This Building Data Genome Project 2 steam meter dataset provides two years of hourly whole-building readings for non-residential assets. The Building Data Genome Project 2 encompasses a comprehensive collection of hourly steam meter readings specifically curated for non-residential building stock. This structured dataset serves as a benchmark for energy performance analysis, offering longitudinal insights into thermal consumption patterns across various building types. The data is formatted for seamless integration with electricity and weather series, allowing for nuanced correlation studies. By providing a standardised reference for steam usage, the compilation supports researchers and engineers in developing robust predictive models, validating energy conservation measures, and conducting comparative studies on building operational efficiency in complex urban environments.

The download is a single file containing .CSV files.

Engineers and data scientists typically process these CSV files within analytical environments such as Python, R, or dedicated building performance simulation software. The data is structured to be merged with corresponding metadata, electricity, and weather datasets to facilitate multi-variable regression or machine learning workflows. Users should perform initial cleaning and time-series alignment to ensure consistency before executing load profiling, baseline estimation, or anomaly detection tasks within their specific energy management or diagnostic platforms.

This dataset is essential for professionals conducting advanced building energy audits, operational diagnostics, or long-term performance benchmarking. It is particularly valuable for researchers and consultants working on commercial or industrial facility upgrades where steam heating systems are prevalent. The data assists in the development of automated fault detection and diagnostics (AFDD) algorithms. Practitioners seeking to understand thermal demand response or optimize central plant operations will find this resource a critical foundation for evidence-based decision-making in sustainable design and facility management projects.