Distributed Learning in Hierarchical Networks
Résumé
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.
Mots clés
online learning
Distributed Learning
Coalition
Algorithmic Game Theory
machine learning
control engineering computing
hierarchical systems
optimal control
Regret
power system control
power system management
renewable energy sources
smart power grid
smart grid
smart grid management
optimal control strategy
competitive learning algorithm
energy forecasting
artificial intelligence
power engineering computing
distributed learning approach
decentralized hierarchical network system
nonrenewable energy production control
renewable energy integration control
Origine : Fichiers produits par l'(les) auteur(s)
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