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Energies2018,11, 1561 25. VanHinsbergen,C.P.I.; vanLint, J.W.C.;vanZuylen,H.J.Bayesiancommitteeofneuralnetworks topredict travel timeswithconfidence intervals.Transp. Res. PartCEmerg. Technol. 2009,17, 498–509. [CrossRef] 26. Khosravi,A.;Nahavandi,S.;Creighton,D.Constructionofoptimalprediction intervals for loadforecasting problems. IEEETrans. PowerSyst. 2010,25, 1496–1503. [CrossRef] 27. DaSilva,A.P.A.;Moulin,L.S.Confidence intervals forneuralnetworkbasedshort-termloadforecasting. IEEETrans. PowerSyst. 2000,15, 1191–1196. [CrossRef] 28. Khosravi, A.; Nahavandi, S.; Creighton, D.; Atiya, A.F. Lower upper bound estimation method for construction of neural network-basedprediction intervals. IEEETrans. NeuralNetw. 2011, 22, 337–346. [CrossRef] [PubMed] 29. Deb,K.;Agrawal,S.;Pratap,A.;Meyarivan,T.Afastelitistnon-dominatedsortinggeneticalgorithmfor multi-objectiveoptimization:NSGA-II.ParallelProbl. SolvingNat. PPSNVI2000, 849–858. [CrossRef] 30. Deb,K.;Pratap,A.;Agarwal,S.;Meyarivan,T.Afastandelitistmultiobjectivegeneticalgorithm:NSGA-II. IEEETrans. Evol. Comput. 2002,6, 182–197. [CrossRef] 31. CoelloCoello,C.A.;Lechuga,M.S.MOPSO:Aproposal formultipleobjectiveparticle swarmoptimization. In Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, Honolulu, HI, USA, 12–17May2002;Volume2,pp.1051–1056. 32. Padhye, N. Topology Optimization of Compliant Mechanism Using Multi-objective Particle Swarm Optimization. InProceedingsof the 10thAnnualConferenceCompaniononGenetic andEvolutionary Computation,Atlanta,GA,USA,12–16 July2008;pp.1831–1834. 33. Alaya, I.; Solnon,C.; Ghedira,K.AntColonyOptimization forMulti-ObjectiveOptimizationProblems. InProceedingsof the19th IEEEInternationalConferenceonToolswithArtificial Intelligence (ICTAI2007), Patras,Greece,29–31October2007;pp.450–457. [CrossRef] 34. Xue,F.; Sanderson,A.C.;Graves,R.J.Pareto-basedmulti-objectivedifferential evolution. InProceedings of the2003CongressonEvolutionaryComputation,Canberra,Australia,8–12December2003;Volume2, pp.862–869. 35. Mirjalili, S.Z.; Mirjalili, S.; Saremi, S.; Faris, H.; Aljarah, I. Grasshopper optimization algorithm for multi-objectiveoptimizationproblems.Appl. Intell. 2017. [CrossRef] 36. Knowles, J.D.;Corne,D.W.ApproximatingtheNondominatedFrontUsingtheParetoArchivedEvolution Strategy.Evol. Comput. 2000,8, 149–172. [CrossRef] [PubMed] 37. Wang, J.; Yang,W.; Du, P.; Niu, T.Anovel hybrid forecasting systemofwind speedbasedon anewly developedmulti-objectivesinecosinealgorithm.EnergyConvers.Manag. 2018,163, 134–150. [CrossRef] 38. Du,P.;Wang, J.;Guo,Z.; Yang,W.Researchandapplicationof anovelhybrid forecasting systembased onmulti-objective optimization forwind speed forecasting. EnergyConvers. Manag. 2017, 150, 90–107. [CrossRef] 39. Wolpert,D.H.;Macready,W.G.Nofree lunchtheoremsforoptimization. IEEETrans. Evol. Comput. 1997, 1, 67–82. [CrossRef] 40. Service,T.C.ANoFreeLunchtheoremformulti-objectiveoptimization. Inf. Process. Lett. 2010,110, 917–923. [CrossRef] 41. Rodriguez,P.;Wiles, J.; Elman, J.L.ARecurrentNeuralNetwork thatLearns toCount. Conn. Sci. 1999, 11, 5–40. [CrossRef] 42. Chandra,R.;Zhang,M.CooperativecoevolutionofElmanrecurrentneuralnetworks forchaotic timeseries prediction.Neurocomputing2012,86, 116–123. [CrossRef] 43. Cacciola,M.;Megali,G.;Pellicanó,D.;Morabito,F.C.Elmanneuralnetworks forcharacterizingvoids in weldedstrips:Astudy.NeuralComput.Appl. 2012,21, 869–875. [CrossRef] 44. Wang,J.J.;Zhang,W.;Li,Y.;Wang,J.J.;Dang,Z.Forecastingwindspeedusingempiricalmodedecomposition andElmanneuralnetwork.Appl. SoftComput. 2014,23, 452–459. [CrossRef] 45. Huang,N.;Shen,Z.;Long,S.;Wu,M.;Shih,H.;Zheng,Q.;Yen,N.;Tung,C.;Liu,H.Theempiricalmode decompositionandtheHilbert spectrumfornonlinearandnon-stationary timeseriesanalysis.Proc. R.Soc. AMath. Phys. Eng. Sci. 1998,454, 903–995. [CrossRef] 46. Wu,Z.;Huang,N.E.EnsembleEmpiricalModeDecomposition:ANoise-AssistedDataAnalysisMethod. Adv.Adapt.DataAnal. 2009,1, 1–41. [CrossRef] 47. Yeh, J.R.; Shieh, J.S.;Huang,N.E.Complementaryensembleempiricalmodedecomposition:Anovelnoise enhanceddataanalysismethod.Adv.Adapt.DataAnal. 2010,2, 135–156. [CrossRef] 316
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Short-Term Load Forecasting by Artificial Intelligent Technologies
Title
Short-Term Load Forecasting by Artificial Intelligent Technologies
Authors
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Editor
MDPI
Location
Basel
Date
2019
Language
English
License
CC BY 4.0
ISBN
978-3-03897-583-0
Size
17.0 x 24.4 cm
Pages
448
Keywords
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
Category
Informatik
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Short-Term Load Forecasting by Artificial Intelligent Technologies