Useful shadow: A new independent metric to evaluate the overshadowing buildings

Abstract

This study addresses the lack of a standardized method for assessing shadow quality in building performance (BP) influenced by shading creators (ShC). The Useful Shadow (USh) metric is introduced, implemented in Python, and compatible with tools like Ladybug, providing a unified framework for evaluating various ShCs. The USh benchmark streamlines BP evaluation during early architectural planning, reducing reliance on computationally intensive simulations. It is derived from a dataset of 13,600 parametric shading devices (SDs) generated through dynamic modeling. An ANN-based BP emulator, combined with NSGA-III, establishes the USh benchmark, which is validated across 13 SD classes and applied to two zones of a residential building, using on-site measurements. Additionally, a workflow and two examples are provided to improve the clarity and practical application of the USh method.

Although the USh benchmark encountered limitations in accurately identifying SDs with performance values close to the benchmark, particularly for low-efficiency SDs, the results demonstrate potential for wider application. This method provides a scalable, standardized metric for reliable shading performance evaluation aligned with regulatory codes. While the current USh benchmark was developed for Shiraz's climate and specific conditions, future work could extend its applicability by generating USh benchmarks for other architectural contexts.