Abstract
In the coming years, ensuring the electricity supply will be one of the most important world challenges. Renewable energies, in particular wind energy, are an alternative to non-sustainable resources thanks to their almost unlimited supply. However, the chaotic nature and the variability of the wind represent a significant barrier to a large-scale development of this energy. Consequently, providing accurate wind power forecasts is a crucial challenge. This paper presents AMAWind, a multi-agent system dedicated to wind power forecasting based on a cooperative approach. Each agent corresponds to a turbine at a given hour, it starts from an initial production forecast and acts in a cooperative way with its neighbors to find an equilibrium on conflicting values. An assessment of this approach was carried out on data coming from a real wind farm.
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References
Bernon, C., Gleizes, M.-P., Peyruqueou, S., Picard, G.: ADELFE: a methodology for adaptive multi-agent systems engineering. In: Petta, P., Tolksdorf, R., Zambonelli, F. (eds.) ESAW 2002. LNCS (LNAI), vol. 2577, pp. 156–169. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-39173-8_12
Boes, J., Migeon, F.: Self-organizing multi-agent systems for the control of complex systems. J. Syst. Softw. 134, 12–28 (2017)
Browell, J., Gilbert, C., McMillan, D.: Use of turbine-level data for improved wind power forecasting. In: 2017 IEEE Manchester PowerTech, pp. 1–6 (2017)
Burton, T., Jenkins, N., Sharpe, D., Bossanyi, E.: Wind Energy Handbook. Wiley, Hoboken (2011)
Capera, D., Georgé, J.P., Gleizes, M.P., Glize, P.: The AMAS theory for complex problem solving based on self-organizing cooperative agents. In: 12th IEEE International Workshops on Enabling Technologies, Infrastructure for Collaborative Enterprises, Linz, Austria, pp. 383–388. IEEE Computer Society (2003)
Dickerson, M.T., Drysdale, R.S.: Fixed-radius near neighbors search algorithms for points and segments. Inf. Process. Lett. 35(5), 269–273 (1990)
Georgé, J.P., Gleizes, M.P., Camps, V.: Cooperation. In: Di Marzo Serugendo, G., Gleizes, M.P., Karageorgos, A. (eds.) Self-organising Software: From Natural to Artificial Adaptation, pp. 193–226. Springer, Cham (2011). https://doi.org/10.1007/978-3-642-17348-6
Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M., Draxl, C.: The state-of-the-art in short-term prediction of wind power: a literature overview. Technical report, ANEMOS.plus (2011)
Giebel, G., Cline, J., Frank, H., Shaw, W., Pinson, P., Hodge, B.M., Kariniotakis, G., Madsen, J., Möhrlen, C.: Wind power forecasting: IEA Wind Task 36 & future research issues. J. Phys.: Conf. Ser. 753, 032042 (2016)
Guivarch, V., Bernon, C., Gleizes, M.P.: Power optimization by cooling photovoltaic plants as a dynamic self-adaptive regulation problem. In: International Conference on Agents and Artificial Intelligence (ICAART), vol. 1, pp. 276–281. SciTePress (2018)
Hong, T., Pinson, P., Fan, S.: Global Energy Forecasting Competition 2012 (2014)
Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., Hyndman, R.J.: Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond (2016)
Jung, J., Broadwater, R.P.: Current status and future advances for wind speed and power forecasting. Renew. Sustain. Energy Rev. 31, 762–777 (2014)
Landry, M., Erlinger, T.P., Patschke, D., Varrichio, C.: Probabilistic gradient boosting machines for GEFCom2014 wind forecasting. Int. J. Forecast. 32(3), 1061–1066 (2016)
Lydia, M., Kumar, S.S., Selvakumar, A.I., Kumar, G.E.P.: A comprehensive review on wind turbine power curve modeling techniques. Renew. Sustain. Energy Rev. 30, 452–460 (2014)
Monteiro, C., Bessa, R., Miranda, V., Botterud, A., Wang, J., Conzelmann, G., et al.: Wind power forecasting: state-of-the-art 2009. Technical report, Argonne National Laboratory (ANL) (2009)
Nygaard, N.G.: Wakes in very large wind farms and the effect of neighbouring wind farms. J. Phys. Conf. Ser. 524(1), 012162 (2014)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(10), 2825–2830 (2011)
Perles, A.: An adaptive multi-agent system for the distribution of intelligence in electrical distribution networks: state estimation. Ph.D. thesis, Université de Toulouse (2017)
Pinson, P.: Wind energy: forecasting challenges for its operational management. Stat. Sci. 28(4), 564–585 (2013)
Ramasamy, P., Chandel, S., Yadav, A.K.: Wind speed prediction in the mountainous region of India using an artificial neural network model. Renew. Energy 80, 338–347 (2015)
Verstaevel, N.: Self-organization of robotic devices through demonstrations. Ph.D. thesis, Université de Toulouse (2016)
Wang, H., Li, G., Wang, G., Peng, J., Jiang, H., Liu, Y.: Deep learning based ensemble approach for probabilistic wind power forecasting. Appl. Energy 188, 56–70 (2017)
Wind Observatory: Analysis of the wind power market, wind jobs and future of the wind industry in France. Technical report (2017)
Acknowledgements
This work is part of the research project Meteo*Swift funded by the ERDF (European Regional Development Fund) of the European Union and the French Occitanie Region and supported by the ANRT (French National Association for Research and Technology). We would also like to thank the CNRM (French Weather Research Centre), our partner in this project.
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Esteoule, T., Perles, A., Bernon, C., Gleizes, MP., Barthod, M. (2018). A Cooperative Multi-Agent System for Wind Power Forecasting. In: Demazeau, Y., An, B., Bajo, J., Fernández-Caballero, A. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Lecture Notes in Computer Science(), vol 10978. Springer, Cham. https://doi.org/10.1007/978-3-319-94580-4_12
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