使用机器学习模型对部门电力消耗和城市碳度的动态相关性分析
Yufei Teng1, Han Zhang2, Yu Zhan3
1Power Internet of Things Key Laboratory of Sichuan Province, State Grid Sichuan Electric Power Research Institute, Chengdu, 610095, China.
Scientific reports
|March 23, 2025
概括
机器学习模型揭示了工业用电量与城市二氧化碳 (CO2) 水平之间的动态联系. 这项研究为打击气候变化的有针对性的碳减排政策提供了见解.
科学领域:
- 环境科学 环境科学
- 气候变化研究 气候变化研究
- 数据科学数据科学数据科学
背景情况:
- 全球气候变化是由二氧化碳 (CO2) 排放所驱动的,这给环境带来了重大挑战.
- 实现碳中和需要了解和减少各种行业的排放,但进展仍然不确定.
研究的目的:
- 开发一个动态相关模型,将部门的电力消耗与城市二氧化碳柱度 (XCO2) 联系起来.
- 通过先进的机器学习,探索特定行业的电力使用和大气二氧化碳水平之间的时间关系.
主要方法:
- 采用机器学习算法:随机森林,极端梯度增强 (XGBoost) 和堆叠回归.
- 集成的时间滚动窗口技术以动态评估相关性.
- 使用16个城市 (2017-2021) 的数据验证了该模型的部门电力消耗和城市XCO2.
主要成果:
- 动态相关模型实现了高精度 (R2高达0.864) 和低误差 (RMSE1.350).
- 该模型在捕捉复杂关系方面显著优于传统方法.
- 揭示了城市二氧化碳度的显著时间波动和特定部门的影响.
结论:
- 先进的机器学习模型可以有效地捕捉部门电力消耗和城市CO2之间的动态相关性.
- 调查结果为制定精确,针对行业的碳减排战略提供了关键的见解.
- 该研究强调了动态分析对于有效的气候变化减缓政策的重要性.
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