A Benchmark Model for Predicting Building Energy and Daylight Performance in The Early Phase of Design Utilizing Parametric Design Exploration

Along with the enormous impact on computational development in architecture and urban design, the way in approaching the built environment is shifting and intended to look closer to performance and evidence-based design. This development holds promise in handling complex computation to approach desired targeted design goals. However, the implementation of form-finding and design performance optimization still lacks, particularly in Japan's sub-tropical climate. This paper describes the parametric design and design exploration process's implementation through the generative algorithm platform to develop a benchmark model to predict building energy and daylight performance and find possible design solutions from the iteration process during the early phase of the design process. The variables incorporated related to the glazing ratio, the length of the overhang, and building orientation. Grasshopper, a parametric-based plugin that works in Rhinoceros, is used to arrange a parametric definition for the overall experiment. The tools used to investigate the environmental analysis and energy consumption are Ladybug and Honeybee, and the exploration process will be conducted using Design Explorer. The context will be situated in Orio district and uses the EPW file of Kitakyushu city, Fukuoka, Japan. The results of this research furthermore can potentially be a comparison for more dynamic factors.