Methodological study demonstrates multi-scale energy assessments using tiered time-series data, suggesting scalable pathways for calibrating real-world building energy models.
This paper builds on the workflow derived in the 2025 Winter Conference Paper “Utilizing Real-Time Electrical Meter Data to Validate Predictive Energy Model Assumptions for a Hospital” by proposing a broader framework for conducting building performance assessments based on the quality and granularity of available time-series data. As more buildings are equipped with advanced metering infrastructure, the ability to validate, refine, and optimize energy models and operational strategies continues to expand. This framework categorizes data availability into defined levels and outlines the corresponding types of performance assessments that can be conducted at the site, building, system, and equipment scale. Drawing from real-world examples, the paper demonstrates how increasing data resolution—from monthly utility bills to sub-metered interval data—enables progressively more detailed diagnostics, calibration exercises, and energy optimization strategies. The methodology offers a scalable approach for engineers, architects, and facility managers seeking to better align energy models, operational expectations, and real-world building behavior. This work aims to support the broader industry shift toward data-driven building design and performance validation.
No takes yet. Share an insight, caveat, or question.
Butler et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: