Abstract
Granular, localized data are essential for generating actionable insights that facilitate the transition to a net-zero energy system, particularly in underdeveloped regions. Understanding residential electricity consumption-especially in response to extreme weather events such as heatwaves and tropical storms-is critical for enhancing grid resilience and optimizing energy management strategies. However, such data are often scarce. This study introduces a comprehensive dataset comprising hourly transformer-level residential electricity load data collected between 2022 and 2023 from 23 residential communities across 10 cities in Guangxi Province, China. The dataset is augmented with meteorological data, including temperature, humidity, and records of extreme weather events. Additionally, calendar-related data (e.g., holidays) are included to facilitate the analysis of consumption patterns. The paper provides a detailed overview of the methodologies employed for data collection, preprocessing, and analysis, with a particular emphasis on how extreme weather influences electricity demand in residential areas. This dataset is anticipated to support future research on energy consumption, climate change adaptation, and grid resilience.