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. Attached Files Name Description MIMEType Size Downloads Title A new fuzzy-based combined prediction interval for wind power forecasting Author(s) Kavousi-Fard, Abdollah 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 Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 27 Apr 2016, 08:44:43 EST Wed, 27 Apr 2016, 08:45:29 EST Thu, 28 Apr 2016, 05:00:25 EST Thu, 28 Apr 2016, 06:10:22 EST Fri, 20 May 2016, 14:27:52 EST Wed, 09 Nov 2016, 11:41:35 EST Filtered Full Citation counts:   Cited 16 times in TR Web of Science Cited 25 times in Scopus Search Google Scholar Access Statistics: 182 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Wed, 27 Apr 2016, 08:45:29 EST

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