Post-retrofit building performance dataset: Energy, IEQ, HVAC operation, and occupant behavior in university apartment buildings

Résumé fourni par la source

This paper presents a comprehensive monitored dataset from single-family attached residences that underwent deep energy retrofits, complementing a previously published pre-retrofit dataset from the same buildings. The dataset is distinct by its multi-domain scope and temporal resolution, simultaneously capturing whole-building and end-use energy consumption, indoor environmental quality, heating, ventilation, and air conditioning (HVAC) system operation (including thermostat setpoints and supply air temperature), window and door operation, building envelope performance, and outdoor environmental parameters at sub-hourly intervals. Data were collected from sixteen occupied apartments across two residential buildings over a continuous twelve-month period, enabling detailed analysis of performance across all seasonal conditions. This high-resolution, multi-faceted dataset uniquely documents the coupled dynamics between building envelope functions, HVAC systems operation, occupant interactions, and weather conditions in the post-retrofit state. A semantic metadata model of building systems and sensors was developed using a JavaScript Object Notation (JSON) schema. Standardized cleaning and pre-processing steps were applied to produce a research-grade dataset. Post-retrofit results reveal improved thermal stability, enhanced ventilation effectiveness with reduced CO 2 concentration, and measurable energy performance improvements compared to pre-retrofit conditions. This openly available dataset provides researchers and practitioners with a unique opportunity to evaluate retrofit impacts through granular time-series analysis, study occupant-building-weather dynamics, perform comparative pre/post retrofit analysis, and develop predictive models for human-centered, energy efficient building transitions.

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