Facilities management (FM) encompasses and requires multidisciplinary activities, and thus has extensive information requirements. While some of these needs are addressed by several existing FM information systems, building information modeling (BIM), which is becoming widely adopted by the construction industry, holds undeveloped possibilities for providing and supporting FM practices with its functionalities of visualization, analysis, control, and so on. This paper explores how BIM can be a beneficial platform for supplementing FM practices. An online survey and face-to-face interviews were conducted to assess the current status of BIM implementations in FM, potential applications, and the level of interest in the utilization of BIM. Interactions between BIM and FM are defined by illustrating application areas and data requirements for BIM-enabled FM practices. Highlighting the synergy between the two, this paper can help professionals recognize potential areas in which BIM can be useful in FM practices.

With ever-rising energy demand and diminishing sources of inexpensive energy resources, energy conservation has become an increasingly important topic. Building heating, ventilation, and air conditioning (HVAC) systems are considered to be a prime target for energy conservation due to their significant contribution to commercial buildings’ energy consumption in the US. Knowing a building’s occupancy plays a crucial role in implementing demand-response HVAC controls, with a corresponding potential for reduction of HVAC energy consumption, especially in office buildings. This paper evaluates occupancy modeling (both binary detection and multi-class estimation) using twelve ambient sensor variables. Performance of six machine-learning techniques is evaluated in both single-occupancy and multi-occupancy offices. Of the six, the decision-tree technique yielded the best overall accuracy (i.e. 96.0% to 98.2%) and root mean square error (RMSE) (i.e. 0.109 to 0.156). The contribution of each individual ambient sensor variable is evaluated via information gain. It is found that CO₂, door status, and light variables have important contributions to the final modeling results. It is observed that the overall accuracy generally increases as the number of sensors increases. This paper also examines the possibility of building a global occupancy model, and explores the reasons for low performance of global occupancy estimation. Lastly, the occupancy model is used to estimate and visualize the accumulative room and thermal zone usage in an office test-bed building for three months. The results reveal that the effective vacancy accounts for a substantial portion of the operational hours, varying from 19.8% to 29.8% with an average of 23.3%, which bears significant potential for energy savings. Furthermore, the authors simulated HVAC energy consumption of the test-bed building for three months in DesignBuilder and EnergyPlus, and compared energy consumption of occupancy-based demand-response HVAC controls using the authors’ occupancy-modeling results to the conventional HVAC controls currently implemented in the test-bed building. The results demonstrate that 20% of gas and 18% of electricity could be effectively saved if occupancy-based demand-response HVAC control is implemented.