This paper describes a data driven urban canopy model that can be coupled with detailed building energy models. The data driven model is used to assess the outdoor air temperature and humidity in a street canyon considering as inputs weather conditions at the atmospheric layer, the surface temperature of surrounding building facades and the street, and the heat released from the use of air-conditioning. Predictions made by the model were tested using measurements of the outdoor air temperature and humidity collected between April and August 2019 in Singapore. Results show that the model estimates the outdoor air temperature with a similar accuracy than others that were validated using the same input and test data, while providing estimates with a higher temporal resolution and considering urban morphology with a higher fidelity. They also demonstrate that the model can predict the impact of waste heat releases and cool pavement on the outdoor air temperature and building energy consumption. In the future, vegetation could be considered as an input of the model if the land surface temperature is measured using an infrared thermal camera. Another improvement would be to define weather conditions at the atmospheric layer from rooftop measurements or a climate model.

Urban building energy modelling aims at studying different approaches to estimate the energy consumed by buildings. However, most urban building energy models introduced in the literature ignore the impact that buildings have on their outdoor conditions, and inversely. Therefore, the paper determines whether interactions between buildings and outdoor conditions affects simulations performed using an urban building energy model, in particular during their calibration. Results suggest that interactions between buildings and their outdoor conditions affect the sensitivity of outcomes provided by an urban building energy model towards uncertain parameters and the probability to calibrate the model in accordance to some standards.