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适应性强化学习框架,用于干旱的城市环境中可持续的微电网优化

Mohamed Ahmed Said Mohamed1, Khaled Almazam2, Mohammed Alzahrani3

  • 1Department of Architectural Engineering, University of Hail, Hail, Kingdom of Saudi Arabia.

Scientific reports
|February 5, 2026
PubMed
概括

本研究介绍了城市微电网的自适应强化学习 (RL) 框架,优化各种能源来源. 该系统在干旱气候中显著减少了排放和能源消耗.

关键词:
成本效益 成本效益 成本效益能源管理 能源管理微电网优化的优化强化学习是一种强化学习.可再生能源是可再生的能源.

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