Smart environments are increasingly common. By utilizing sensor data from the indoor environment and applying methods like machine learning, they can autonomously control and increase productivity, comfort, and well-being of occupants.

The aim of this thesis was to model indoor climate in conference rooms and use K-means clustering to determine quality levels. Together, they enable categorization of conference room quality level during meetings. Theoretically, by alerts to the user, this may enhance occupant productivity, comfort, and well-being. Moreover, the objective was to determine which features and which k would produce the highest quality clusters given chosen evaluation measures.

To do this, a quasi-experiment was used. CO2, temperature, and humidity sensors were placed in four conference rooms and were sampled continuously. K-means clustering was then used to generate clusters with 10 days of sensor data. To evaluate which feature combination and which k created optimal clusters, we used Silhouette, Davis Bouldin, and the Elbow method.

The resulting model, using three clusters to represent quality levels, enabled categorization of the quality of specific meetings. Additionally, all three methods indicated that a feature combination of CO2 and humidity, with k = 2 or k = 3, was suitable.