NDLI: Construction of neural network-based prediction intervals for short-term electrical load forecasting

Content Provider	IEEE Xplore Digital Library
Author	Hao Quan Srinivasan, D. Khosravi, A. Nahavandi, S. Creighton, D.
Copyright Year	2013
Description	Author affiliation: Centre for Intell. Syst. Res., Deakin Univ., Geelong, VIC, Australia (Khosravi, A.; Nahavandi, S.; Creighton, D.) \|\| Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore (Hao Quan; Srinivasan, D.)
Abstract	Short-term load forecasting (STLF) is of great importance for control and scheduling of electrical power systems. The uncertainty of power systems increases due to the random nature of climate and the penetration of the renewable energies such as wind and solar power. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in datasets. To quantify these potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for construction of prediction intervals (PIs). A newly proposed method, called lower upper bound estimation (LUBE), is applied to develop PIs using NN models. The primary multi-objective problem is firstly transformed into a constrained single-objective problem. This new problem formulation is closer to the original problem and has fewer parameters than the cost function. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Two case studies from Singapore and New South Wales (Australia) historical load datasets are used to validate the PSO-based LUBE method. Demonstrated results show that the proposed method can construct high quality PIs for load forecasting applications.
Starting Page	66
Ending Page	72
File Size	455488
Page Count	7
File Format	PDF
ISBN	9781467360029
ISSN	23267690
DOI	10.1109/CIASG.2013.6611500
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Upper bound Load forecasting Biological system modeling Time series analysis Artificial neural networks Cost function
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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