This comprehensive dataset provides two years of hourly electricity meter readings for 1,636 non-residential building assets. The Building Data Genome Project 2 comprises a massive collection of time-series energy data formatted for immediate analytical use. It captures the hourly electricity consumption profiles of over one thousand distinct non-residential building structures, providing a robust empirical foundation for energy benchmarking and predictive modelling. The data is structured to facilitate complex longitudinal analysis of building performance across various sectors, including commercial, institutional, and industrial typologies. By standardising these readings into a consistent CSV format, the collection enables researchers and engineers to perform large-scale comparative studies on energy consumption patterns and operational efficiency.

The download is a single file containing .CSV files.

Professionals integrate this dataset into energy modelling and data science workflows by importing the CSV files into platforms such as Python, R, or dedicated building simulation software. Users can perform exploratory data analysis, train machine learning algorithms for load forecasting, or benchmark specific project energy profiles against the provided aggregate data. The structured nature of the files allows for rapid data cleaning and feature engineering, which is essential for developing accurate predictive models for whole-building energy performance in various climate zones.

This dataset is primarily utilised by MEP engineers, sustainability consultants, and researchers involved in high-performance building design and facility management. It is invaluable for validating energy models during the early design stages or for developing smart building control strategies. Professionals searching for benchmark data to support energy efficiency retrofits or to test HVAC optimisation algorithms will find this resource highly relevant for non-residential building projects. The data serves as a critical reference point for understanding operational energy demand across diverse building types and occupancy profiles.