Understanding the Importance of Post Occupancy Usage Trends During Concept-Stage Sustainable Building Design

As sustainable building mandates become more prevalent in new commercial and mixed use buildings, it is a challenge to create a broad, one-size-fits-all certification process. While designers can estimate energy usage with computational tools such as model based design, anticipating the post occupancy usage is more challenging. Understanding and predicting energy usage trends is especially complicated in unique mixed use building applications, such as university student housing buildings, where occupancy varies significantly as a function of enrollment, course scheduling, and student study habits. This research explores a computational modeling approach used to achieve LEED (Leadership in Energy and Environmental Design) Platinum certification for a student housing complex design. A case study is presented from the California State University, Fullerton (CSUF) campus, and examines the impact of post occupancy building usage trends, and diversity factor, defined as a building’s instantaneous energy usage normalized by the maximum allowable usage, on energy use estimates. The CSUF case model, which was originally created using EnergySoft’s EnergyPro 5 software, is examined. An annual predictive energy use comparison is performed in EnergyPro 5 using general building design mandates (i.e., ASHRAE 90.1, California Title 24), and CSUF case specific building usage details (e.g., student scheduling, diversity factor). In addition, the energy usage estimates of these two predictive models are compared to the actual usage data collected during the 2014 academic year. The results of this comparison show the benefits of considering post occupancy usage, and recommendations are presented for creating unique and application based computational models, early in the design process. This research has broad applications, and can extend to sustainable building design in other organizations, whose operational schedule falls outside of current prediction methods for sustainability mandates.