One of the main required actions for moving forward in zero-carbon cities’ goals is to increase building energy efficiency. Urban Building Energy Modeling (UBEM) can help optimize the built environment's energy efficiency and improve the design and operation of building energy systems. Moreover, building modeling provides key information for Heating, Ventilation and Air Conditioning (HVAC) control, building energy management, different building energy efficiency scenarios, and feedback to the occupants. In order to parametrize Urban Building Energy Modeling (UBEM), individual buildings' characteristics, such as constructions, internal loads, energy systems, etc., are required. Many dynamic UBEMs have already been developed, yet most use simplified assumptions for some of the parameters needed. The capabilities required for having a comprehensive UBEM are creating detailed 3D urban building geometry, creating a comprehensive building attributes library, conducting detailed archetype selection, and assignment of this construction information to the building surfaces. More importantly, occupant behavior is another important factor that influences the modeling results, and it is the root of high uncertainty in UBEM results. Most existing tools have a significant weakness in dealing with occupant related monitoring data or estimation of likely schedules. 
Many studies have been conducted to develop and introduce different occupant behavior measurements and methods for urban building energy modeling. However, none of the previous studies introduced scalable and practical occupant behavior monitoring on an urban scale. Some of the main challenges that prevent the scalability of the already developed occupant behavior methods and measurements are the fact that they suffer from threatening the occupant's privacy and the high cost of their infrastructure and deployment. 
This thesis aims to leverage passive WiFi sensing methods for occupant behavior monitoring and pattern analysis for UBEM using commercial off-the-shelf WiFi devices, for which the infrastructure is already available in many buildings. Passive and device-free WiFi sensing does not threaten the occupant's privacy, and its deployment cost is low. Therefore, this method of occupancy measurement has the potential to be used on an urban scale and to play an essential role to manage energy in zero carbon and smart cities in the future.