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Improving the quality of prediction intervals through optimal aggregation

Hosen, Mohammad Anwar, Khosravi, Abbas, Nahavandi, Saeid and Creighton, Douglas 2015, Improving the quality of prediction intervals through optimal aggregation, IEEE Transactions on industrial electronics, vol. 62, no. 7, pp. 4420-4429, doi: 10.1109/TIE.2014.2383994.

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Title Improving the quality of prediction intervals through optimal aggregation
Author(s) Hosen, Mohammad AnwarORCID iD for Hosen, Mohammad Anwar orcid.org/0000-0001-8282-3198
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Journal name IEEE Transactions on industrial electronics
Volume number 62
Issue number 7
Start page 4420
End page 4429
Total pages 10
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J
Publication date 2015-07
ISSN 0278-0046
Keyword(s) Aggregation
Neural Network
Prediction interval
Simulated annealing
Uncertainty and disturbance
Weighted average
Science & Technology
Technology
Automation & Control Systems
Engineering, Electrical & Electronic
Instruments & Instrumentation
Engineering
neural network (NN)
prediction interval (PI)
simulated annealing (SA)
NEURAL-NETWORK ENSEMBLES
FORECAST COMBINATION
BATCH REACTOR
POLYMERIZATION
MODEL
OPTIMIZATION
PERFORMANCE
ALGORITHM
CONTROLLERS
Summary Neural networks (NNs) are an effective tool to model nonlinear systems. However, their forecasting performance significantly drops in the presence of process uncertainties and disturbances. NN-based prediction intervals (PIs) offer an alternative solution to appropriately quantify uncertainties and disturbances associated with point forecasts. In this paper, an NN ensemble procedure is proposed to construct quality PIs. A recently developed lower-upper bound estimation method is applied to develop NN-based PIs. Then, constructed PIs from the NN ensemble members are combined using a weighted averaging mechanism. Simulated annealing and a genetic algorithm are used to optimally adjust the weights for the aggregation mechanism. The proposed method is examined for three different case studies. Simulation results reveal that the proposed method improves the average PI quality of individual NNs by 22%, 18%, and 78% for the first, second, and third case studies, respectively. The simulation study also demonstrates that a 3%-4% improvement in the quality of PIs can be achieved using the proposed method compared to the simple averaging aggregation method.
Language eng
DOI 10.1109/TIE.2014.2383994
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076649

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