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Distributed Learning in Hierarchical Networks

  • Hélène Le Cadre

    ()

    (Laboratoire Information, Modèles, Apprentissage [Gif-sur-Yvette] - CEA)

  • Jean-Sébastien Bedo

    ()

    (Orange Labs [Paris] - Telecom Orange)

Registered author(s):

    In this article, we propose distributed learning based approaches to study the evolution of a decentralized hierarchical system, an illustration of which is the smart grid. Smart grid management requires the control of non-renewable energy production and the inegration of renewable energies which might be highly unpredictable. Indeed, their production levels rely on uncontrolable factors such as sunshine, wind strength, etc. First, we derive optimal control strategies on the non-renewable energy productions and compare competitive learning algorithms to forecast the energy needs of the end users. Second, we introduce an online learning algorithm based on regret minimization enabling the agents to forecast the production of renewable energies. Additionally, we define organizations of the market promoting collaborative learning which generate higher performance for the whole smart grid than full competition.

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    Date of creation: 09 Oct 2012
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    Handle: RePEc:hal:journl:hal-00740905
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-00740905
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    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. Drew Fudenberg & David K. Levine, 1996. "The Theory of Learning in Games," Levine's Working Paper Archive 624, David K. Levine.
    3. Young, H. Peyton, 2009. "Learning by trial and error," Games and Economic Behavior, Elsevier, vol. 65(2), pages 626-643, March.
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