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Energy consumption has dramatically increased in buildings over the past decade. A synthetic urban building energy consumption dataset can be used to estimate energy demand and anthropogenic carbon emission, which overcomes the constraints of real-world datasets, including difficulties in the collection of energy uses from different sources, expense, and time. In this study, a 24-h building energy consumption dataset was designed and established in a hot and humid subtropical region, based on ENVI-met, Energyplus™ and Access software. The dataset considers 72 conditions from three aspects including six built-up local climate zones, seven building categories (five public and two residential buildings), and four widely-used air conditioning systems. A total of 17,400 building energy consumption data were collected. We analyzed the hourly variations of building energy consumption under different conditions, and the factors influencing the building energy consumption. Finally, based on the dataset, taking Tianhe District in Guangzhou as an example, we further explored the hourly spatio-temporal distribution patterns of building energy consumption and daily total building energy consumption distribution using ArcGIS software. This study provides a method for establishing an urban building energy consumption dataset from a local-scale view, which shows a new light on developing the National Building Energy Consumption Database response to global low-carbon action. [Display omitted] • Propose a bottom-up building energy consumption (BEC) accounting method • Establish a LCZ-based BEC dataset based on a series of coupling factors • Present spatio-temporal visualization maps of local-scale BEC • A LCZ-based BEC dataset contributes to design "smart energy network" • The established BEC dataset can be applied to guide smart urban planning [ABSTRACT FROM AUTHOR]