Indoor thermal condition in urban heat Island – Development of a predictive tool

Urban Heat Island (UHI) effects have caused extensive economic and health related issues to many city residents, especially the most vulnerable such as elderly people living in buildings without air conditioners or mechanical ventilation systems. To reinforce the resiliency of individuals and communities in facing extreme heat event, cities are developing reliable tools to predict the indoor thermal characteristics using available building characteristics, climate data and socio-economical factors. In this study, a novel approach is proposed to predict the indoor thermal conditions in these buildings. First, a measurement campaign is conducted to monitor indoor thermal condition within 55 buildings in most vulnerable regions on the Island of Montreal. Two models, Simplified and Advanced, are developed to predict hourly indoor dry-bulb temperatures. Both models use an advanced Artificial Neural Network (ANN) technique. The Simplified ANN Model generates a correlation between airport weather observations and monitored indoor dry-bulb temperatures. On the other hand, the Advanced Model includes ten influential parameters, which represent the effect of neighboring environment, building characteristics and its usage patterns on the indoor thermal condition. Comparison of these two predictive models is conducted on different levels of simulation and validation. The Advanced Model shows better accuracy in predicting the indoor thermal conditions, thus justifying the use of neighborhood specific parameters to forecast indoor environment condition in an urban heat island area.