Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNNLS.2013.2276053
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dc.titleShort-term load and wind power forecasting using neural network-based prediction intervals
dc.contributor.authorQuan, H.
dc.contributor.authorSrinivasan, D.
dc.contributor.authorKhosravi, A.
dc.date.accessioned2014-10-07T04:36:12Z
dc.date.available2014-10-07T04:36:12Z
dc.date.issued2014-02
dc.identifier.citationQuan, 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. https://doi.org/10.1109/TNNLS.2013.2276053
dc.identifier.issn2162237X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/83013
dc.description.abstractElectrical 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TNNLS.2013.2276053
dc.sourceScopus
dc.subjectLoad forecasting
dc.subjectneural network (NN)
dc.subjectparticle swarm optimization (PSO)
dc.subjectprediction interval (PI)
dc.subjectuncertainty
dc.subjectwind power
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TNNLS.2013.2276053
dc.description.sourcetitleIEEE Transactions on Neural Networks and Learning Systems
dc.description.volume25
dc.description.issue2
dc.description.page303-315
dc.identifier.isiut000330040800005
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