Abstract This study presents key findings of observed datasets in a nearly zero-energy office building for over 66 working days from June to mid-September in 2019, Luxembourg. Measurements of indoor and outdoor environmental parameters as well as user-shade override adjustments were extracted from the KNX-based building management system (BMS) in 47 office rooms located in three typical floor levels. Relative frequency and “rate of change” of blind use were analysed in terms of window orientation, occupancy level, and the time of the day. Logistic regression and data mining techniques were used to identify potentially useful and understandable occupant behaviour patterns and reveal the main triggers behind blind adjustments. The well-designed automation system together with the inner glare protection formed the base of very low user-shade interactions. A mean of 0.184 manual blind adjustments per day per office. Eight regression sub-models were developed and all were incapable of predicting user-shade lowering and raising events. Alternatively, two user profiles were mined based on 20 rules gained from clustering analysis: user (ß) was representing the passive user, and user (μ) the medium user. Overall, we conclude that the automated shading system in this building is satisfactory, user-friendly, and a robust control system.