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Energies2018,11, 1893 21. Antoniadis,A.; Brossat,X.;Cugliari, J.; Poggi, J.M. PrĂ©visiond’unprocessusĂ valeurs fonctionnelles en prĂ©sencedenonstationnaritĂ©s. ApplicationĂ  la consommationd’électricitĂ©. J. Soc. Française Stat. 2012, 153, 52–78. 22. Misiti, M.; Misiti, Y.; Oppenheim, G.; Poggi, J.M. Optimized Clusters for Disaggregated Electricity LoadForecasting. Rev. Stat. J.2010,8, 105–124. 23. Quilumba,F.L.;Lee,W.J.;Huang,H.;Wang,D.Y.;Szabados,R.L. UsingSmartMeterData to Improvethe Accuracyof IntradayLoadForecastingConsideringCustomerBehaviorSimilarities. IEEETrans. SmartGrid 2015,6, 911–918. [CrossRef] 24. Cugliari, J.;Goude,Y.;Poggi, J.M. DisaggregatedElectricityForecastingusingWavelet-BasedClusteringof IndividualConsumers. InProceedingsof the2016IEEEInternationalEnergyConference (ENERGYCON), Leuven,Belgium,4–8April2016. 25. Antoniadis, A.; Brossat, X.; Cugliari, J.; Poggi, J.M. Une approche fonctionnelle pour la prĂ©vision non-paramĂ©triquede laconsommationd’électricitĂ©. J.Soc. FrançaiseStat. 2014,155, 202–219. 26. Labeeuw,W.;Stragier, J.;Deconinck,G. Potentialofactivedemandreductionwithresidentialwetappliances: AcasestudyforBelgium. SmartGrid IEEETrans. 2015,6, 315–323. [CrossRef] 27. Mallat,S.AWaveletTourofSignalProcessing;AcademicPress:Cambridge,MA,USA,1999. 28. Mallat, S. A theory formultiresolution signal decomposition: Thewavelet representation. IEEETrans. PatternAnal.Mach. Intell. 1989,11, 674–693. [CrossRef] 29. Bosq, D. Modelization, nonparametric estimation and prediction for continuous time processes. InNonparametric Functional Estimation andRelated Topics; Roussas, G., Ed.; NATOASI Series, (SeriesC: MathematicalandPhysicalSciences);Springer:Dordrecht,TheNetherland;1991;Volume335,pp. 509–529. 30. Poggi, J.M. PrĂ©visionnonparamĂ©triquede laconsommationĂ©lectrique. Rev. Stat.Appl. 1994,4, 93–98. 31. Antoniadis,A.;Paparoditis,E.; Sapatinas,T. Afunctionalwavelet-kernelapproachfor timeseriesprediction. J.R.Stat. Soc. Ser. BStat.Meth. 2006,68, 837. [CrossRef] 32. Cugliari, J. PrĂ©vision Non ParamĂ©trique De Processus Ă  Valeurs Fonctionnelles. Application Ă  la ConsommationD’électricitĂ©. Ph.D.Thesis,UniversitĂ©ParisSud,Orsay,France,2011. 33. Antoniadis,A.;Brossat,X.;Cugliari, J.;Poggi, J.M. Clusteringfunctionaldatausingwavelets. Int. J.Wave. Multiresolut. Inform. Proc. 2013,11. [CrossRef] 34. Steinley,D.; Brusco,A.M. newvariableweightingandselectionprocedure fork-meansclusteranalysis. Multivar. Behav. Res. 2008,43, 32. [CrossRef] [PubMed] 35. RCoreTeam.R:ALanguageandEnvironmentforStatisticalComputing; RFoundationforStatisticalComputing: Vienna,Austria,2018. 36. Jiang,Z.;Lin,R.;Yang,F.;Budan,W.AFusedLoadCurveClusteringAlgorithmbasedonWaveletTransform. IEEETrans. Ind. Inform. 2017. [CrossRef] 37. Thouvenot,V.;Pichavant,A.;Goude,Y.;Antoniadis,A.;Poggi, J.M. Electricity forecastingusingmulti-stage estimatorsofnonlinearadditivemodels. IEEETrans. PowerSyst. 2016,31, 3665–3673. [CrossRef] 38. Polikar,R. Ensemble learning. InEnsembleMachineLearning; Springer: Berlin,Germany,2012;pp. 1–34. 39. Gaillard,P.;Goude,Y. Forecastingelectricityconsumptionbyaggregatingexperts;howtodesignagoodset ofexperts. InModelingandStochasticLearning forForecasting inHighDimensions; Springer: Berlin,Germany, 2015;pp. 95–115. 40. Goehry,B.;Goude,Y.;Massart, P.; Poggi, J.M. ForĂȘtsalĂ©atoirespour laprĂ©visionĂ plusieursĂ©chellesde consommationsĂ©lectriques. InProceedingsof the50Ăšmes JournĂ©esdeStatistique, Paris Saclay, France, 28May–1June2018; talk112. 41. Li, P.; Zhang, B.; Weng, Y.; Rajagopal, R. A sparse linearmodel and signiïŹcance test for individual consumptionprediction. IEEETrans. PowerSyst. 2017,32, 4489–4500. [CrossRef] 42. Wang,Y.;Chen,Q.;Gan,D.;Yang, J.;Kirschen,D.S.;Kang,C.DeepLearning-BasedSocio-demographic InformationIdentiïŹcationfromSmartMeterData. IEEETrans. SmartGrid2018. [CrossRef] 43. Anderson,B.;Lin,S.;Newing,A.;Bahaj,A.; James,P. Electricityconsumptionandhouseholdcharacteristics: Implications for census-taking ina smartmetered future. Comput. Environ. UrbanSyst. 2017,63, 58–67. [CrossRef] 249
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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
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Short-Term Load Forecasting by Artificial Intelligent Technologies