从2012年到2019年,中国280个城市的1公里×1公里电网每月电力消耗数据
Xiaoqin Yan1,2, Zhou Huang3,4, Shuliang Ren1,2
1Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, China.
Scientific data
|August 13, 2024
概括
这项研究开发了一种新的机器学习方法,用于估计中国城市在1公里电网分辨率下消耗的电力. 改进的数据支持更好的能源转型战略和可持续能源政策.
科学领域:
- 环境科学 环境科学
- 能源政策 能源政策
- 数据科学数据科学数据科学
背景情况:
- 准确的电力消耗数据对于能源转型战略至关重要.
- 现有的数据缺乏高时空分辨率和有效的多源特征融合.
- 复杂的时空异质性挑战了数据的可用性.
研究的目的:
- 开发一种创新的缩放方法,用于高时空分辨率的电能消耗估计.
- 通过多源数据集成和机器学习来解决数据限制.
- 为城市能源政策制定提供一个强大的数据集.
主要方法:
- 结合多源数据与机器学习和空间插值技术.
- 开发了一种创新的电力消耗缩放模型.
- 使用两个评估数据集验证模型,并与官方行业数据进行比较.
主要成果:
- 与基线模型相比,实现了显著的准确性改进 (30.1%和33.4%的R2增加).
- 在280个中国城市 (2012-2019) 的1公里×1公里电网上生成的每月电力消耗数据.
- 与官方数据 (皮尔森相关性0.83-0.91) 和捕获的城市土地使用模式显示出高度一致性.
结论:
- 开发的方法有效地弥合了细粒度的电力消耗数据的差距.
- 生成的数据集为制定可持续能源政策提供了可靠的基础.
- 该方法提高了对城市能源消耗动态的理解.
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