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Title: Short-term load and wind power forecasting using neural network-based prediction intervals
Authors: Quan, H.
Srinivasan, D. 
Khosravi, A.
Keywords: Load forecasting
neural network (NN)
particle swarm optimization (PSO)
prediction interval (PI)
wind power
Issue Date: Feb-2014
Citation: Quan, H., Srinivasan, D., Khosravi, A. (2014-02). Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE Transactions on Neural Networks and Learning Systems 25 (2) : 303-315. ScholarBank@NUS Repository.
Abstract: Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time. © 2013 IEEE.
Source Title: IEEE Transactions on Neural Networks and Learning Systems
ISSN: 2162237X
DOI: 10.1109/TNNLS.2013.2276053
Appears in Collections:Staff Publications

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