Please use this identifier to cite or link to this item: https://doi.org/10.1109/CIASG.2013.6611500
Title: Construction of neural network-based prediction intervals for short-term electrical load forecasting
Authors: Quan, H.
Srinivasan, D. 
Khosravi, A.
Nahavandi, S.
Creighton, D.
Issue Date: 2013
Source: Quan, H.,Srinivasan, D.,Khosravi, A.,Nahavandi, S.,Creighton, D. (2013). Construction of neural network-based prediction intervals for short-term electrical load forecasting. IEEE Symposium on Computational Intelligence Applications in Smart Grid, CIASG : 66-72. ScholarBank@NUS Repository. https://doi.org/10.1109/CIASG.2013.6611500
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. © 2013 IEEE.
Source Title: IEEE Symposium on Computational Intelligence Applications in Smart Grid, CIASG
URI: http://scholarbank.nus.edu.sg/handle/10635/69708
ISBN: 9781467360029
ISSN: 23267682
DOI: 10.1109/CIASG.2013.6611500
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