Building Data Genome Project 2 – Solar Generation Meter Data

This CSV dataset provides two years of hourly on-site solar generation meter readings for non-residential building performance analysis. The Building Data Genome Project 2 – Solar Generation Meter Data is a comprehensive structured dataset capturing hourly energy production metrics. It focuses on non-residential building assets, providing a granular look at on-site solar generation performance over a two-year period. The data is formatted for high-compatibility integration, ensuring alignment with existing consumption meter readings and external weather series. This resource serves as a foundational benchmark for energy researchers and sustainability consultants aiming to validate performance models, calibrate simulation software, or conduct longitudinal studies on renewable energy integration in the built environment.

The download is a single file containing .CSV files.

Professionals typically integrate this CSV dataset into data science workflows using Python, R, or specialized energy modeling software. By importing the files, users can correlate generation metrics with hourly building consumption logs and weather data. This process allows for the creation of robust regression models, energy performance visualizations, and predictive simulations. The structured nature of the data supports rapid filtering and normalization, making it suitable for large-scale comparative analysis across diverse building typologies and climate zones within a project.

This dataset is essential for energy auditors, sustainability consultants, and researchers working on net-zero building designs and performance optimization. It is frequently employed during the feasibility and validation stages of commercial or institutional projects to benchmark expected solar yields against actual operational data. By pairing these metrics with building consumption profiles, architects and engineers can better assess the efficacy of renewable energy systems. Practitioners searching for high-quality, real-world energy performance data will find this a critical resource for longitudinal building analytics.