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Combined nonparametric prediction intervals for wind power generation
journal contribution
posted on 2013-10-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid NahavandiPrediction 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.
History
Journal
IEEE transactions on sustainable energyVolume
4Issue
4Pagination
849 - 856Publisher
IEEELocation
Piscataway, New JerseyPublisher DOI
ISSN
1949-3029eISSN
1949-3037Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2013, ElsevierUsage metrics
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No categories selectedKeywords
lower upper bound estimationneural networks (NNs)prediction interval (Lis)wind powerScience & TechnologyTechnologyGreen & Sustainable Science & TechnologyEnergy & FuelsEngineering, Electrical & ElectronicScience & Technology - Other TopicsEngineeringLower upper bound estimation (LUBE)prediction intervals (PIs)NEURAL-NETWORKSPEEDFORECASTSCOMBINATIONACCURACY
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