Abstract
Building occupancy patterns are an important factor in considering the energy efficiency of buildings and a key input for building performance modelling. More specifically, the energy consumption associated with heating, cooling, lighting, and plug load usage depends on the number of occupants in a building. Identifying occupancy patterns and profiles in buildings is a key factor for the optimisation of building operating systems and can potentially reduce the performance gap between the planning stage and the actual energy usage. This study aims to identify the patterns and profiles of the occupants in a selected case study building in England. In this study, occupancy data were collected over 12 months at five minutes intervals. A sensor was used to obtain high accuracy occupancy data compared to previous studies that encountered uncertainties in data collection. A set of clustering analyses was carried out to identify occupancy patterns and profiles in the building. The results of this study identified three different occupancy patterns and profiles as well as four drivers that influenced the occupants in the case study building: the beginning of the academic term, the examination period, the weekday/weekends, and the vacation driver.