Page - 249 - in Short-Term Load Forecasting by Artificial Intelligent Technologies
Image of the Page - 249 -
Text of the Page - 249 -
Energies2018,11, 1893
21. Antoniadis,A.; Brossat,X.;Cugliari, J.; Poggi, J.M. Prévisiond’unprocessusàvaleurs fonctionnelles en
présencedenonstationnarités. Applicationà la consommationd’électricité. 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 prévision
non-paramétriquede laconsommationd’électricité. 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. Prévisionnonparamétriquede laconsommationélectrique. 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. Prévision Non Paramétrique De Processus à Valeurs Fonctionnelles. Application à la
ConsommationD’électricité. Ph.D.Thesis,UniversitéParisSud,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. Forêtsaléatoirespour laprévisionàplusieurséchellesde
consommationsélectriques. InProceedingsof the50èmes JournéesdeStatistique, Paris Saclay, France,
28May–1June2018; talk112.
41. Li, P.; Zhang, B.; Weng, Y.; Rajagopal, R. A sparse linearmodel and significance 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
InformationIdentificationfromSmartMeterData. 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
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