Smart Urban Windcatcher: Conception of an Ai-Empowered Wind-Channeling System for Real-Time Enhancement of Urban Wind Environment

Abstract

The dense configuration and rapid proliferation of high-rise buildings in central Hong Kong have led to increasing stagnation of pedestrian-level airflows, lowering the wind speed and exacerbating issues in wind and thermal comfort. Addressing these problems can be difficult without significant building renovations and urban re-planning. This paper introduces a novel framework for the real-time enhancement of the local urban wind environment using motion-controlled billboards, or smart urban windcatchers, managed by a deep reinforcement learning (DRL) agent. The DRL agent determines the windcatchers' optimal positions based on street-level sensor data, independent of meteorological data. The framework's effectiveness was assessed using a simplified city grid model, where two windcatchers aimed to optimize the pedestrian-level wind environment on a specific street. Simulation results indicated that the windcatchers could effectively alter the flow direction in the streets, promoting or diverting air passages per demand. The DRL agent also gave accurate instructions to the windcatchers under various weather conditions, achieving near-optimal wind environment scores. This paper introduces the conception and confirms the feasibility of the windcatcher system, laying the groundwork for future research—as it is much needed and welcomed—to enhance the system and overcome the acknowledged challenges in large-scale, real-world implementation.

Keywords: Deep reinforcement learning, Flow control, Urban wind environment, Pedestrian wind comfort, Windcatcher