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

Quan, Hao, Srinivasan, Dipti, Khosravi, Abbas, Nahavandi, Saeid and Creighton, Doug 2013, Construction of neural network-based prediction intervals for short-term electrical load forecasting, in CIASG 2013 : Proceedings of the 2013 IEEE Computational Intelligence Applications in Smart Grid, IEEE, Piscataway, N. J., pp. 66-72.

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Title Construction of neural network-based prediction intervals for short-term electrical load forecasting
Author(s) Quan, Hao
Srinivasan, Dipti
Khosravi, Abbas
Nahavandi, Saeid
Creighton, Doug
Conference name IEEE Computational Intelligence Applications in Smart Grid (2013 : Singapore)
Conference location Singapore
Conference dates 16-19 Apr. 2013
Title of proceedings CIASG 2013 : Proceedings of the 2013 IEEE Computational Intelligence Applications in Smart Grid
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE Symposium on Computational Intelligence series
Start page 66
End page 72
Total pages 7
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) electrical power system
neural network
prediction interval
short term load forecasting
short-term electrical loads
wind and solar power
Summary 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.
ISBN 1467360023
9781467360029
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850699 Energy Storage, Distribution and Supply not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30062968

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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