Web-Books
im Austria-Forum
Austria-Forum
Web-Books
Informatik
Short-Term Load Forecasting by Artificial Intelligent Technologies
Seite - 334 -
  • Benutzer
  • Version
    • Vollversion
    • Textversion
  • Sprache
    • Deutsch
    • English - Englisch

Seite - 334 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Bild der Seite - 334 -

Bild der Seite - 334 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Text der Seite - 334 -

Energies2018,11, 1449 8. Dai,Q.;Cai,T.;Duan,S.;Zhao,F.StochasticModelingandForecastingofLoadDemandforElectricBus Battery-SwapStation. IEEETrans. PowerDeliv. 2014,29, 1909–1917. [CrossRef] 9. Tarsitano,A.;Amerise, I.L.Short-termloadForecastingUsingaTwo-StageSarimaxModel.Energy2017,133, 108–114. [CrossRef] 10. Zhang,W.G.;Xie, F.X.;Huang,M.; Juan,L.; Li,Y.ResearchonShort-TermLoadForecastingMethodsof ElectricBusesChargingStation.PowerSyst. Prot. Control2013,41, 61–66. 11. Xiao,L.; Shao,W.;Yu,M.;Ma, J.; Jin,C.ResearchandApplicationofaHybridWaveletNeuralNetwork Modelwith the ImprovedCuckooSearchAlgorithmforElectricalPowerSYSTEMforecasting.Appl. Energy 2017,198, 203–222. [CrossRef] 12. Li,S.;Wang,P.;Goel,L.ANovelWavelet-BasedEnsembleMethodforShort-TermLoadForecastingwith HybridNeuralNetworksandFeatureSelection. IEEETrans. PowerSyst. 2016,31, 1788–1798. [CrossRef] 13. Xiong,X.;Chen,L.;Liang, J.ANewFrameworkofVehicleCollisionPredictionbyCombiningSVMand HMM. IEEETrans. Intell. Transp. Syst. 2017,19, 1–12. [CrossRef] 14. Yang,Y.L.;Che, J.X.;Li,Y.Y.;Zhao,Y.J.;Zhu,S.L.Anincrementalelectric loadforecastingmodelbasedon supportvector regression.Energy2016,113, 796–808. [CrossRef] 15. Liu,W.;Xiaobo,X.U.;Xi,Z.DailyloadforecastingbasedonSVMforelectricbuschargingstation.Electr.Power Autom.Equip. 2014,34, 41–47. 16. Deo,R.C.;Kisi,O.;Singh,V.P.DroughtforecastingineasternAustraliausingmultivariateadaptiveregression spline, least squaresupportvectormachineandM5Treemodel.Atmos. Res. 2017,184, 149–175. [CrossRef] 17. Lin,W.M.; Tu,C.S.; Yang,R.F.; Tsai,M.T.Particle swarmoptimisationaided least-square supportvector machine for loadforecastwithspikes. IETGener. Trans.Distrib. 2016,10, 1145–1153. [CrossRef] 18. Li,C.;Li,S.;Liu,Y.Aleast squaressupportvectormachinemodeloptimizedbymoth-ïŹ‚ameoptimization algorithmforannualpower loadforecasting.Appl. Intell. 2016,45, 1–13. [CrossRef] 19. Liang,Y.;Niu,D.;Ye,M.;Hong,W.C.Short-TermLoadForecastingBasedonWaveletTransformandLeast SquaresSupportVectorMachineOptimizedbyImprovedCuckooSearch.Energies2016,9, 827. [CrossRef] 20. Padilha,C.A.D.A.; Barone,D.A.C.;Neto,A.D.D.Amulti-level approachusinggenetic algorithms inan ensembleofLeastSquaresSupportVectorMachines.Knowl.-BasedSyst. 2016,106, 85–95. [CrossRef] 21. Dong,R.;Xu, J.;Lin,B.ROI-basedstudyonimpact factorsofdistributedPVprojectsbyLSSVM-PSO.Energy 2017,124, 336–349. [CrossRef] 22. Sun,W.;Sun, J.DailyPM2.5ConcentrationPredictionBasedonPrincipalComponentAnalysisandLSSVM OptimizedbycuckooSearchAlgorithm. J.Environ.Manag. 2016,188, 144. [CrossRef] [PubMed] 23. Niu, D.; Liang, Y.; Wang, H.; Wang,M.; Hong,W.C. Icing Forecasting of Transmission Lines with a ModiïŹedBackPropagationNeuralNetwork-SupportVectorMachine-ExtremeLearningMachinewith Kernel (BPNN-SVM-KELM)Basedon theVariance-CovarianceWeightDeterminationMethod. Energies 2017,10, 1196. [CrossRef] 24. Chen,X.;Tang,C.;Wang,J.;Zhang,L.;Liu,Y.ANovelHybridBasedonWolfPackAlgorithmandDifferential EvolutionAlgorithm. Int. Symp.Comput. Intell.Des. 2017, 69–74. [CrossRef] 25. Xue,B.;Zhang,M.;Browne,W.N.;Yao,X.ASurveyonEvolutionaryComputationApproaches toFeature Selection. IEEETrans. Evolut. Comput. 2016,20, 606–626. [CrossRef] 26. Hassanpour,H.;Zehtabian,A.;Nazari,A.;Dehghan,H.GenderclassiïŹcationbasedonfuzzyclusteringand principalcomponentanalysis. IETComput.Vis. 2016,10, 228–233. [CrossRef] 27. Kumar,M.R.;Ghosh,S.;Das,S.Frequencydependentpiecewisefractional-ordermodellingofultracapacitors usinghybridoptimizationandfuzzyclustering. J.PowerSources2016,335, 98–104. [CrossRef] 28. Alban,N.;Laurent,B.;Mitherand,N.;Ousman,B.;Martin,N.;Etienne,M.RobustandFastSegmentation BasedonFuzzyClusteringCombinedwithUnsupervisedHistogramAnalysis. IEEEIntell. Syst. 2017,32, 6–13. [CrossRef] 29. Bai,X.;Wang,Y.;Liu,H.;Guo,S.Symmetry InformationBasedFuzzyClusteringfor InfraredPedestrian Segmentation. IEEETrans. FuzzySyst. 2017. [CrossRef] 30. Zhu,B.;Han,D.;Wang,P.Forecastingcarbonpriceusingempiricalmodedecompositionandevolutionary least squaressupportvector regression.Appl. Energy2017,191, 521–530. [CrossRef] 31. Yuan,X.;Tan,Q.;Lei,X.;Yuan,Y.;Wu,X.WindPowerPredictionUsingHybridAutoregressiveFractionally IntegratedMovingAverageandLeastSquareSupportVectorMachine.Energy2017,129, 122–137. [CrossRef] 334
zurĂŒck zum  Buch Short-Term Load Forecasting by Artificial Intelligent Technologies"
Short-Term Load Forecasting by Artificial Intelligent Technologies
Titel
Short-Term Load Forecasting by Artificial Intelligent Technologies
Autoren
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Herausgeber
MDPI
Ort
Basel
Datum
2019
Sprache
englisch
Lizenz
CC BY 4.0
ISBN
978-3-03897-583-0
Abmessungen
17.0 x 24.4 cm
Seiten
448
Schlagwörter
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
Kategorie
Informatik
Web-Books
Bibliothek
Datenschutz
Impressum
Austria-Forum
Austria-Forum
Web-Books
Short-Term Load Forecasting by Artificial Intelligent Technologies