IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i4p995-d1594274.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/4/995/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/4/995/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Sheng & Ocłoń, Paweł & Klemeš, Jiří Jaromír & Michorczyk, Piotr & Pielichowska, Kinga & Pielichowski, Krzysztof, 2022. "Renewable energy systems for building heating, cooling and electricity production with thermal energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    2. Fan, Cheng & Lu, Mengyan & Sun, Yongjun & Liang, Dekun, 2024. "A novel linear programming-based predictive control method for building battery operations with reduced cost and enhanced computational efficiency," Renewable Energy, Elsevier, vol. 237(PC).
    3. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    4. Florian Klausmann & Anna-Lena Klingler, 2023. "Adaptive Control Strategy for Stationary Electric Battery Storage Systems with Reliable Peak Load Limitation at Maximum Self-Consumption of Locally Generated Energy," Energies, MDPI, vol. 16(9), pages 1-19, May.
    5. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    6. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    7. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    8. Cavus, Muhammed & Allahham, Adib & Adhikari, Kabita & Giaouris, Damian, 2024. "A hybrid method based on logic predictive controller for flexible hybrid microgrid with plug-and-play capabilities," Applied Energy, Elsevier, vol. 359(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2024. "Real-time automatic control of multi-energy system for smart district community: A coupling ensemble prediction model and safe deep reinforcement learning," Energy, Elsevier, vol. 304(C).
    2. Muthu Kumaran Gunasegaran & Md Hasanuzzaman & ChiaKwang Tan & Ab Halim Abu Bakar & Vignes Ponniah, 2022. "Energy Analysis, Building Energy Index and Energy Management Strategies for Fast-Food Restaurants in Malaysia," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    3. Al-Falahi, Monaaf D.A. & Jayasinghe, Shantha D.G. & Enshaei, Hossein, 2019. "Hybrid algorithm for optimal operation of hybrid energy systems in electric ferries," Energy, Elsevier, vol. 187(C).
    4. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    5. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    6. Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2025. "Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review," Energies, MDPI, vol. 18(6), pages 1-51, March.
    7. Jen Chun Wang & Kuo-Tsang Huang & Meng Yun Ko, 2019. "Using the Fuzzy Delphi Method to Study the Construction Needs of an Elementary Campus and Achieve Sustainability," Sustainability, MDPI, vol. 11(23), pages 1-13, December.
    8. Hannan, M.A. & Ali, Jamal A. & Mohamed, Azah & Hussain, Aini, 2018. "Optimization techniques to enhance the performance of induction motor drives: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1611-1626.
    9. Laura Canale & Anna Rita Di Fazio & Mario Russo & Andrea Frattolillo & Marco Dell’Isola, 2021. "An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings," Energies, MDPI, vol. 14(4), pages 1-33, February.
    10. Mostafayi Darmian, Sobhan & Tavana, Madjid & Ribeiro-Navarrete, Samuel, 2024. "An investment evaluation and incentive allocation model for public-private partnerships in renewable energy development projects," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    11. Ma, Huan & Sun, Qinghan & Chen, Qun & Zhao, Tian & He, Kelun, 2023. "Exergy-based flexibility cost indicator and spatio-temporal coordination principle of distributed multi-energy systems," Energy, Elsevier, vol. 267(C).
    12. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    13. Wenting Zhao & Haoran Xu & Peng Chen & Juan Zhang & Jing Li & Tingting Cai, 2025. "Elastic Momentum-Enhanced Adaptive Hybrid Method for Short-Term Load Forecasting," Energies, MDPI, vol. 18(13), pages 1-25, June.
    14. Wu, Han & Liang, Yan & Heng, Jiani, 2023. "Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting," Applied Energy, Elsevier, vol. 339(C).
    15. Xiaozhi Gao & Han Xiao & Shiwei Xu & Hsiung-Cheng Lin & Pengyu Chang, 2024. "What Is the Optimal Solution for Scheduling Multiple Energy Systems? Overview and Analysis of Integrated Energy Co-Dispatch Models," Energies, MDPI, vol. 17(18), pages 1-25, September.
    16. Diaa Salman & Mehmet Kusaf, 2021. "Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting," Sustainability, MDPI, vol. 13(24), pages 1-22, December.
    17. Pei, Jingyin & Dong, Yunxuan & Guo, Pinghui & Wu, Thomas & Hu, Jianming, 2024. "A Hybrid Dual Stream ProbSparse Self-Attention Network for spatial–temporal photovoltaic power forecasting," Energy, Elsevier, vol. 305(C).
    18. Kong, Xiangfei & Du, Xinyu & Xue, Guixiang & Xu, Zhijie, 2023. "Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism," Energy, Elsevier, vol. 282(C).
    19. Nima Mirzaei, 2022. "A Multicriteria Decision Framework for Solar Power Plant Location Selection Problem with Pythagorean Fuzzy Data: A Case Study on Green Energy in Turkey," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    20. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:995-:d:1594274. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.