使用机器学习绘制住宅电力不平等的空间时间差异
Ying Yu1, Xijing Li2,3, Angel Hsu1
1Data-Driven EnviroLab, Department of Public Policy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.
Environmental science & technology
|October 30, 2024
概括
电气化是脱碳的关键,但有可能导致能源负担不起. 这项研究使用机器学习绘制电力不平等的地图,并确定脆弱的社区,揭示了重要的季节性和农村负担.
科学领域:
- 能源政策 能源政策
- 环境科学环境科学
- 数据科学是数据科学.
背景情况:
- 通过电气化实现能源部门的脱碳是必不可少的.
- 如果没有适当的保障措施,能源负担能力可能会增加.
- 了解社区规模的住宅电力不平等至关重要.
研究的目的:
- 开发一个高分辨率的,空间时间显式机器学习 (ML) 框架来预测住宅电力消耗.
- 构建电力负担能力差距 (EAG) 度量,以确定能源脆弱的社区.
- 分析能源负担能力的空间和时间差异.
主要方法:
- 利用公开可用的,多来源的遥感和人口普查数据.
- 开发了一种机器学习 (ML) 框架,用于预测通道级月度电力消耗 (2013-2020年).
- 构建了电力负担能力差距 (EAG) 度量 (电费账单与3%的家庭收入).
主要成果:
- 与LEAD数据相比,ML框架提高了电力消耗数据的分辨率,达到0.82的R2.
- 据估计,每年16.18亿美元的经济负担会影响电费负担能力.
- 每月的EAG在夏季/冬季高出2-3倍,农村居民面临的负担高达城市同行1.7倍.
结论:
- 开发的框架增强了对住宅电力不平等的理解.
- 在能源负担能力方面发现了显著的季节性和城乡差异.
- 洞察力可以通过解决时空不匹配问题,为公平的电气化战略和能源正义努力提供信息.
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