This CSV dataset provides two years of hourly whole-building natural-gas meter readings for non-residential building performance analysis. The dataset comprises two years of high-resolution hourly natural-gas consumption readings, structured for rigorous building performance simulation and energy benchmarking. It captures whole-building energy usage patterns across a diverse range of non-residential structures, providing a reliable empirical foundation for comparative studies. The data is aligned with electricity, weather, and metadata series to ensure consistency in multi-variable analysis. By isolating gas-specific energy loads, this collection allows researchers and practitioners to perform detailed disaggregation and trend analysis, facilitating a deeper understanding of thermal demand and operational efficiency in various building types.

The download is a single file containing .CSV files.

Professionals can integrate this dataset into energy modeling platforms, data science environments, or spreadsheet software for statistical processing and visualization. The structured CSV format ensures compatibility with common programming libraries, allowing for seamless alignment with weather files and building metadata. Practitioners typically use this information to calibrate baseline energy models, validate simulation assumptions, or conduct longitudinal performance assessments, enabling data-driven decision-making during the design, commissioning, and operational phases of building management.

This resource is intended for energy consultants, building physicists, and researchers working on decarbonization strategies or operational optimization. It is particularly valuable for projects requiring historical energy benchmarking, predictive maintenance planning, or comparative analysis of non-residential building stock. By leveraging this empirical data, teams can better inform energy-saving retrofits and improve the accuracy of whole-building performance simulations. It serves as an essential reference for those looking to implement evidence-based design practices and improve the environmental impact of institutional, commercial, and industrial facilities.