A data-driven optimized daylight pattern for responsive facades design

Abstract

ABSTRACT This study presents a data-driven approach for investigating practical aspects of responsive facades illuminance optimization. In this approach, the hourly indoor illuminance data and spatial information are integrated to form an objective function. Then, the objective function is used to assess the visual performance of responsive facade systems by matching a wide range of angle movements with hourly daylight patterns. An office room with a responsive facade was simulated parametrically to test the proposed optimization function through design scenarios. Raw indoor illuminance data was generated for a year of both horizontal and vertical facade configurations in four different facade orientations and four facade locations/climate zones. Data analytic techniques were deployed for quality assurance, pre-processing, managing and analyzing the simulated data. A Brute-force search algorithm was utilized to determine the hourly optimum angle of the facade configuration. The result reveals hourly optimum adaptation angles can significantly improve indoor illuminance of all possible scenarios such as various facade configurations, facade orientations, and facade locations/climate zones in comparison with no-louvers and fixed louvers scenarios.