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A new fuzzy-based combined prediction interval for wind power forecasting

Kavousi-Fard, Abdollah, Khosravi, Abbas and Nahavandi, Saeid 2016, A new fuzzy-based combined prediction interval for wind power forecasting, IEEE transactions on power systems, vol. 31, no. 1, pp. 18-26, doi: 10.1109/TPWRS.2015.2393880.

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Title A new fuzzy-based combined prediction interval for wind power forecasting
Author(s) Kavousi-Fard, Abdollah
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, Saeid
Journal name IEEE transactions on power systems
Volume number 31
Issue number 1
Start page 18
End page 26
Total pages 9
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016-01
ISSN 0885-8950
Keyword(s) Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
Combined lower upper bound estimation (LUBE)
interactive fuzzy satisfying method
uncertainty
wind power forecast error
ARTIFICIAL NEURAL-NETWORKS
DISTRIBUTION FEEDER RECONFIGURATION
PROBABILISTIC FORECASTS
QUANTILE REGRESSION
GENERATION
ALGORITHM
SYSTEM
MODEL
SPEED
Summary This paper makes use of the idea of prediction intervals (PIs) to capture the uncertainty associated with wind power generation in power systems. Since the forecasting errors cannot be appropriately modeled using distribution probability functions, here we employ a powerful nonparametric approach called lower upper bound estimation (LUBE) method to construct the PIs. The proposed LUBE method uses a new framework based on a combination of PIs to overcome the performance instability of neural networks (NNs) used in the LUBE method. Also, a new fuzzy-based cost function is proposed with the purpose of having more freedom and flexibility in adjusting NN parameters used for construction of PIs. In comparison with the other cost functions in the literature, this new formulation allows the decision-makers to apply their preferences for satisfying the PI coverage probability and PI normalized average width individually. As the optimization tool, bat algorithm with a new modification is introduced to solve the problem. The feasibility and satisfying performance of the proposed method are examined using datasets taken from different wind farms in Australia.
Language eng
DOI 10.1109/TPWRS.2015.2393880
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
0906 Electrical And Electronic Engineering
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083077

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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