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Deep-Fuzzy Logic Control for Optimal Energy Management: A Predictive and Adaptive Framework for Grid-Connected Microgrids

Author

Listed:
  • Muhammed Cavus

    (Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle Upon Tyne NE1 8SA, UK)

  • Dilum Dissanayake

    (School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK)

  • Margaret Bell

    (School of Engineering, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK)

Abstract

This paper introduces a novel energy management framework, Deep-Fuzzy Logic Control (Deep-FLC), which combines predictive modelling using Long Short-Term Memory (LSTM) networks with adaptive fuzzy logic to optimise energy allocation, minimise grid dependency, and preserve battery health in grid-connected microgrid (MG) systems. Integrating LSTM-based predictions provides foresight into system parameters such as state of charge, load demand, and battery health, while fuzzy logic ensures real-time adaptive control. Results demonstrate that Deep-FLC achieves a 25.7% reduction in operational costs compared to the conventional system and a 17.5% saving cost over the Fuzzy Logic Control (FLC) system. Additionally, Deep-FLC delivers the highest battery efficiency of 61% and constraints depth of discharge to below 2% per time step, resulting in a reduction of the state of health degradation to less than 0.2% over 300 h. By combining predictive analytics with adaptive control, this study addresses the limitations of standalone approaches and establishes Deep-FLC as a robust, efficient, and sustainable energy management solution. Key novel contributions include the integration of advanced prediction mechanisms with fuzzy control and its application to battery-integrated grid-connected MG systems.

Suggested Citation

  • Muhammed Cavus & Dilum Dissanayake & Margaret Bell, 2025. "Deep-Fuzzy Logic Control for Optimal Energy Management: A Predictive and Adaptive Framework for Grid-Connected Microgrids," Energies, MDPI, vol. 18(4), pages 1-25, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:995-:d:1594274
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    References listed on IDEAS

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