R. European-commission, . From, and . Commission, Benchmarking smart metering deployment in the EU-27 with a focus on electricity, The European Commission, pp.1403084595595-2014, 2014.

J. Duplex, S. Gosswiller, and S. Fagnoni, A better knowledge of electricity consumption for residential customers through the Linky smart meter, 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013)
DOI : 10.1049/cp.2013.1188

R. Chicheportiche, Gazpar sort de son écrin, Pétrole Gaz Inf, pp.60-62, 2013.

O. T. Masoso and L. J. Grobler, The dark side of occupants' behaviour on building energy use, Energy Build, pp.173-177, 2010.

D. Yan, T. Hong, B. Dong, A. Mahdavi, S. D. Oca et al., Definition and Simulation of Occupant Behavior in Buildings, IEA EBC Annex Energy Build, vol.66, 2017.

A. and A. Standard, Refrigerating and Air-Conditioning Engineers Inc, Energy Conservation in Existing Buildings (ANSI/IESNA Approved), pp.100-2006, 2006.

A. Alajmi, Energy audit of an educational building in a hot summer climate, Energy Build, 2012.

Y. Allab, M. Pellegrino, X. Guo, E. Nefzaoui, and A. Kindinis, Energy and comfort assessment in educational building: Case study in a French university campus, Energy and Buildings, vol.143, 2017.
DOI : 10.1016/j.enbuild.2016.11.028
URL : https://hal.archives-ouvertes.fr/hal-01578585

P. Ville-de, Rénovation énergétique des écoles parisiennes, Ville de Paris, Direction des Espaces Verts et de l'Environnement-Agence d'Ecologie Urbaine-Division Climat-Energie, 2015.

A. Thewes, S. Maas, F. Scholzen, D. Waldmann, and A. , Zürbes, Field study on the energy consumption of school buildings in Luxembourg, Energy Build, 2014.

V. Butala and P. Novak, Energy consumption and potential energy savings in old school buildings, Energy and Buildings, vol.29, issue.3, pp.241-246, 1999.
DOI : 10.1016/S0378-7788(98)00062-0

D. Agdas, R. S. Srinivasan, K. Frost, and F. J. Masters, Energy use assessment of educational buildings: Toward a campus-wide sustainable energy policy, Sustainable Cities and Society, vol.17, 2015.
DOI : 10.1016/j.scs.2015.03.001

V. I. Deshko and O. M. Shevchenko, University campuses energy performance estimation in Ukraine based on measurable approach, Energy Build, 2013.

E. H. Borgstein, R. Lamberts, and J. L. Hensen, Evaluating energy performance in non-domestic buildings: A review, Energy Build, 2016.

L. Dias-pereira, D. Raimondo, S. P. Corgnati, M. Gameiro, and . Silva, Energy consumption in schools ??? A review paper, Renewable and Sustainable Energy Reviews, vol.40, 2014.
DOI : 10.1016/j.rser.2014.08.010

M. A. Hossain, T. J. Peshek, Y. Xu, Y. Hu, L. Ji et al., Predictive linear regression model for microinverter internal temperature, 2014 IEEE 40th Photovoltaic Specialist Conference (PVSC), pp.2014-0992, 2014.
DOI : 10.1109/PVSC.2014.6925081
URL : http://engineering.case.edu/centers/sdle/sites/engineering.case.edu.centers.sdle/files/1406pvscmicroinverter-v2_0.pdf

M. G. Kendall and A. Stuart, Advanced Theory of Statistics, 1983.

R. B. Cleveland, W. S. Cleveland, J. E. Mcrae, and I. Terpenning, STL: A Seasonal-Trend Decomposition Procedure Based on Loess, J. Off. Stat, pp.6-9, 1990.

K. Basu, V. Debusschere, S. Bacha, A. Hably, D. Van-delft et al., A generic data driven approach for low sampling load disaggregation, Sustain. Energy Grids Netw, 2017.
DOI : 10.1016/j.segan.2016.12.006

G. W. Hart, Nonintrusive appliance load monitoring, Proceedings of the IEEE, vol.80, issue.12, pp.1870-1891, 1992.
DOI : 10.1109/5.192069

M. Zeifman and K. Roth, Nonintrusive appliance load monitoring: Review and outlook, IEEE Trans. Consum. Electron, vol.57, 2011.
DOI : 10.1109/tce.2011.5735484

E. Azar and C. C. Menassa, Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings, Journal of Computing in Civil Engineering, vol.26, issue.4, 2012.
DOI : 10.1061/(ASCE)CP.1943-5487.0000158

Z. Yang and B. Becerik-gerber, The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use, Energy Build, 2014.

J. Yang, M. Santamouris, and S. E. Lee, Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings, Energy Build, 2016.

R. Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing

K. Basu, A. Hably, V. Debusschere, S. Bacha, G. J. Driven et al., A comparative study of low sampling non intrusive load dis-aggregation, IECON 2016, 42nd Annual Conference of the IEEE Industrial Electronics Society, pp.5137-5142, 2016.
DOI : 10.1109/IECON.2016.7793294
URL : https://hal.archives-ouvertes.fr/hal-01385572

M. R. Braun, H. Altan, and S. B. Beck, Using regression analysis to predict the future energy consumption of a supermarket in the UK, Applied Energy, vol.130, pp.16247-16248, 2012.
DOI : 10.1016/j.apenergy.2014.05.062

B. Bsi and . En, Performance énergétique des bâtiments Consommation globale d'énergie et définition des évaluations énergétiques https://www.boutique.afnor.org/norme/bs-en-156032008/performance- energetique-des-batiments-consommation-globale-d-energie-et-definition-des-evaluations- energetiques/article, p.105860, 2008.

C. Collectif and R. Thermique, Guide réglementaire -Principes et exigences -Cadre réglementaire -Méthodes de calcul, pp.2012-9782868915337, 2012.

J. Vesterberg, S. Andersson, and T. Olofsson, Robustness of a regression approach, aimed for calibration of whole building energy simulation tools, Energy Build, 2014.

C. A. Webber, J. A. Roberson, M. C. Mcwhinney, R. E. Brown, M. J. Pinckard et al., After-hours power status of office equipment in the USA, Energy, pp.31-2823, 2006.