Seite - 20 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Energies2018,11, 2226 Funding:Funding: This researchwasfundedbytheNationalNaturalScienceFoundationofChina(51509056); theHeilongjiangProvinceNaturalScienceFund(E2017028); theFundamentalResearchFunds for theCentral Universities (HEUCFG201813); theOpenFundof theStateKeyLaboratoryofCoastalandOffshoreEngineering (LP1610); Heilongjiang SanjiangProjectAdministration ScientificResearch andExperiments (SGZL/KY-08); andthe JiangsuDistinguishedProfessorProject (no. 9213618401), JiangsuNormalUniversity, JiangsuProvincial DepartmentofEducation,China. Acknowledgments:Ming-WeiLi, JingGeng,andYangZhangacknowledgethesupport fromtheprojectgrants: theNationalNaturalScienceFoundationofChina(51509056); theHeilongjiangProvinceNaturalScienceFund (E2017028); theFundamentalResearchFunds for theCentralUniversities (HEUCFG201813); theOpenFund of theStateKeyLaboratoryofCoastal andOffshoreEngineering (LP1610); andHeilongjiangSanjiangProject AdministrationScientificResearchandExperiments (SGZL/KY-08).Wei-ChiangHongacknowledges thesupport fromtheJiangsuDistinguishedProfessorProject (no. 9213618401)ofJiangsuNormalUniversity, JiangsuProvincial DepartmentofEducation,China. Conflictsof Interest:Theauthorsdeclarenoconflictof interest. References 1. Maçaira,P.M.;Souza,R.C.;Oliveira,F.L.C.Modellingandforecasting theresidential electricityconsumption inBrazilwithpegelsexponential smoothingtechniques.ProcediaComput. Sci. 2015,55, 328–335. [CrossRef] 2. Pappas, S.S.; Ekonomou, L.; Karampelas, P.; Karamousantas, D.C.; Katsikas, S.K.; Chatzarakis, G.E.; Skafidas,P.D.Electricitydemand load forecastingof theHellenicpower systemusinganARMAmodel. Electr. PowerSyst. Res. 2010,80, 256–264. [CrossRef] 3. Dudek,G.Pattern-basedlocal linearregressionmodels forshort-termloadforecasting.Electr.PowerSyst.Res. 2016,130, 139–147. [CrossRef] 4. Chen,Y.;Luh,P.B.;Guan,C.;Zhao,Y.;Michel,L.D.;Coolbeth,M.A.;Friedland,P.B.;Rourke,S.J. Short-term load forecasting: Similarday-basedwaveletneuralnetworks. IEEETrans. PowerSyst. 2010,25, 322–330. [CrossRef] 5. Li,S.;Wang,P.;Goel,L.Short-termloadforecastingbywavelet transformandevolutionaryextremelearning machine.Electr. PowerSyst. Res. 2015,122, 96–103. [CrossRef] 6. Fan,G.F.;Wang,A.; Hong,W.C.Combininggreymodel and self-adapting intelligent greymodelwith genetic algorithmandannual share changes innatural gasdemand forecasting. Energies 2018, 11, 1625. [CrossRef] 7. Ma, X.; Liu, Z.Application of a novel time-delayed polynomial greymodel to predict the natural gas consumption inChina. J.Comput.Appl.Math. 2017,324, 17–24. [CrossRef] 8. Lou,C.W.;Dong,M.C.Anovel randomfuzzyneuralnetworks for tacklinguncertaintiesof electric load forecasting. Int. J.Electr. PowerEnergySyst. 2015,73, 34–44. [CrossRef] 9. Ertugrul,Ö.F.Forecastingelectricity loadbyanovel recurrentextremelearningmachinesapproach. Int. J. Electr. PowerEnergySyst. 2016,78, 429–435. [CrossRef] 10. Geng, J.;Huang,M.L.;Li,M.W.;Hong,W.C.Hybridizationofseasonalchaoticcloudsimulatedannealing algorithminaSVR-based loadforecastingmodel.Neurocomputing2015,151, 1362–1373. [CrossRef] 11. Hooshmand,R.A.;Amooshahi,H.;Parastegari,M.Ahybrid intelligentalgorithmBasedshort-termload forecastingapproach. Int. J.Electr. PowerEnergySyst. 2013,45, 313–324. [CrossRef] 12. Niu,D.X.;Shi,H.;Wu,D.D.Short-termloadforecastingusingBayesianneuralnetworks learnedbyhybrid MonteCarloalgorithm.Appl. SoftComput. 2012,12, 1822–1827. [CrossRef] 13. Hanmandlu,M.;Chauhan,B.K.Loadforecastingusinghybridmodels. IEEETrans. PowerSyst. 2011,26, 20–29. [CrossRef] 14. Mahmoud,T.S.;Habibi,D.;Hassan,M.Y.;Bass,O.Modellingself-optimisedshort termloadforecastingfor mediumvoltage loadsusingtunningfuzzysystemsandartificialneuralnetworks.EnergyConvers.Manag. 2015,106, 1396–1408. [CrossRef] 15. Suykens, J.A.K.; Vandewalle, J.; DeMoor, B.Optimal control by least squares support vectormachines. NeuralNetw. 2001,14, 23–35. [CrossRef] 16. Sankar,R.; Sapankevych,N.I.Timeseriespredictionusingsupportvectormachines:Asurvey. IEEEComput. Intell.Mag. 2009,4, 24–38. 17. Vapnik,V.N.TheNatureofStatisticalLearningTheory; Springer:NewYork,NY,USA,1995. 20