Page - 334 - in Short-Term Load Forecasting by Artificial Intelligent Technologies
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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-flameoptimization
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
ModifiedBackPropagationNeuralNetwork-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.Genderclassificationbasedonfuzzyclusteringand
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
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