Building performance optimization is a powerful technique that assists designers in identifying optimal design solutions. However, occupant-related uncertainty is considered a major challenge that can influence the credibility of the selected optimal design. This paper proposes an occupant-centric method for window and shading design optimization in office buildings that assists designers in evaluating and handling the impact of occupant-related assumptions. The method consists of three major steps: generating occupant scenarios, conducting occupant-centric mathematical optimization, and deriving rules for selecting optimal design parameters using decision trees. An office building model with numerous possible window-related parameters was used to demonstrate the proposed optimization process. Sixty-four different occupant scenarios were considered. The results of the optimization process demonstrated that occupant-related assumptions are crucial in determining optimal window and shading design, as different occupant scenarios led to different optimal solutions. The study findings also provided insights to building designers regarding classifying and selecting window and shading design parameters under varying occupant scenarios.
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