As urbanization and population growth have increased over the past decade, more construction has been built in urban areas to form large metropolitan areas. Researchers are paying more attention to the link between human activities and the immediate surroundings – urban microclimate –to improve the quality of life and minimize adverse impacts on the environment and climate. This thesis focuses on the urban microclimate and its impact on outdoor thermal comfort and building energy performance. This study will start a comprehensive literature review presenting the latest progress in urban microclimate research on urban wind and thermal environment, covering methods and practical issues.
For the short-term analysis, this research studies how urban configuration affects the urban microclimate and outdoor thermal comfort. In the present work, temperature distribution at three different urban areas will be simulated during a summer heatwave in 2013 in Montreal, Canada. The impact of different building configurations on the flow pattern will be investigated. What’s more, thermal comfort and the impact of heatwaves on the human body will be considered by humidex (humidity index). The results show that this model is capable of estimating local microclimate and outdoor thermal comfort.
An artificial neural network (ANN) model is also presented in this study to predict urban microclimates based on long-term measurements from local weather stations near urban buildings and their significance in analyzing building energy consumption. The ANN model could connect local and remote meteorological parameters for a whole year. The 20-year historical weather data at the airport was then used to generate a local TMY, and then building heating and cooling loads were analyzed. This method was evaluated for five weather stations to assess the impact of the local microclimate on the energy consumption of buildings. 
This study underscores the crucial role of urban microclimate in building energy consumption through both short-term and long-term evaluations. Accurate prediction of local weather conditions around buildings is essential within urban microclimates. The research introduces a pioneering approach using an artificial neural network model for predicting microclimate parameters based on extensive onsite measurements, emphasizing its significance in building energy analysis.