Towards scalable and actionable pedestrian outdoor thermal comfort estimation: A progressive modelling approach

Abstract

The outdoor microclimate highly affects the quality of urban outdoor spaces which are essential to a city's socio-economic vitality. Due to the complexity associated with estimating outdoor thermal comfort (OTC), its study is currently only feasible at a high computational cost and time which makes it ineffective in any iterative design process. By coupling physics-based simulations and statistical modelling techniques, this paper presents a probabilistic, progressive, and accuracy-adaptive modelling approach for faster spatially-resolved OTC estimation that is scalable to large urban neighborhoods. This is achieved through three interrelated strategies: (1) Throughout the different simulation stages, spatiotemporal OTC categories are displayed with successively rising confidence levels to support instant design decision-making. (2) Confidence levels are based on probability distributions of partially known environmental variables such as wind or mean radiant temperature. (3) Wind distributions across an urban area are initially based on a spatially-informed set of rules and later replaced with explicit simulations of wind flow fields. The approach is tested against state-of-the-art computational fluid dynamic simulations for a 3 km2 sample area of San Francisco's financial district. Results show that scalable and actionable predictions are achievable at all simulation stages with the percentage of misclassified hourly OTC ranges during occupied hours falling from 36% for instant climate-based results to 8% and 7% for spatially clustered wind and building aerodynamics informed predictions which take minutes to calculate for the investigated urban area. The building aerodynamics informed simulations accurately predicts diurnal and seasonal OTC ranges for, on average, 97% of outdoor points.