Web-Books
im Austria-Forum
Austria-Forum
Web-Books
Informatik
Short-Term Load Forecasting by Artificial Intelligent Technologies
Seite - 178 -
  • Benutzer
  • Version
    • Vollversion
    • Textversion
  • Sprache
    • Deutsch
    • English - Englisch

Seite - 178 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Bild der Seite - 178 -

Bild der Seite - 178 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Text der Seite - 178 -

Energies2018,11, 2038 22. Hedén,W.PredictingHourlyResidentialEnergyConsumptionUsingRandomForestandSupportVector Regression:AnAnalysisoftheImpactofHouseholdClusteringonthePerformanceAccuracy,Degree-Project inMathematics (SecondCicle). Royal InstituteofTechnologySCISchool ofEngineeringSciences. 2016. Available online: https://kth.diva-portal.org/smash/get/diva2:932582/FULLTEXT01.pdf (accessedon 4July2018). 23. AhmedMohammed,A.;Aung,Z.EnsembleLearningApproachforProbabilisticForecastingofSolarPower Generation.Energies2016,9, 1017. [CrossRef] 24. Lin,Y.;Luo,H.;Wang,D.;Guo,H.;Zhu,K.AnEnsembleModelBasedonMachineLearningMethodsand DataPreprocessingforShort-TermElectricLoadForecasting.Energies2017,10, 1186. [CrossRef] 25. SistemadeInformacióndelOperadordelSistema(Esios);RedEléctricadeEspaña.AltaComoConsumidor DirectoenMercadoPeninsular.Availableonline: https://www.esios.ree.es/es/documentacion/guia-alta- os-consumidor-directo-mercado-peninsula (accessedon4July2018). 26. MINETAD. Ministerio de Energía, Turismo y Agenda Digital. Gobierno de España. Available online: http://www.minetad.gob.es/ENERGIA/ELECTRICIDAD/DISTRIBUIDORES/Paginas/ ConsumidoresDirectosMercado.aspx(accessedon4July2018). 27. Touzani,S.;Granderson, J.; Fernandes,S.Gradientboostingmachine formodeling theenergyconsumption ofcommercialbuildings.EnergyBuild. 2018,158, 1533–1543. [CrossRef] 28. James,G.;Witten,D.;Hastie,T.;Tibshirani,R.AnIntroduction toStatisticalLearning; Springer:NewYork,NY, USA,2013; ISBN978-1-4614-7138-7. 29. RPackage:RandomForest. RepositoryCRAN.Availableonline: https://cran.r-project.org/web/packages/ randomForest/randomForest.pdf (accessedon4July2018). 30. Hothorn,T.;Hornik,K.;Zeileis,A.UnbiasedRecursivePartitioning:AConditional InferenceFramework. J.Comput.Graph. Stat. 2006,15, 651–674. [CrossRef] 31. Strasser,H.;Weber,C.On the asymptotic theoryof permutation statistics. Math. Methods Stat. 1999, 8, 220–250. 32. RPackage: Party. RepositoryCRAN.Availableonline: https://cran.r-project.org/web/packages/party/ party.pdf (accessedon4July2018). 33. Freund, Y.; Schapire, R.E.Adecision-theoretic generalization of on-line learning and an application to boosting. J.Comput. Syst. Sci. 1997,55, 119–139. [CrossRef] 34. Friedman, J.H.Greedyfunctionapproximation:Agradientboostingmachine.Ann. Stat. 2001,19, 1189–1232. [CrossRef] 35. Hastie, T.; Tibshirani, R.; Friedman, J.H. 10. Boosting andAdditive Trees. InThe Elements of Statistical Learning, 2nded.;Springer:NewYork,NY,USA,2009;pp.337–384. ISBN0-387-84857-6. 36. Friedman, J.H.StochasticGradientBoosting.Comput. Stat.DataAnal. 2002,38, 367–378. [CrossRef] 37. Chen,T.;Guestrin,C.XGBoost:Ascalable treeboostingsystem. InProceedingsof the22ndACMSIGKDD InternationalConferenceonKnowledgeDiscoveryandDataMining,SanFrancisco,CA,USA,13–17August 2016;pp.785–794. 38. R Package: Xgboost. Repository CRAN.Available online: https://cran.r-project.org/web/packages/ xgboost/xgboost.pdf (accessedon4July2018). 39. Pérez-Lombard,L.;Ortiz, J.;Pout,C.Areviewonbuildingsconsumption information.EnergyBuild. 2008, 40, 394–398. [CrossRef] 40. Strobl,C.;Boulesteix,A.L.;Kneib,T.;Augustin,T.;Zeileis,A.ConditionalVariable Importance forRandom Forests.BMCBioinf. 2008,9, 307. [CrossRef] [PubMed] 41. BoletínOficialdelEstado. Ley24/2013,de26deDiciembre,delSectorEléctrico.Availableonline: https: //www.boe.es/buscar/doc.php?id=BOE-A-2013-13645 (accessedon4July2018). 42. BoletínOficialdelEstado.RealDecreto1955/2000,de1deDiciembre,PorelQueseRegulanlasActividades deTransporte,Distribución,Comercialización,SuministroyProcedimientosdeAutorizacióndeInstalaciones deEnergíaEléctrica.Availableonline: http://www.boe.es/buscar/act.php?id=BOE-A-2000-24019&tn=1& p=20131230&vd=#a70(accessedon4July2018). 178
zurück 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