城市旅行碳排放减缓方法使用深度强化学习
Jie Shen1, Fanghao Zheng2, Yuanli Ma3
1Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China.
Scientific reports
|November 13, 2024
概括
本研究介绍了一种深度强化学习 (DRL) 战略,用于减少城市碳排放. 该模型优化了城市规划,在宁波市实现了显著的旅行碳排放减排.
科学领域:
- 城市规划与环境科学
- 人工智能和机器学习
背景情况:
- 城市化推动了能源消耗和碳排放的增加.
- 有效的城市规划和管理对于减轻这些环境影响至关重要.
研究的目的:
- 通过深度强化学习 (DRL) 提出一个自下而上的城市碳排放减缓战略.
- 优化土地利用和建筑配置,以减少与旅行相关的碳排放.
主要方法:
- 利用了宁波市的多源城市数据 (景点,交通系统).
- 开发了一个具有Actor-Critic框架的DRL模型,以优化城市配置.
- 包含不同旅行模式的不同碳排放系数.
主要成果:
- 在城市场景中,DLR方法表明了显著的碳减排效应.
- 调整奖励函数的折扣率产生了各种优化策略 (短期和长期).
- 实现了0.47% (短期) 和0.61% (长期) 的减排,超过了统一分布的预期 (0.39%).
结论:
- 基于DRL的方法为城市碳排放减缓提供了适应性和数据驱动的策略.
- 这项研究强调了人工智能在优化环境可持续性城市规划方面的潜力.
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