Current green building practice has been largely advanced by an integrated design process. This integrated design process involves multiple disciplines, such as architecture, civil, mechanical, and electrical engineering. The design method heavily relies on utilizing building performance simulation to illustrate how design parameters affect the energy consumption and quality of the indoor environment before actual design decisions are made (Anderson, 2014). The architectural design tools in the integrated design process supersede traditional geometrical exploration instruments, such as Sketchup, Revit, ArchiCad, and Rhino (Negendahl, 2015). More building performance simulating tools, such as Ecotect, Computational Fluid Dynamics (CFD), Radiance, and EnergyPlus, have been developed to help architects measure building performance (e.g., natural ventilation, daylighting, solar radiation, and energy uses) in the design process and attain green building standards such as Leadership in Energy and Environmental Design (LEED). The information presented by these tools guide architects at a certain level in achieving green building goals. However, building simulation is generally beyond the architect's knowledge domain. Many architects have difficulty in understanding these technical terms and models, as well as their design implications. Therefore, specific consultants have emerged to help architects grasp the meanings of these numbers and models, which require architects to implement a high level of design collaboration and coordination (Aksamija, 2015; Gou & Lau, 2014). Simulation consultants can work in parallel with architects at the early design stage to intervene in the conceptual and schematic design; they may also work behind architects to verify the building performance after the design is finished and make their design green through technical alterations.
Most existing literature argues for an early intervention of building performance simulation in the architectural design process and explores different algorithms or models for optimal intervention (Degens, Scholzen, & Odenbreit, 2015; Sick, Schade, Mourtada, Uh, & Grausam, 2014; Svetlana Olbina & Yvan Beliveau, 2007). However, the difference between early intervention and late verification is often not investigated. Few qualitative studies can help understand how the building performance simulation is actually implemented, and how it influences the quality of design solutions in addition to the quantity of performance outcomes. The current research presents two case studies that compare building performance simulation as an early intervention and a late verification tool in the architectural design process, which contextualizes the building simulation research in real building practices.