Multivariate relationships between campus design parameters and energy performance using reinforcement learning and parametric modeling

Abstract

For the past two decades, extensive research has been conducted to evaluate the performance of urban form scenarios such as sky view factors, solar radiation, or energy performance. The relationships between urban design parameters and multiple performance should be stipulated to evaluate scenarios for supporting timely and reliable design decisions. However, there are three major challenges: (1) only a few design alternatives are devised, (2) the multiple performance has not been provided synthetically, and (3) the relationships between design parameters and performance are difficult to be determined because they are multivariate relationships. This research proposes a new methodology applying a generative design approach using a reinforcement learning algorithm, a parametric performance modeling, and a multivariate adaptive regression splines approach to identify relationships between design parameters and urban performance. This research aims to support decision-making of urban design to achieve an energy efficient and visually qualified urban environment. A data-driven urban design approach is proposed to generate possible design alternatives using reinforcement learning, and a design-driven analysis is conducted to evaluate multiple performance of urban buildings using parametric modeling. The multivariate analysis presented relationships between urban geometric forms and performance criteria by using 30 samples. The findings show that to maintain optimal solar potential, the building coverage ratio is recommended to be bigger than 0.17. To maintain the optimal energy balance, the threshold for sky view factor is recommended as 54.17%. These relationships contribute to deriving design strategies and guidelines for designing a sustainable campus.