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Construction of neural network-based prediction intervals for short-term electrical load forecasting
conference contribution
posted on 2013-01-01, 00:00 authored by H Quan, D Srinivasan, Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Douglas CreightonDouglas CreightonShort-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.
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IEEE Computational Intelligence Applications in Smart Grid (2013 : Singapore)Pagination
66 - 72Publisher
IEEELocation
SingaporePlace of publication
Piscataway, N. J.Start date
2013-04-16End date
2013-04-19ISBN-13
9781467360029ISBN-10
1467360023Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
CIASG 2013 : Proceedings of the 2013 IEEE Computational Intelligence Applications in Smart GridUsage metrics
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