The BIM-based building energy simulation plays an important role in sustainable design on the track of achieving the net-zero carbon building stock by 2050. However, the issues on BIM-BEM interoperability make the design process inefficient and less automatic. The insufficient semantic information may lead to results inaccurate while the error-prone geometry will terminate the simulation engine. Defective models and authoring tools lagging behind the standard often cause failures in creating a clean geometry that is acceptable to the simulation engine. This project aims to develop a workflow that helps with the documentation of a lightweight geometry in gbXML format. The implemented workflow bypasses the modeling inaccuracies and irrelevant details by reconstructing the model based on extrusions on patched floor plans. Compared with other gbXML files exported by BIM authoring tools, the resulting gbXML is more lightweight with airtight space boundaries. The gbXML has been further tested against EnergyPlus to demonstrate its capability in aiding a seamless geometry exchange between BIM and BEM.

Occupant behaviours in the buildings are not only random and uncertain but also related to each occupant’s habitual preference. This leads to the performance gap between actual and expected energy consumption in buildings. Therefore, accurate information and modelling with regard to occupant behaviour are important for reliable energy simulation and energy-saving optimization design. Existing studies on occupant behaviour models in office space usually focus on single-person offices or full-floor buildings, without considering the behavioural differences among offices with different occupancy. Therefore, this study established the air-conditioning usage behaviour models in offices with different occupancy based on questionnaires and measured data. The results show that occupant compromise and clustering effect will increase with the increase of occupancy. Using the established models as input, this study compared the simulation results with that under the standard schedule. The difference rate is as high as 32.19% in winter and 13.07% in the whole year. And for areas with high energy consumption in winter, the gap may be bigger.