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
Self-organizing multi-agent systems for the control of complex systems, J. Syst. Softw, vol.134, pp.12-28, 2017. ,
Use of turbine-level data for improved wind power forecasting, 2017 IEEE Manchester PowerTech, pp.1-6, 2017. ,
Wind Energy Handbook, 2011. ,
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. ,
Fixed-radius near neighbors search algorithms for points and segments, Inf. Process. Lett, vol.35, issue.5, pp.269-273, 1990. ,
Cooperation, Self-organising Software: From Natural to Artificial Adaptation, pp.193-226, 2011. ,
The stateof-the-art in short-term prediction of wind power: a literature overview, ANEMOS.plus, 2011. ,
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
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. ,
, Global Energy Forecasting Competition 2012, 2014.
, Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond, 2016.
Current status and future advances for wind speed and power forecasting, Renew. Sustain. Energy Rev, vol.31, pp.762-777, 2014. ,
Probabilistic gradient boosting machines for GEFCom2014 wind forecasting, Int. J. Forecast, vol.32, issue.3, pp.1061-1066, 2016. ,
A comprehensive review on wind turbine power curve modeling techniques, Renew. Sustain. Energy Rev, vol.30, pp.452-460, 2014. ,
Wind power forecasting: state-of-the-art, 2009. ,
Wakes in very large wind farms and the effect of neighbouring wind farms, J. Phys. Conf. Ser, vol.524, issue.1, p.12162, 2014. ,
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
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
Wind energy: forecasting challenges for its operational management, Stat. Sci, vol.28, issue.4, pp.564-585, 2013. ,
Wind speed prediction in the mountainous region of India using an artificial neural network model, Renew. Energy, vol.80, pp.338-347, 2015. ,
Self-organization of robotic devices through demonstrations, 2016. ,
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.