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The operational building data presented in this paper has been collected from six office rooms located in an office building (research and educational purposes) located on the main campus of Aalborg University in Denmark. The dataset consists of measurements of occupancy, indoor environmental quality, room-level and system-level heating, ventilation and lighting operation at a 5 min resolution. The indoor environmental quality and building system data were collected from the building management system. The occupancy level in each monitored room is established from the computer vision-based analysis of wall-mounted camera footage of each office. The number of people present in the room is estimated using the YOLOv5s image recognition algorithm. The present dataset can be used for occupancy analysis, indoor environmental quality investigations, machine learning, and model predictive control.

The data presented were sourced from 34,884 commercial smart heat meters and 10,765 commercial smart water meters, spanning a timeframe of up to 5 years (2018–2022). All data primarily originated from single-family houses in Aalborg Municipality, Denmark. Furthermore, comprehensive building characteristics were collected for each building, where available, from the Danish Building and Dwelling Register (BBR) and Energy Performance Certificate (EPC) input data. This effort yielded an extensive pool of up to 86 distinct characteristics per building. All smart meter data were processed employing a well-established methodology, resulting in equidistant hourly data without any erroneous or missing values. The building characteristics derived from the EPCs were additionally filtered using rule sets to improve the data quality. This dataset holds substantial value for researchers involved in the domains of the built environment, district heating, and water sectors.