, 3.1.1 Choice of a machine learning algorithm

.. .. Detailed-results,

, First, as explained in section 4.6.2.1, it is necessary to decide on the initialization value of the forecast and the increment value

F. Berna, P. Goldberg, L. K. Horwitz, J. Brink, S. Holt et al., Microstratigraphic evidence of in situ fire in the acheulean strata of wonderwerk cave, northern cape province, south africa, Proceedings of the National Academy of Sciences, vol.109, issue.20, pp.1215-1220, 2012.

K. H. Fasol, A short history of hydropower control, IEEE Control systems magazine, vol.22, issue.4, pp.68-76, 2002.

D. and L. Gourieres, Wind power plants: theory and design, 2014.

C. Zou, Q. Zhao, G. Zhang, and B. Xiong, Energy revolution: from a fossil energy era to a new energy era, Natural Gas Industry B, vol.3, issue.1, pp.1-11, 2016.

J. Rifkin, The third industrial revolution, 2011.

R. P. Feynman, R. B. Leighton, and M. Sands, The feynman lectures on physics, vol.33, issue.9, pp.750-752, 1965.

G. R. Slemon, Magnetoelectric devices: transducers, transformers, and machines, 1966.

, International Energy Agency, Global energy & climate outlook, 2018.

M. Sato, Thermochemistry of the formation of fossil fuels, vol.2, pp.271-283, 1990.

D. Sarkar, Thermal power plant: design and operation, 2015.

, International Energy Agency, World energy outlook, 2018.

, Bp statistical review of world energy, BP, 2019.

, Intergovernmental Panel on Climate Change, Summary for policymakers, 2018.

H. Ritchie and M. Roser, Co2 and other greenhouse gas emissions, 2019.

, The world factbook, 2019.

, International Renewable Energy Agency, Capacity and generation, 2018.

M. Combe, Energies renouvelables: des investissements en baisse!, 2018.

, International Renewable Energy Agency, Finance and investiment, 2018.

, Bilan, 2018.

M. Aneke and M. Wang, Energy storage technologies and real life applications -a state of the art review, Applied Energy, vol.179, pp.350-377, 2016.

A. Barbaux, Pourquoi edf n'investit pas plus dans les step pour le stockage des énergies renouvelables, 2017.

. Ares, Grid scale energy storage, 2019.

, Observatoire des marchés de détail du 4e trimestre, 2018.

S. Amelang and K. Appunn, The causes and effects of negative power prices, 2018.

. Legifrance, Loi n°2015-992 du 17 août 2015 relative à la transition énergétique pour la croissance verte, 2015.

, Commission de régulation de l'énergie, Services système et mécanisme d'ajustement, 2018.

P. Pinson, Wind energy: forecasting challenges for its operational management, Statistical Science, pp.564-585, 2013.

, Global cells, Met Office, 2019.

, Navier-stokes equation, 2019.

S. E. Tuller and A. C. Brett, The characteristics of wind velocity that favor the fitting of a weibull distribution in wind speed analysis, Journal of Climate and Applied Meteorology, vol.23, issue.1, pp.124-134, 1984.

, Wind Energy -The Facts, The annual variability of wind speed, 2019.

. Cermak-peterka-petersen, Wind profile characterization, 2015.

, Global wind atlas methodology, 2019.

, Wind turbine blade, 2016.

L. Futuren and . Éolienne, comment ça marche ?, 2019.

W. Tong, Wind power generation and wind turbine design, 2010.

. Wikipedia, Moulin saint-elzéar de montfuron, Available: https : / / fr . wikipedia . org / wiki / Moulin _ Saint -Elz % 5C % C3 % 5C % A9ar _ de _ Montfuron, 2019.

, Windpump, 2019.

, Quiet Revolution, Photos, 2019.

, Éolienne savonius, Solutions alternatives, 2019.

G. Vergnet, , 2019.

. Orsted, World's largest offshore wind farm officially unveiled, 2019.

. Wikipedia, Floating wind turbine, 2019.

S. Gallup, Germany invests in renewable energy sources, 2019.

A. Betz, Introduction to the theory of flow machines, 1966.

T. Burton, N. Jenkins, D. Sharpe, and E. Bossanyi, Wind energy handbook, 2011.

. The-wind and . Power, Database, 2016.

, Haliade-x offshore wind turbine platform, 2019.

L. Bird, J. Cochran, and X. Wang, Wind and solar energy curtailment: Experience and practices in the United States, 2014.

, Pitch-regulated and stall-regulated wind turbine, Research Hubs, 2015.

F. Grasso, D. Coiro, N. Bizzarrini, and G. Calise, Design of advanced airfoil for stall-regulated wind turbines, Journal of Physics: Conference Series, vol.753, pp.22-30, 2016.

N. G. Nygaard, Wakes in very large wind farms and the effect of neighbouring wind farms, Journal of Physics: Conference Series, vol.524, issue.1, 2014.

. Vattenfall, Horns rev 1, systerpark till horns rev 3, 2016.

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

Y. Zhang, J. Wang, and X. Wang, Review on probabilistic forecasting of wind power generation, Renewable and Sustainable Energy Reviews, vol.32, pp.255-270, 2014.

T. Gneiting, Probabilistic forecasting, Journal of the Royal Statistical Society: Series A (Statistics in Society, vol.171, issue.2, pp.319-321, 2008.

R. Bessa, C. Möhrlen, V. Fundel, M. Siefert, J. Browell et al., Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry, Energies, vol.10, issue.9, p.1402, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01589969

C. Graham, The parameterisation and prediction of wave height and wind speed persistence statistics for oil industry operational planning purposes, Coastal Engineering, vol.6, issue.4, pp.303-329, 1982.

S. Kuwashima and N. Hogben, The estimation of wave height and wind speed persistence statistics from cumulative probability distributions, Coastal Engineering, vol.9, issue.6, pp.563-590, 1986.

P. Pinson and H. Madsen, Ensemble-based probabilistic forecasting at horns rev, Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, vol.12, issue.2, pp.137-155, 2009.

, Adaptive modelling and forecasting of offshore wind power fluctuations with markov-switching autoregressive models, Journal of forecasting, vol.31, issue.4, pp.281-313, 2012.

K. Rogers, J. Collins, J. Parkes, and L. Landberg, Wind power forecasting offshore, more or less accurate than onshore?, 2012.

E. N. Lorenz, Deterministic nonperiodic flow, Journal of the atmospheric sciences, vol.20, issue.2, pp.130-141, 1963.

J. Coiffier, Fundamentals of numerical weather prediction, 2011.

P. Bauer, A. Thorpe, and G. Brunet, The quiet revolution of numerical weather prediction, Nature, vol.525, issue.7567, p.47, 2015.

M. France, Glossaire: maille, 2019.

, Global forecast system (gfs), 2019.

, Documentation and support, 2019.

M. France, Données de modèle atmosphérique global, 2019.

, Données de modèle atmosphérique à aire limitée à haute résolution, 2019.

J. F. Manwell, J. G. Mcgowan, and A. L. Rogers, Wind energy explained: theory, design and application, 2010.

J. D. Holmes, Wind loading of structures, 2018.

T. R. Oke, Boundary layer climates. Routledge, 2002.

G. Kariniotakis, P. Pinson, N. Siebert, G. Giebel, and R. Barthelmie, The state of the art in short-term prediction of wind power-from an offshore perspective, Proceedings of, pp.20-21, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00529338

M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan, A review on the forecasting of wind speed and generated power, Renewable and Sustainable Energy Reviews, vol.13, issue.4, pp.915-920, 2009.

A. Costa, A. Crespo, J. Navarro, G. Lizcano, H. Madsen et al., A review on the young history of the wind power short-term prediction, Renewable and Sustainable Energy Reviews, vol.12, issue.6, pp.1725-1744, 2008.

L. Landberg, G. Giebel, H. A. Nielsen, T. Nielsen, and H. Madsen, Short-term prediction-an overview, Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, vol.6, pp.273-280, 2003.

N. Siebert, Development of methods for regional wind power forecasting, 2008.
URL : https://hal.archives-ouvertes.fr/tel-00287551

L. Landberg, Short-term prediction of the power production from wind farms, Journal of Wind Engineering and Industrial Aerodynamics, vol.80, issue.1-2, pp.207-220, 1999.

U. Focken, M. Lange, and H. Waldl, Previento-a wind power prediction system with an innovative upscaling algorithm, Proceedings of the European Wind Energy Conference, vol.276, 2001.

I. Mart?, D. Cabezón, J. Villanueva, M. J. Sanisidro, Y. Loureiro et al., Localpred and regiopred. advanced tools for wind energy prediction in complex terrain, Proceedings of the European Wind Energy Conference EWEC'03, pp.16-19, 2003.

J. Zack, M. Brower, and B. Bailey, Validating of the forewind model in wind forecasting applications, Talk on the EUWEC Special Topic Conference Wind Power for the 21st Century, pp.25-27, 2000.

E. Alpaydin, Introduction to machine learning, 2009.

G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control, 2015.

P. Ailliot and V. Monbet, Markov-switching autoregressive models for wind time series, Environmental Modelling & Software, vol.30, pp.92-101, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01083071

J. P. Catalão, H. M. Pousinho, and V. M. Mendes, Short-term wind power forecasting in portugal by neural networks and wavelet transform, Renewable energy, vol.36, issue.4, pp.1245-1251, 2011.

V. Vapnik, The nature of statistical learning theory, Springer science & business media, 2013.

J. Zeng and W. Qiao, Support vector machine-based short-term wind power forecasting, 2011 IEEE/PES Power Systems Conference and Exposition, pp.1-8, 2011.

J. Zhou, J. Shi, and G. Li, Fine tuning support vector machines for short-term wind speed forecasting, Energy Conversion and Management, vol.52, issue.4, 1990.

G. Santamar?a-bonfil, A. Reyes-ballesteros, and C. Gershenson, Wind speed forecasting for wind farms: a method based on support vector regression, Renewable Energy, vol.85, pp.790-809, 2016.

S. S. Haykin, Neural networks and learning machines/Simon Haykin, 2009.

M. L. Minsky and S. A. Papert, Perceptrons: expanded edition, 1988.

D. D?az, A. Torres, and J. R. Dorronsoro, Deep neural networks for wind energy prediction, International Work-Conference on Artificial Neural Networks, pp.430-443, 2015.

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

A. Ghaderi, B. M. Sanandaji, and F. Ghaderi, Deep forecast: deep learning-based spatio-temporal forecasting, 2017.

Q. Cao, B. T. Ewing, and M. A. Thompson, Forecasting wind speed with recurrent neural networks, European Journal of Operational Research, vol.221, issue.1, pp.148-154, 2012.

Z. Liu, W. Gao, Y. Wan, and E. Muljadi, Wind power plant prediction by using neural networks, pp.3154-3160, 2012.

S. Balluff, J. Bendfeld, and S. Krauter, Short term wind and energy prediction for offshore wind farms using neural networks, 2015 International Conference on Renewable Energy Research and Applications (ICRERA), pp.379-382, 2015.

Z. O. Olaofe, A 5-day wind speed & power forecasts using a layer recurrent neural network (lrnn), Sustainable Energy Technologies and Assessments, vol.6, pp.1-24, 2014.

E. López, C. Valle, H. Allende, E. Gil, and H. Madsen, Wind power forecasting based on echo state networks and long short-term memory, Energies, vol.11, issue.3, p.526, 2018.

A. S. Qureshi, A. Khan, A. Zameer, and A. Usman, Wind power prediction using deep neural network based meta regression and transfer learning, Applied Soft Computing, vol.58, pp.742-755, 2017.

Z. Zhou, Ensemble methods: foundations and algorithms, 2012.

L. Breiman, Bagging predictors, Machine learning, vol.24, issue.2, pp.123-140, 1996.

A. Lahouar and J. B. Slama, Hour-ahead wind power forecast based on random forests, Renewable energy, vol.109, pp.529-541, 2017.

T. K. Ho, Random decision forests, Proceedings of 3rd international conference on document analysis and recognition, vol.1, pp.278-282, 1995.

Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of computer and system sciences, vol.55, issue.1, pp.119-139, 1997.

J. H. Friedman, Greedy function approximation: a gradient boosting machine, Annals of statistics, pp.1189-1232, 2001.

J. Wu, B. Zhang, and K. Wang, Application of adaboost-based bp neural network for short-term wind speed forecast, Power System Technology, vol.36, issue.9, pp.221-225, 2012.

Y. Ren, X. Qiu, and P. N. Suganthan, Empirical mode decomposition based adaboostbackpropagation neural network method for wind speed forecasting, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL), pp.1-6, 2014.

J. P. Heinermann, Wind power prediction with machine learning ensembles, 2016.

M. Landry, T. P. Erlinger, D. Patschke, and C. Varrichio, Probabilistic gradient boosting machines for gefcom2014 wind forecasting, International Journal of Forecasting, vol.32, issue.3, pp.1061-1066, 2016.

T. Hong, P. Pinson, and S. Fan, Global energy forecasting competition 2012, 2014.

B. Gorman, A kaggler's guide to model stacking in practice, 2016.

B. Himmetoglu, Stacking models for improved predictions, 2016.

M. Abkar and F. Porté-agel, A new wind-farm parameterization for large-scale atmospheric models, Journal of Renewable and Sustainable Energy, vol.7, issue.1, pp.13-121, 2015.

M. L. Aitken, B. Kosovi?, J. D. Mirocha, and J. K. Lundquist, Large eddy simulation of wind turbine wake dynamics in the stable boundary layer using the weather research and forecasting model, Journal of Renewable and Sustainable Energy, vol.6, issue.3, pp.33-137, 2014.

M. Calaf, C. Meneveau, and J. Meyers, Large eddy simulation study of fully developed wind-turbine array boundary layers, Physics of fluids, vol.22, issue.1, pp.15-110, 2010.

M. J. Churchfield, S. Lee, J. Michalakes, and P. J. Moriarty, A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics, Journal of turbulence, issue.13, p.14, 2012.

V. Sharma, M. Calaf, M. Lehning, and M. Parlange, Time-adaptive wind turbine model for an les framework, Wind Energy, vol.19, issue.5, pp.939-952, 2016.

A. Jimenez, A. Crespo, E. Migoya, and J. Garc?a, Advances in large-eddy simulation of a wind turbine wake, Journal of Physics: Conference Series, vol.75, pp.12-041, 2007.

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.

R. Girard, K. Laquaine, and G. Kariniotakis, Assessment of wind power predictability as a decision factor in the investment phase of wind farms, Applied Energy, vol.101, pp.609-617, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00734082

P. Trombe, P. Pinson, and H. Madsen, A general probabilistic forecasting framework for offshore wind power fluctuations, Energies, vol.5, issue.3, pp.621-657, 2012.

F. Davò, S. Alessandrini, S. Sperati, L. Delle-monache, D. Airoldi et al., Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting, Solar Energy, vol.134, pp.327-338, 2016.

J. R. Andrade and R. J. Bessa, Improving renewable energy forecasting with a grid of numerical weather predictions, IEEE Transactions on Sustainable Energy, vol.8, issue.4, pp.1571-1580, 2017.

M. Chailles, Vents dominants, 2019.

J. Ferber, Multi-agent systems: an introduction to distributed artificial intelligence, vol.1, 1999.

G. Weiss, Multiagent systems: a modern approach to distributed artificial intelligence, 1999.

G. D. Serugendo, M. Gleizes, and A. Karageorgos, Self-organising Software, 2011.

D. Weyns, H. V. Parunak, F. Michel, T. Holvoet, and J. Ferber, Environments for multiagent systems state-of-the-art and research challenges, International Workshop on Environments for Multi-Agent Systems, pp.1-47, 2004.
URL : https://hal.archives-ouvertes.fr/lirmm-00106417

S. J. Russell and P. Norvig, Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited, 2016.

J. Lind, Iterative software engineering for multiagent systems: the MASSIVE method, 2001.

C. Paniah, Approche multi-agents pour la gestion des fermes éoliennes offshore, vol.11, 2015.

R. Roche, F. Lauri, B. Blunier, A. Mirao, and A. Koukam, Chapter 16 multi-agent technology for power system control, 2012.

R. Roche, B. Blunier, A. Miraoui, V. Hilaire, and A. Koukam, Multi-agent systems for grid energy management: a short review, IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society, pp.3341-3346, 2010.

L. Hernández, C. Baladron, J. M. Aguiar, B. Carro, A. Sanchez-esguevillas et al., A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants, IEEE Communications Magazine, vol.51, issue.1, pp.106-113, 2013.

P. Vytelingum, T. D. Voice, S. D. Ramchurn, A. Rogers, and N. R. Jennings, Agentbased micro-storage management for the smart grid, Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, vol.1, pp.39-46, 2010.

S. D. Ramchurn, P. Vytelingum, A. Rogers, and N. Jennings, Agent-based control for decentralised demand side management in the smart grid, The 10th International Conference on Autonomous Agents and Multiagent Systems, vol.1, pp.5-12, 2011.

A. González-briones, P. Chamoso, F. De-la-prieta, Y. Demazeau, and J. Corchado, Agreement technologies for energy optimization at home, Sensors, vol.18, issue.5, p.1633, 2018.

M. Sahnoun, D. Baudry, N. Mustafee, A. Louis, P. A. Smart et al., Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system, Journal of Intelligent Manufacturing, pp.1-17, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01248095

M. Kpakpo, M. Itmi, and A. Cardon, A MAS model approach to a wind farm maintenance strategy, Proceedings of the 10th International Conference on Agents and Artificial Intelligence, ICAART 2018, vol.1, pp.159-167, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02383142

A. Zaher and S. Mcarthur, A multi-agent fault detection system for wind turbine defect recognition and diagnosis, IEEE Lausanne Power Tech, pp.22-27, 2007.

J. Albouys-perrois, N. Sabouret, Y. Haradji, M. Schumann, and C. Inard, Simulation multi-agent de l'autoconsommation collective en relation avec l'activité des foyers, Journées Francophones sur les Systèmes Multi-Agents, pp.116-125, 2019.

P. A. Frensch and J. Funke, Definitions, traditions, and a general framework for understanding complex problem solving, 1995.

J. Funke, Complex problem solving: a case for complex cognition?, Cognitive processing, vol.11, issue.2, pp.133-142, 2010.

J. C. Polkinghorne, Reductionism, Interdisciplinary Encyclopedia of Religion and Science, p.10, 2002.

J. Ladyman, J. Lambert, and K. Wiesner, What is a complex system?, European Journal for Philosophy of Science, vol.3, issue.1, pp.33-67, 2013.

T. , D. Wolf, and T. Holvoet, Emergence and self-organisation: a statement of similarities and differences, Proceedings of the International Workshop on Engineering Self-Organising Applications, pp.96-110, 2004.

V. Camps, M. Gleizes, and P. Glize, A self-organization process based on cooperation theory for adaptive artificial systems, Problems of Evolution in Real and Virtual Systems: Proceedings of the First International Conference on Philosophy and Computer Science, p.10, 1998.

N. R. Jennings, Agent-oriented software engineering, European Workshop on Modelling Autonomous Agents in a Multi-Agent World, pp.1-7, 1999.

P. Glize, L'adaptation des systèmes à fonctionnalité émergente par auto-organisation coopérative, 2001.

V. Noël and F. Zambonelli, Methodological guidelines for engineering selforganization and emergence, Software Engineering for Collective Autonomic Systems, pp.355-378, 2015.

J. Georgé, Résolution de problèmes par émergence: étude d'un environnement de programmation émergente, vol.3, 2004.

M. Gleizes, V. Camps, and P. Glize, A theory of emergent computation based on cooperative self-organization for adaptive artificial systems, Fourth European Congress of Systems Science, pp.20-24, 1999.

J. Georgé, B. Edmonds, and P. Glize, Making self-organising adaptive multiagent systems work, Methodologies and Software Engineering for Agent Systems, pp.321-340, 2004.

N. Bonjean, W. Mefteh, M. P. Gleizes, C. Maurel, and F. Migeon, Adelfe 2.0", in Handbook on agent-oriented design processes, pp.19-63, 2014.

G. Picard, Méthodologie de développement de systèmes multi-agents adaptatifs et conception de logiciels à fonctionnalité émergente, 2004.

N. Bonjean, Ingénierie des systèmes multi-agents adaptatifs: vers un guide pour la conception du comportement d'agent coopératif, 2009.

M. Gleizes, Self-adaptive complex systems, European Workshop on Multi-Agent Systems, pp.114-128, 2011.

D. Capera, Systèmes multi-agents adaptatifs pour la résolution de problãmes application à la conception de mécanismes, 2005.

J. Nigon, E. Glize, D. Dupas, F. Crasnier, and J. Boes, Use cases of pervasive artificial intelligence for smart cities challenges, 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, pp.1021-1027, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01484992

J. Nigon, Apprentissage artificiel adapté aux systèmes complexes par autoorganisation coopérative de systèmes multi-agents, 2017.

J. Georgé, M. Gleizes, P. Glize, and C. Régis, Real-time simulation for flood forecast: an adaptive multi-agent system staff, Proceedings of the AISB, vol.3, pp.109-114, 2003.

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

J. Blanc-rouchossé, A. Blavette, G. Camilleri, and M. Gleizes, Electric vehicles fleet for frequency regulation using a multi-agent system, International Conference on Practical Applications of Agents and Multi-Agent Systems, pp.84-96, 2018.

V. Guivarch, C. Bernon, and M. P. Gleizes, Power optimization by cooling photovoltaic plants as a dynamic self-adaptive regulation problem, in ICAART, pp.276-281, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02559776

D. Guastella, V. Camps, and M. Gleizes, Multi-agent systems for estimating missing information in smart cities, 2019.

M. Landry, T. P. Erlinger, D. Patschke, and C. Varrichio, Probabilistic gradient boosting machines for gefcom2014 wind forecasting, International Journal of Forecasting, vol.32, issue.3, pp.1061-1066, 2016.

G. Ridgeway, The state of boosting, Computing Science and Statistics, pp.172-181, 1999.

A. Perles, F. Crasnier, and J. Georgé, Amak-a framework for developing robust and open adaptive multi-agent systems, International Conference on Practical Applications of Agents and Multi-Agent Systems, pp.468-479, 2018.

J. Vanderplas, Python data science handbook: essential tools for working with data, 2016.

Y. Seity, P. Brousseau, S. Malardel, G. Hello, P. Bénard et al., The arome-france convective-scale operational model, Monthly Weather Review, vol.139, issue.3, pp.976-991, 2011.

. Météo-france, Données publiques, 2019.

B. Lu, Y. Li, X. Wu, and Z. Yang, A review of recent advances in wind turbine condition monitoring and fault diagnosis, 2009 IEEE Power Electronics and Machines in Wind Applications, pp.1-7, 2009.

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, The elements of statistical learning: data mining, inference and prediction, The Mathematical Intelligencer, vol.27, pp.83-85, 2005.

K. Rosaen, K-fold cross-validation, 2016.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: machine learning in python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

, Mlp regressor documentation, 2019.

, Random forest regressor documentation, 2019.

, Gradient boosting regressor documentation, 2019.

, Svr documentation, 2019.

T. Bouziat, A cooperative architecting procedure for systems of systems based on self-adaptive multi-agent systems, 2017.

, World electricity generation mix, 2018.

, Atmospheric CO 2 concentration estimated from gas microbubbles trapped in ice cores [15]

. .. , French primary energy balance of the negaWatt scenario [16], p.14

]. .. , Global trends in renewable energy investment, p.16

]. .. , 20 2.1 Representation of the three main convective cells: Polar, Ferrel and Hadley cells [31]

, Example of wind speed distribution [34]

. .. , Difference of wind profiles according to topography [36], p.28

. .. , , p.30

]. .. , 30 2.8 (a) Windmill, (b) Wind water pump, (c) Darrieus, (d) Savonius, (e) Two-blade turbine, (f) Offshore, (g) Floating, (h) Standard

. .. , Hysteresis phenomenon applied to wind generation, p.36

. .. , Physical approach process for the wind power forecasting, p.43

, List of Figures 2.16 Comparison between the production of two close and distant wind turbines in the same wind farm

. .. , 58 3.2 Comparison of the production between two close (left) and distant turbines (right), The layout of a wind farm consisting of 15 wind turbines

, Pearson correlation coefficient between the production of each turbine in a wind farm over three years of data

, Comparison of production between two neighboring turbines T1 and T2, p.59

, Location of the five wind farms studied, p.61

, Pearson correlation coefficient between the production of the five French wind farms over three years of data, p.61

W. T. Links-between, . Gp, and . .. Wth/gph-entities, 76 4.2 Architecture of the AMAWind-Turbine system and relationships between entities and agents

, Neighborhood between WTH agents for an example of a layout, p.78

, Example of a non cooperative situation of type Uselessness, p.81

, Example of a non cooperative situation of type Incompetence, p.82

, Friedman's gradient boosting algorithm [173]

, Example of probabilistic wind power forecast divided into percentiles, p.84

. .. , 85 4.10 Example of probabilistic wind power forecast difference between two wind turbines divided into percentiles

, Neighboring criticality functions example, vol.87

. .. Amak, 88 4.14 Class diagram representing the links between entities and agents in AMAWind-Turbine

F. Links-between, . Gp, and . .. Fh/gph-entities, 93 5.2 Architecture of the AMAWind-Farm system and relationships between entities and agents

. .. Agents, 94 5.4 Class diagram representing the links between entities and agents in AMAWind-Farm, 3 Neighborhood of Farm Hour (FH)

, The layout of the five studied wind farms at the same scale, p.103

. .. , 103 6.3 Distribution of the measured wind and production of one wind turbine in each farm, Power curves of the wind turbines of the five wind farms studied

, Power curve of the wind farm C with final and filtered records, p.106

]. .. , 108 7.1 Forecast and criticality evolution, as functions of the cycle number, on the fifteen turbines of the farm A

, 116 7.4 Relationship between error improvement and criticality improvement (average over 5% intervals)

, Distribution histograms of number of cycles on AMAWind-Turbine, p.118

, Criticality and forecast evolution as a function of the cycles on five Farm Hour agents

, The four scenarios tested and the factors ? applied to subcriticalities, p.130

, Criticality evolution during the operation of the AMAWind-Farm system, p.132

, Relation between error improvement and criticality improvement on AMAWind-Farm (average over 5% intervals)

. .. , World electricity production by renewable source [12], p.15

. .. , The different types of wind power forecasts according to time scale, p.41

, Assessment of the criteria for physical models

, Assessment of the criteria for statistical models

, Wind power forecasting methods summary

, Characteristics of the wind farms studied

, Size of wind farm production history before and after filtering, p.106

, Comparison of computation time according to the initial forecast used (HH:MM), vol.114

, 2 Summary of computation times for each farm on AMAWind-Turbine, p.114

, Average first and last cycle criticality for each farm

. .. , Results in terms of NMAE and NRMSE for the five farms, p.122

, Detailed results for each subset of the 10-fold cross-validation (NMAE), p.123

, Detailed results for each subset of the 10-fold cross validation (NRMSE), p.124

. .. , 130 8.2 Comparison of computation times on AMAWind-Farm according to the initial forecast used (HH:MM)

A. First and . .. Last-cycle-criticality-on-amawind-farm, , p.132

.. .. Results-summary-on-amawind-farm,

. .. , Detailed results on AMAWind-Farm in terms of NMAE (%), p.135

. .. , Detailed results on AMAWind-Farm in terms of NRMSE (%), p.136