Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

Quan,H, Srinivasan,D and Khosravi,A 2014, Uncertainty handling using neural network-based prediction intervals for electrical load forecasting, Energy, vol. 73, pp. 916-925, doi: 10.1016/j.energy.2014.06.104.

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Title Uncertainty handling using neural network-based prediction intervals for electrical load forecasting
Author(s) Quan,H
Khosravi,AORCID iD for Khosravi,A orcid.org/0000-0001-6927-0744
Journal name Energy
Volume number 73
Start page 916
End page 925
Publisher Elsevier Ltd
Place of publication London, England
Publication date 2014-08-14
ISSN 0360-5442
Keyword(s) Load forecasting
Neural network
Particle swarm optimization
Prediction interval
Science & Technology
Physical Sciences
Energy & Fuels
Summary The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.
Language eng
DOI 10.1016/j.energy.2014.06.104
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850699 Energy Storage
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069965

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