication Piscataway, New Jersey
Publication date 2013-10
ISSN 1949-3029
Keyword(s) lower upper bound estimation
neural networks (NNs)
prediction interval (Lis)
wind power
Summary Prediction intervals (PIs) are a promising tool for quantification of uncertainties associated with point forecasts of wind power. However, construction of PIs using parametric methods is questionable, as forecast errors do not follow a standard distribution. This paper proposes a nonparametric method for construction of reliable PIs for neural network (NN) forecasts. A lower upper bound estimation (LUBE) method is adapted for construction of PIs for wind power generation. A new framework is proposed for synthesizing PIs generated using an ensemble of NN models in the LUBE method. This is done to guard against NN performance instability in generating reliable and informative PIs. A validation set is applied for short listing NNs based on the quality of PIs. Then, PIs constructed using filtered NNs are aggregated to obtain combined PIs. Performance of the proposed method is examined using data sets taken from two wind farms in Australia. Simulation results indicate that the quality of combined PIs is significantly superior to the quality of PIs constructed using NN models ranked and filtered by the validation set.
Language eng
DOI 10.1109/TSTE.2013.2253140
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850509 Wind Energy
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, Elsevier
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