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<p>Timely and spatially refined electricity consumption data are essential for supporting urban energy transition, electricity demand response, and supply-demand balancing strategies. However, publicly accessible datasets with high temporal resolution and city-level granularity remain scarce, particularly in developing countries such as China. In this study, we generate electricity consumption estimate for 296 Chinese cities in 2022 at both daily and monthly resolutions. In addition, this study presents a top-down framework for estimating city-level daily and monthly electricity consumption by integrating open-access multi-source data, including nighttime light imagery and high-resolution meteorological variables. The validation results demonstrate a strong alignment between the estimated multi-resolution values and officially reported annual statistics. This dataset addresses critical data gaps in urban-scale electricity studies by providing a publicly accessible, lower-cost, and scalable proxy for capturing electricity consumption dynamics at both daily and monthly scales. It is particularly valuable for academic research, exploratory analysis, and understanding spatiotemporal patterns of urban electricity consumption.</p>

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Multi-resolution dataset of electricity consumption in Chinese cities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月10日

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  • Kaile Zhou,
  • Rong Hu,
  • Xinhui Lu,
  • Ziwei Yang,
  • Yaxuan Gao

摘要

Timely and spatially refined electricity consumption data are essential for supporting urban energy transition, electricity demand response, and supply-demand balancing strategies. However, publicly accessible datasets with high temporal resolution and city-level granularity remain scarce, particularly in developing countries such as China. In this study, we generate electricity consumption estimate for 296 Chinese cities in 2022 at both daily and monthly resolutions. In addition, this study presents a top-down framework for estimating city-level daily and monthly electricity consumption by integrating open-access multi-source data, including nighttime light imagery and high-resolution meteorological variables. The validation results demonstrate a strong alignment between the estimated multi-resolution values and officially reported annual statistics. This dataset addresses critical data gaps in urban-scale electricity studies by providing a publicly accessible, lower-cost, and scalable proxy for capturing electricity consumption dynamics at both daily and monthly scales. It is particularly valuable for academic research, exploratory analysis, and understanding spatiotemporal patterns of urban electricity consumption.