Distributed Learning in Hierarchical Networks
AbstractIn 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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Bibliographic InfoPaper provided by HAL in its series Post-Print with number hal-00740905.
Date of creation: 09 Oct 2012
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Publication status: Published - Presented, ValueTools 2012, 2012, Cargèse, France
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Algorithmic Game Theory; Coalition; Distributed Learning; Regret;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-10-20 (All new papers)
- NEP-CMP-2012-10-20 (Computational Economics)
- NEP-CSE-2012-10-20 (Economics of Strategic Management)
- NEP-ENE-2012-10-20 (Energy Economics)
- NEP-FOR-2012-10-20 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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