深度强化学习对城市水系统的挑战和机遇
Ahmed Negm1, Xiandong Ma1, George Aggidis1
1Lancaster University Energy Group, School of Engineering, Lancaster LA1 4YW, UK.
Water research
|February 8, 2024
概括
深度强化学习 (DRL) 为复杂的城市水系统 (UWS) 提供了创新的解决方案. 这项技术可以通过解决诸如基础设施老化和水损失等挑战来改善水资源管理.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 城市水系统 (UWS) 对文明至关重要,但面临着诸如基础设施老化,短暂流动和水损失等挑战.
- 城市化和气候变化的增加加剧了管理UWS的复杂性.
- 需要创新的解决方案来确保可持续的供应和运输的水.
研究的目的:
- 探索深度强化学习 (DRL) 在彻底改变UWS管理方面的潜力.
- 提供DRL应用的新审查,特别是在水分网和雨水系统内.
- 提供关键的见解,了解DRL如何为城市水系统的各个方面带来好处.
主要方法:
- 深度强化学习 (DRL) 背景和里程碑的解释.
- 使用一种新的分类法来对DRL算法进行分类.
- 在UWS中对DRL应用进行新的审查,重点是水分配和雨水系统.
主要成果:
- DRL利用深度神经网络进行复杂的函数近似和表示,在各种领域证明有效.
- 审查强调了DRL在解决UWS复杂性的变革潜力.
- 研究了在水分和雨水系统中的特定应用.
结论:
- DRL提出了一种强有力的方法来应对城市水资源管理中长期存在的挑战.
- 该技术可以显著提高UWS的效率,可持续性和弹性.
- 进一步研究和实施DRL对于未来的水资源管理至关重要.
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