Machine learning for wind speed estimation

Abstract

For more than two decades, computational analysis has been pivotal in expanding architectural capabilities, enabling sustainable design through detailed environmental analysis. Central to creating sustainable environments is the profound understanding of wind dynamics, which significantly influence comfort levels around buildings. Traditionally, wind tunnel experiments, in situ measurements, and computational fluid dynamics (CFD) simulations have been employed to assess wind speeds in urban settings. However, the advent of machine learning (ML) has introduced innovative methodologies that extend beyond these conventional approaches, offering new insights and applications in architectural design. This study focuses on evaluating pedestrian-level wind speeds using ML techniques, with a comparative analysis against traditional in situ measurements and CFD simulations. Our findings reveal that ML can predict wind speeds with sufficient accuracy for preliminary design phases. One of the primary challenges addressed is the integration of visual outputs from ML models with quantitative data, a necessary step to enhance model reliability and applicability. By developing novel techniques for this integration, our research marks a significant contribution to the field, benchmarking the effectiveness of ML against established methods. The results validate the ML model's capability to accurately estimate wind speeds, thereby supporting the design of more sustainable and comfortable urban environments.