• DocumentCode
    495548
  • Title

    Multi-agent Framework for Energy Supply/Demand Prediction

  • Author

    Peng, YiGong ; Lu, ZhongCheng ; Yu, Jinshou

  • Author_Institution
    Coll. of Inf. Sci. & Inf. Eng., East China Univ. of Sci. & Technol., Shanghai, China
  • Volume
    4
  • fYear
    2009
  • fDate
    March 31 2009-April 2 2009
  • Firstpage
    586
  • Lastpage
    590
  • Abstract
    The framework based on multi-agent (MA) is proposed for energy supply/demand prediction, facing to the drawback hard to model energy supply/demand prediction system mathematically and attractions of multi-agent methods used in a complex system. In the proposed framework an intelligent hybrid agent and a MA hierarchical infrastructure of energy supply/ demand prediction are presented. The intelligent hybrid agent is composed of deliberative agent, reflex agent, decision-making module and coordination module, in which deliberative agent is designed for realizing static prediction, reflex agent is for dynamic prediction, decision-making module is used for static prediction or dynamic prediction and coordination module is for communication among agents. Meanwhile, the implementation method for all the agents and modules are discussed respectively. The simulation work shows that this approach is valid to improve the prediction accuracy. Future research efforts will be done to extend its application to energy based complex systems.
  • Keywords
    decision making; energy consumption; multi-agent systems; coordination module; decision-making module; deliberative agent; energy supply-demand prediction; intelligent hybrid agent; multiagent framework; Accuracy; Computer science; Decision making; Educational institutions; Genetic mutations; Information science; Intelligent agent; Mathematical model; Power engineering and energy; Predictive models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Information Engineering, 2009 WRI World Congress on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-0-7695-3507-4
  • Type

    conf

  • DOI
    10.1109/CSIE.2009.1067
  • Filename
    5171063