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Designing a Fuzzy Q-Learning Power Energy System Using Reinforcement Learning

Author

Listed:
  • Avanija J.

    (Sree Vidyanikethan Engineering College, India)

  • Suneetha Konduru

    (Jain University (Deemed), India)

  • Vijetha Kura

    (Matrusri Engineering College, India)

  • Grande NagaJyothi

    (Golden Valley Integrated Campus, India)

  • Bhanu Prakash Dudi

    (CVR College of Engineering, India)

  • Mani Naidu S.

    (Vel Tech, Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India)

Abstract

Modern power and energy systems are becoming more complicated and uncertain as distributed energy resources (DERs), flexible loads, and other developing technologies become more integrated. This brings great challenges to the operation and control. Furthermore, the deployment of modern sensor and smart metres generates a considerable amount of data, which opens the door to fresh data-driven ways for dealing with complex operation and control difficulties. One of the most commonly touted strategies for control and optimization problems is reinforcement learning (RL). Designing a fuzzy Q-learning power energy system using RL technique will control and reduce the problems arranging in the energy system.

Suggested Citation

  • Avanija J. & Suneetha Konduru & Vijetha Kura & Grande NagaJyothi & Bhanu Prakash Dudi & Mani Naidu S., 2022. "Designing a Fuzzy Q-Learning Power Energy System Using Reinforcement Learning," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 11(3), pages 1-12, July.
    • Handle: RePEc:igg:jfsa00:v:11:y:2022:i:3:p:1-12
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