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Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

  • Tiago M. SousaEmail author
  • Tiago Pinto
  • Isabel Praça
  • Zita Vale
  • Hugo Morais
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 290)

Abstract

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Keywords

Bayesian Network Success Probability Multiagent System Electricity Market Dynamic Bayesian Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tiago M. Sousa
    • 1
    Email author
  • Tiago Pinto
    • 1
  • Isabel Praça
    • 1
  • Zita Vale
    • 1
  • Hugo Morais
    • 2
  1. 1.GECAD – Knowledge Engineering and Decision-Support Research Center, Institute of EngineeringPolitechnic of Porto (ISEP/IPP)PortoPortugal
  2. 2.Automation and Control GroupTechnical University of DenmarkCopenhagenDenmark

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