6th International Conference on Performance Evaluation Methodologies and Tools

Research Article

Distributed Learning in Hierarchical Networks

Download411 downloads
  • @INPROCEEDINGS{10.4108/valuetools.2012.250217,
        author={H\^{e}l\'{e}ne Le Cadre and Jean-S\^{e}bastien Bedo},
        title={Distributed Learning in Hierarchical Networks},
        proceedings={6th International Conference on Performance Evaluation Methodologies and Tools},
        publisher={IEEE},
        proceedings_a={VALUETOOLS},
        year={2012},
        month={11},
        keywords={algorithmic game theory coalition distributed learning regret},
        doi={10.4108/valuetools.2012.250217}
    }
    
  • Hélène Le Cadre
    Jean-Sébastien Bedo
    Year: 2012
    Distributed Learning in Hierarchical Networks
    VALUETOOLS
    ICST
    DOI: 10.4108/valuetools.2012.250217
Hélène Le Cadre1,*, Jean-Sébastien Bedo2
  • 1: Institut CEA-LIST
  • 2: Orange/France-Télécom
*Contact email: helene.lecadre@gmail.com

Abstract

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 integration 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.