Machine-learning to analyze human-building interactions: How do people use mobile solar protections?

Buildings significantly impact urban energy consumption. Mobile passive solutions, such as manual solar protections, can mitigate heat gains, but their effectiveness depends on occupants’ decisions. Analyzing occupant adjustments of manual systems is challenging due to the complexity of human behavior. While multiple studies have observed human-building interactions, analyzing large buildings over extended periods remains a technical challenge. This study examines 359 manually controlled solar protections over a year using systematic photographic data. To manage the large volume of images, an unsupervised machine learning algorithm was employed to cluster similar solar protection positions for each window, which were then manually tagged to identify their positions. Results show that solar protections are mostly closed year-round (28 % aperture on average), with minimal differences between occupied and non-occupied days and between warm and cool seasons. Notably, one-third of the solar protections were not operated throughout the year. Our results provide insights into the usage of manual façade systems and offer valuable knowledge for energy simulation. Methodologically, the use of machine-learning algorithms presents a new way to process large datasets of images, emphasizing the importance of prioritizing quality over quantity and strategic data collection. Future research can apply this methodology to different building types and climates to gain broader insights into occupant behavior and solar protection interactions. • Machine-learning can be a useful tool for observing buildings. • 359 roller shutters of a university building were observed for a year. • The average aperture of solar protections was found to be 28 %. • One third of the solar protections remained unoperated manually for one year. • Minimal variations in patterns were detected between seasons.