As summer heatwaves become more intense and more frequent, achieving thermal comfort in buildings without relying on air conditioning has become critical to meet CO₂ emission targets. To address this, a field study was conducted in an office building at INES, Le Bourget-du-Lac, France. The bioclimatic design of the building takes advantage of the local climate, influenced by mountain ranges and Bourget Lake, to minimize overheating while ensuring occupant comfort thanks to solar control, natural ventilation and thermal inertia. It optimizes natural ventilation through features such as a green atrium with automated openings and internal leafs. Pedestal fans completed this strategy to maximize occupant comfort without air-conditioning.

Data were collected over six years (2017-2022) through sensors networks and thermal comfort surveys thanks to a Human Machine Interface (HMI), which has been developed and improved over the different campaigns. The surveys gather occupant perception of comfort, clothing details, and the effect of draughts, while sensors monitored door and window openings, office temperatures and fan use. Instrumentation includes temperature sensors connected to computers, plug-in power sensors for fans, and openings status sensors. Data were recorded every 5–10 minutes using a Jeedom home automation system linked to a Raspberry Pi, with a Python script transferring data to a central database. Across the study, up to 29 participants contributed annually, generating datasets ranging per year from 250 to 17,500 responses.

This dataset supports research into the relationship between thermal comfort, occupant perception, and passive cooling strategies, such as natural and mixed-mode ventilation. It provides insights into variations in occupant comfort and behavior across similar offices environment and forms a basis for studying building overheating, operational performance, and the effectiveness of passive cooling strategies. This dataset also facilitates the development of ventilation control algorithms and cascade cooling strategies by analyzing the conditions under which occupants use desk fans, helping optimize mixed-mode ventilation systems for comfort and efficiency.

Python, 3.11