Autors: Stavrev, S., Ginchev, D.
Title: Reinforcement Learning Techniques in Optimizing Energy Systems
Keywords: energy systems; reinforcement learning; optimization; deep

Abstract: Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further i

References

    Issue

    Electronics, vol. 13, issue 9, 2024, Switzerland, MDPI, https://doi.org/10.3390/electronics13081459

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    Вид: статия в списание, публикация в издание с импакт фактор, индексирана в Scopus