Scholarlink

<p>Integrating machine learning (ML) into Hybrid Renewable Energy Systems (HRES) promises to boost energy efficiency and grid stability in decentralized smart grids. Techniques like predictive analytics and reinforcement learning optimize resource allocation and real-time decision-making, yet challenges such as data quality, integration complexity, and high costs impede broad adoption. This review explores ML’s role in optimizing HRES for decentralized grids, pinpointing key obstacles and proposing solutions through interdisciplinary collaboration; spanning engineering, data science, and policy—and targeted policy measures. A systematic literature review analyzed studies and case reports from 2010 to 2023, focusing on ML applications like demand forecasting, energy management, and predictive maintenance in HRES, with metrics like energy efficiency and grid stability evaluated across diverse case studies. ML-driven optimization yielded notable gains, including a 20% reduction in energy wastage and a 15–25% boost in grid stability, as evidenced by Stanford University’s solar campus and the UK National Grid, enhancing energy flows and decision-making in decentralized systems. ML holds immense potential for HRES optimization, but practical deployment hinges on overcoming data and integration barriers. Collaboration between engineers, data scientists, and policymakers can refine ML models, while policies like tax incentives for digital infrastructure and standardized data protocols can address real-world challenges—such as inconsistent datasets and scalability—fostering sustainable, resilient energy systems. Future research should prioritize robust ML models and practical implementation frameworks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Review of Machine Learning Approaches for Optimizing Hybrid Renewable Energy Systems (HRES) in Decentralized Smart Grids: Enhancing Energy Efficiency and Grid Stability

  • 期刊论文

  • 发表日期：

    2025年10月23日

查看全文
  • Asif Ahamed,
  • Hasib Fardin,
  • Ekramul Hasan,
  • S. M. Tamim Hossain Rimon,
  • Md Musa Haque

摘要

Integrating machine learning (ML) into Hybrid Renewable Energy Systems (HRES) promises to boost energy efficiency and grid stability in decentralized smart grids. Techniques like predictive analytics and reinforcement learning optimize resource allocation and real-time decision-making, yet challenges such as data quality, integration complexity, and high costs impede broad adoption. This review explores ML’s role in optimizing HRES for decentralized grids, pinpointing key obstacles and proposing solutions through interdisciplinary collaboration; spanning engineering, data science, and policy—and targeted policy measures. A systematic literature review analyzed studies and case reports from 2010 to 2023, focusing on ML applications like demand forecasting, energy management, and predictive maintenance in HRES, with metrics like energy efficiency and grid stability evaluated across diverse case studies. ML-driven optimization yielded notable gains, including a 20% reduction in energy wastage and a 15–25% boost in grid stability, as evidenced by Stanford University’s solar campus and the UK National Grid, enhancing energy flows and decision-making in decentralized systems. ML holds immense potential for HRES optimization, but practical deployment hinges on overcoming data and integration barriers. Collaboration between engineers, data scientists, and policymakers can refine ML models, while policies like tax incentives for digital infrastructure and standardized data protocols can address real-world challenges—such as inconsistent datasets and scalability—fostering sustainable, resilient energy systems. Future research should prioritize robust ML models and practical implementation frameworks.