C. Bernon, M. Gleizes, S. Peyruqueou, and G. Picard, ADELFE: a methodology for adaptive multi-agent systems engineering, ESAW 2002, vol.2577, pp.156-169, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01205160

J. Boes and F. Migeon, Self-organizing multi-agent systems for the control of complex systems, J. Syst. Softw, vol.134, pp.12-28, 2017.

J. Browell, C. Gilbert, and D. Mcmillan, Use of turbine-level data for improved wind power forecasting, 2017 IEEE Manchester PowerTech, pp.1-6, 2017.

T. Burton, N. Jenkins, D. Sharpe, and E. Bossanyi, Wind Energy Handbook, 2011.

D. Capera, J. P. Georgé, M. P. Gleizes, and P. Glize, The AMAS theory for complex problem solving based on self-organizing cooperative agents, 12th IEEE International Workshops on Enabling Technologies, Infrastructure for Collaborative Enterprises, pp.383-388, 2003.

M. T. Dickerson and R. S. Drysdale, Fixed-radius near neighbors search algorithms for points and segments, Inf. Process. Lett, vol.35, issue.5, pp.269-273, 1990.

J. P. Georgé, M. P. Gleizes, V. Camps, . Di-marzo, G. Serugendo et al., Cooperation, Self-organising Software: From Natural to Artificial Adaptation, pp.193-226, 2011.

G. Giebel, R. Brownsword, G. Kariniotakis, M. Denhard, and C. Draxl, The stateof-the-art in short-term prediction of wind power: a literature overview, ANEMOS.plus, 2011.

G. Giebel, J. Cline, H. Frank, W. Shaw, P. Pinson et al., Wind power forecasting: IEA Wind Task 36 & future research issues, J. Phys.: Conf. Ser, vol.753, p.32042, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01409112

V. Guivarch, C. Bernon, and M. P. Gleizes, Power optimization by cooling photovoltaic plants as a dynamic self-adaptive regulation problem, International Conference on Agents and Artificial Intelligence (ICAART), vol.1, pp.276-281, 2018.

T. Hong, P. Pinson, and S. Fan, Global Energy Forecasting Competition 2012, 2014.

T. Hong, P. Pinson, S. Fan, H. Zareipour, A. Troccoli et al., Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond, 2016.

J. Jung and R. P. Broadwater, Current status and future advances for wind speed and power forecasting, Renew. Sustain. Energy Rev, vol.31, pp.762-777, 2014.

M. Landry, T. P. Erlinger, D. Patschke, and C. Varrichio, Probabilistic gradient boosting machines for GEFCom2014 wind forecasting, Int. J. Forecast, vol.32, issue.3, pp.1061-1066, 2016.

M. Lydia, S. S. Kumar, A. I. Selvakumar, and G. E. Kumar, A comprehensive review on wind turbine power curve modeling techniques, Renew. Sustain. Energy Rev, vol.30, pp.452-460, 2014.

C. Monteiro, R. Bessa, V. Miranda, A. Botterud, J. Wang et al., Wind power forecasting: state-of-the-art, 2009.

N. G. Nygaard, Wakes in very large wind farms and the effect of neighbouring wind farms, J. Phys. Conf. Ser, vol.524, issue.1, p.12162, 2014.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: machine learning in python, J. Mach. Learn. Res, vol.12, issue.10, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

A. Perles, An adaptive multi-agent system for the distribution of intelligence in electrical distribution networks: state estimation, 2017.
URL : https://hal.archives-ouvertes.fr/tel-01743586

P. Pinson, Wind energy: forecasting challenges for its operational management, Stat. Sci, vol.28, issue.4, pp.564-585, 2013.

P. Ramasamy, S. Chandel, and A. K. Yadav, Wind speed prediction in the mountainous region of India using an artificial neural network model, Renew. Energy, vol.80, pp.338-347, 2015.

N. Verstaevel, Self-organization of robotic devices through demonstrations, 2016.

H. Wang, G. Li, G. Wang, J. Peng, H. Jiang et al., Deep learning based ensemble approach for probabilistic wind power forecasting, Appl. Energy, vol.188, pp.56-70, 2017.

, Wind Observatory: Analysis of the wind power market, wind jobs and future of the wind industry in France, 2017.