D. Kiedanski, A. Orda, and D. Kofman, The effect of ramp constraints on coalitional storage games, Proceedings of the Tenth ACM International Conference on Future Energy Systems, e-Energy '19, pp.226-238, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02314732

E. L. Ratnam, S. R. Weller, and C. M. Kellett, An optimization-based approach to scheduling residential battery storage with solar pv: Assessing customer benefit, Renewable Energy, vol.75, pp.123-134, 2015.

S. J. Chiang, K. T. Chang, and C. Y. Yen, Residential photovoltaic energy storage system, IEEE Transactions on Industrial Electronics, vol.45, pp.385-394, 1998.

J. Horta, D. Kofman, D. Menga, and A. Silva, Novel market approach for locally balancing renewable energy production and flexible demand, 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp.533-539, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01647151

D. Kiedanski, D. Kofman, J. Horta, and D. Menga, Strategy-proof local energy market with sequential stochastic decision process for battery control, IEEE Innovative Smart Grid Technologies 2019 NA, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02083472

D. Thomas, O. Deblecker, and C. S. Ioakimidis, Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics uncertainty and stochastic electric vehicles driving schedule, Applied Energy, vol.210, pp.1188-1206, 2018.

Q. Wei, G. Shi, R. Song, and Y. Liu, Adaptive dynamic programmingbased optimal control scheme for energy storage systems with solar renewable energy, IEEE Transactions on Industrial Electronics, vol.64, pp.5468-5478, 2017.

X. Wu, X. Hu, S. Moura, X. Yin, and V. Pickert, Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array, Journal of Power Sources, vol.333, pp.203-212, 2016.

R. Cruise, L. Flatley, R. Gibbens, and S. Zachary, Control of energy storage with market impact: Lagrangian approach and horizons, Operations Research, vol.67, p.2019

C. Byrne and G. Verbic, Feasibility of residential battery storage for energy arbitrage, 2013 Australasian Universities Power Engineering Conference (AUPEC), pp.1-7, 2013.

T. Senjyu, H. Takara, K. Uezato, and T. Funabashi, One-hour-ahead load forecasting using neural network, IEEE Transactions on Power Systems, vol.17, pp.113-118, 2002.

D. C. Park, M. El-sharkawi, R. Marks, L. Atlas, and M. Damborg, Electric load forecasting using an artificial neural network, IEEE transactions on Power Systems, vol.6, issue.2, pp.442-449, 1991.

H. S. Hippert, C. E. Pedreira, and R. C. Souza, Neural networks for short-term load forecasting: A review and evaluation, IEEE Transactions on power systems, vol.16, issue.1, pp.44-55, 2001.

H. K. Alfares and M. Nazeeruddin, Electric load forecasting: literature survey and classification of methods, International journal of systems science, vol.33, issue.1, pp.23-34, 2002.

M. Y. Cho, J. C. Hwang, and C. S. Chen, Customer short term load forecasting by using arima transfer function model, Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95, vol.1, pp.317-322, 1995.

P. Lusis, K. R. Khalilpour, L. Andrew, and A. Liebman, Short-term residential load forecasting: Impact of calendar effects and forecast granularity, Applied Energy, vol.205, pp.654-669, 2017.

G. Bathurst and G. Strbac, Value of combining energy storage and wind in short-term energy and balancing markets, Electric Power Systems Research, vol.67, issue.1, pp.1-8, 2003.

S. Humeau, T. K. Wijaya, M. Vasirani, and K. Aberer, Electricity load forecasting for residential customers: Exploiting aggregation and correlation between households, 2013 Sustainable Internet and ICT for Sustainability (SustainIT), pp.1-6, 2013.

W. Kong, Z. Y. Dong, D. J. Hill, F. Luo, and Y. Xu, Short-term residential load forecasting based on resident behaviour learning, IEEE Transactions on Power Systems, vol.33, issue.1, pp.1087-1088, 2017.

S. Humeau, T. K. Wijaya, M. Vasirani, and K. Aberer, Electricity load forecasting for residential customers: Exploiting aggregation and correlation between households, 2013 Sustainable Internet and ICT for Sustainability (SustainIT), pp.1-6, 2013.

M. Ghofrani, M. Hassanzadeh, M. Etezadi-amoli, and M. S. Fadali, Smart meter based short-term load forecasting for residential customers, 2011 North American Power Symposium, pp.1-5, 2011.

A. Dunbar, F. Tagliaferri, I. M. Viola, and G. P. Harrison, The impact of electricity price forecast accuracy on the optimality of storage revenue, 3rd Renewable Power Generation Conference, pp.1-6, 2014.

D. Krishnamurthy, C. Uckun, Z. Zhou, P. R. Thimmapuram, and A. Botterud, Energy storage arbitrage under day-ahead and real-time price uncertainty, IEEE Transactions on Power Systems, vol.33, issue.1, pp.84-93, 2018.

K. Bradbury, L. Pratson, and D. Patio-echeverri, Economic viability of energy storage systems based on price arbitrage potential in real-time u.s. electricity markets, Applied Energy, vol.114, pp.512-519, 2014.

K. Ahlert and C. Block, Assessing the impact of price forecast errors on the economics of distributed storage systems, 2010 43rd Hawaii International Conference on System Sciences, pp.1-10, 2010.

M. U. Hashmi, A. Mukhopadhyay, A. Bu?i?, and J. Elias, Optimal control of storage under time varying electricity prices, 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp.134-140, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01672483

G. Mulder, D. Six, B. Claessens, T. Broes, N. Omar et al., The dimensioning of pv-battery systems depending on the incentive and selling price conditions, Applied Energy, vol.111, pp.1126-1135, 2013.

P. Mokrian and M. Stephen, A stochastic programming framework for the valuation of electricity storage, 26th USAEE/IAEE North American Conference, pp.24-27, 2006.

M. Hashmi, A. Mukhopadhyay, A. Busic, and J. Elias, Storage optimal control under net metering policies

, Powerwall 2 datasheet, pp.2019-2020

Y. Chen, M. U. Hashmi, D. Deka, and M. Chertkov, Stochastic battery operations using deep neural networks," in in IEEE ISGT, 2019.

P. Street-inc and . Dataport, , p.2019

S. Kolassa and W. Schtz, Advantages of the mad/mean ratio over the mape, Foresight: The International Journal of Applied Forecasting, vol.6, pp.40-43, 2007.