Rong Hu

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.

Considering the growing demand for electricity in industrial parks, understanding their electric power load patterns is critical for improving energy efficiency and ensuring the rational utilization of energy resources. However, the detailed electric power load data of various buildings in industrial parks are rarely available and accessible, which hinders the related studies. In this context, we present the electric power load data of 6 years (from January 1, 2016 to December 31, 2021) for various types of buildings in an industrial park in Suzhou, China. The data are obtained from smart meters and have various time resolutions (i.e., 5 minutes, 30 minutes, and 1 hour). This work describes the data collection, processing process, and different imputation methods. The high-resolution electric power load data can be used for various research tasks, including load prediction, load pattern recognition, anomaly detection, and demand response strategy development.