, International Energy Agency. Global EV Outlook, 2017.

, International Energy Agency. Nordic EV Outlook, 2018.

, Status of NVE's Work on Network Tariffs in the Electricity Distribution System; The Norwegian Water Resources and Energy Directorate (NVE): Oslo, 2016.

Q. Wu, J. M. Jensen, L. H. Hansen, A. Bjerre, A. H. Nielsen et al., EV Portfolio Management and Grid Impact Study, Annual Report, 2009.

K. Qian, C. Zhou, M. Allan, and Y. Yuan, Modeling of load demand due to EV battery charging in distribution systems, IEEE Trans. Power Syst, vol.26, 2011.

S. Bae and A. Kwasinski, Spatial and temporal model of electric vehicle charging demand, IEEE Trans. Smart Grid, vol.3, pp.394-403, 2012.

G. Putrus, P. Suwanapingkarl, D. Johnston, E. Bentley, and M. Narayana, Impact of electric vehicles on power distribution networks, Proceedings of the 2009 IEEE Vehicle Power and Propulsion Conference, pp.827-831, 2009.

F. Mwasilu, J. J. Justo, E. K. Kim, T. D. Do, and J. W. Jung, Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration, Renew. Sustain. Energy Rev, vol.34, pp.501-516, 2014.

J. Dickert and P. Schegner, Residential load models for network planning purposes. Modern Electric Power Systems (MEPS), Proceedings of the 2010 IEEE International Symposium, pp.1-6, 2010.

J. Tomi´ctomi´c and W. Kempton, Using fleets of electric-drive vehicles for grid support, J. Power Sources, vol.168, pp.459-468, 2007.

C. A. Correa-florez, A. Gerossier, A. Michiorri, R. Girard, and G. Kariniotakis, Residential electrical and thermal storage optimisation in a market environment. CIRED-Open Access Proc, 1967.
URL : https://hal.archives-ouvertes.fr/hal-01518380

J. Ponocko and J. V. Milanovic, Forecasting Demand Flexibility of Aggregated Residential Load Using Smart Meter Data, IEEE Trans. Power Syst, vol.33, pp.5446-5455, 2018.

R. Gough, C. Dickerson, P. Rowley, and C. Walsh, Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage, Appl. Energy, vol.192, pp.12-23, 2017.

, International Energy Agency. Energy Technology Perspective, 2017.

C. Smith, M. Fowler, E. Greene, and C. Nielson, Carbon emissions and climate change: A study of attitudes and their relationship with travel behavior, Proceedings of the Transportation Research Board's National Transportation Planning Applications Conference, pp.17-21, 2009.

C. Beckel, L. Sadamori, T. Staake, and S. Santini, Revealing household characteristics from smart meter data, vol.78, pp.397-410, 2014.

C. Madrid, J. Argueta, and J. Smith, Performance Characterization-1999 Nissan Altra-EV with Lithium-ion Battery

E. Southern-california, , 1999.

. Ionity, Fast Charging Station Network Starts to Take Shape: Site Partners for 18 European Countries Secured, 2017.

T. Duong, Kernel Smoothing, 2018.

N. H. Anderson, P. Hall, and D. M. Titterington, Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates, J. Multivar. Anal, vol.50, pp.41-54, 1994.

T. Duong and M. L. Hazelton, Cross-validation bandwidth matrices for multivariate kernel density estimation. Scand, J. Stat, vol.32, pp.485-506, 2005.

F. Murtagh and P. Legendre, Ward's hierarchical agglomerative clustering method: Which algorithms implement Ward's criterion?, J. Classif, vol.31, pp.274-295, 2014.

M. N. Wright, A. Ziegler, and . Ranger, A Fast Implementation of Random Forests for High Dimensional Data in C++ and R, J. Stat. Softw, vol.77, pp.1-17, 2017.

L. Breiman, Random forests, Mach. Learn, vol.45, pp.5-32, 2001.

S. Haben, J. Ward, D. V. Greetham, C. Singleton, and P. Grindrod, A new error measure for forecasts of household-level, high resolution electrical energy consumption, Int. J. Forecast, vol.30, pp.246-256, 2014.

G. Ridgeway and . Gbm, Generalized Boosted Regression Models, 2017.

A. Gerossier, R. Girard, G. Kariniotakis, and A. Michiorri, Probabilistic day-ahead forecasting of household electricity demand. CIRED-Open Access Proc, pp.2500-2504, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01518373

J. H. Friedman, Stochastic gradient boosting, Comput. Stat. Data Anal, vol.38, pp.367-378, 2002.

S. Musti and K. M. Kockelman, Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas. Transp. Res. Part A Policy Pract, vol.45, pp.707-720, 2011.

. Ercot, . Ercot-load, and . History, , 2019.

E. V. Nordic and . Barometer, , 2018.

R. Luthander, M. Shepero, J. Munkhammar, and J. Widén, Photovoltaics and opportunistic electric vehicle charging in the power system-A case study on a Swedish distribution grid, Proceedings of the 7th International Workshop on Integration of Solar into Power Systems, pp.24-25, 2017.

Y. He, B. Venkatesh, and L. Guan, Optimal scheduling for charging and discharging of electric vehicles, IEEE Trans. Smart Grid, vol.3, pp.1095-1105, 2012.

Y. Cao, S. Tang, C. Li, P. Zhang, Y. Tan et al., An optimized EV charging model considering TOU price and SOC curve, IEEE Trans. Smart Grid, vol.3, pp.388-393, 2012.