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Energies2018,11, 1893 time. Thiscouldhelp tomodel thechangesofclustersalongtimebutwehave to thinkaboutapenalty mechanismallowingtomakechanges in theclusteronlywhenit isuseful. AuthorContributions:B.Auder, J.Cugliari,Y.GoudeandJ.-M.Poggiequallycontributedto thiswork. Acknowledgments: This research benefited from the support of the FMJH ’Program Gaspard Monge for optimization and operations research and their interactionswith data science’, and from the support from EDFandThales. Conflictsof Interest:Theauthorsdeclarenoconflictof interest. References 1. Yan,Y.;Qian,Y.; Sharif,H.;Tipper,D. ASurveyonSmartGridCommunicationInfrastructures:Motivations, RequirementsandChallenges. IEEECommun. Surv. Tutor. 2013,15, 5–20. [CrossRef] 2. Mallet, P.; Granstrom, P.O.; Hallberg, P.; Lorenz, G.; Mandatova, P. Power to the People!: European PerspectivesontheFutureofElectricDistribution. IEEEPowerEnergyMag. 2014,12, 51–64. [CrossRef] 3. Wang,Y.;Chen,Q.;Hong,T.;Kang,C. ReviewofSmartMeterDataAnalytics:Applications,Methodologies, andChallenges. IEEETrans. SmartGrid2018. [CrossRef] 4. Jamme,D. LecompteurLinky: Briqueessentielledesréseaux intelligents français. InProceedingsof the OfficeFranco-AllemandPourLaTransitionénergétique,Berlin,Germany,11May2017. 5. Alahakoon,D.; Yu, X. Smart ElectricityMeterData Intelligence for Future Energy Systems: ASurvey. IEEETrans. Ind. Inform.2016,12, 425–436. [CrossRef] 6. Ryberg, T. The Second Wave of Smart Meter Rollouts Begin in Italy and Sweden. 2017. Available online: https://www.metering.com/regional-news/europe-uk/second-wave-smart-meter- rollouts-begins-italy-sweden/(accessedon1June2018). 7. Jiang,H.;Wang,K.;Wang,Y.;Gao,M.;Zhang,Y. Energybigdata:Asurvey. IEEEAccess2016,4, 3844–3861. [CrossRef] 8. Kaufman,L.;Rousseeuw,P. FindingGroups inData:AnIntroduction toClusterAnalysis;Wiley:Hoboken,NJ, USA,1990. 9. Liao,T.W. Clusteringof timeseriesdataasurvey. PatternRecognit. 2005,38, 1857–1874. [CrossRef] 10. Jacques, J.; Preda, C. FunctionalDataClustering: ASurvey. Adv. DataAnal. Classif. 2014, 8, 231–255. [CrossRef] 11. Chicco, G. Overview and performance assessment of the clustering methods for electrical load patterngrouping. Energy2012,42, 68–80. [CrossRef] 12. Zhou,K.;Yang,S.;Shen,C.Areviewofelectric loadclassificationinsmartgridenvironment. Renew. Sustain. EnergyRev. 2013,24, 103–110. [CrossRef] 13. Wang, Y.; Chen, Q.; Kang, C.; Zhang, M.; Wang, K.; Zhao, Y. Load profiling and its application to demandresponse:Areview. TsinghuaSci. Technol. 2015,20, 117–129. [CrossRef] 14. Figueiredo,V.;Rodrigues,F.;Vale,Z.;Gouveia, J.B. Anelectricenergyconsumercharacterizationframework basedondataminingtechniques. IEEETrans. PowerSyst. 2005,20, 596–602. [CrossRef] 15. Mutanen,A.;Ruska,M.;Repo,S.; Jarventausta,P. CustomerClassificationandLoadProfilingMethodfor DistributionSystems. IEEETrans. PowerDeliv. 2011,26, 1755–1763. [CrossRef] 16. Rhodes, J.D.;Cole,W.J.;Upshaw,C.R.;Edgar,T.F.;Webber,M.E. Clusteringanalysisof residential electricity demandprofiles. Appl. Energy2014,135, 461–471. [CrossRef] 17. Kwac, J.; Flora, J.; Rajagopal, R. Household Energy Consumption SegmentationUsingHourly Data Smart Grid. IEEETrans. 2014,5, 420–430. 18. Sun,M.;Konstantelos, I.; Strbac,G. C-Vinecopulamixturemodel forclusteringof residential electrical load patterndata. InProceedingsof the2017 IEEEPowerEnergySocietyGeneralMeeting,Chicago, IL,USA, 16–20 July2017. [CrossRef]. 19. Alzate, C.; Sinn,M. Improved electricity load forecasting via kernel spectral clustering of smartmeter. InProceedings of the InternationalConference onDataMining,Dallas, TX,USA, 7–10December 2013; pp.943–948. 20. Chaouch, M. Clustering-Based Improvement of Nonparametric Functional Time Series Forecasting: Application to Intra-DayHousehold-LevelLoadCurves. IEEETrans. SmartGrid2014,5, 411–419. [CrossRef] 248