 The urban heat island effect has been found to increase the energy consumption of buildings in urban areas due to its impact on both the cooling and heating demands of the building. This paper proposes a novel approach to accurately model the urban canopy temperature using an artificial neural network, ANN, and then apply it to a neighborhood in downtown Vancouver. The results show that the UHI effect increases the total cooling energy demand by 23% and decreases the total heating energy consumption by 29%, resulting in an overall negative effect on the total energy demand of the building, which is 18% higher in the urban area. Additionally, the UHI effect increases the number of hours of indoor temperature above the cooling set point by 7.6%. This methodology can be applied to other cities around the world to determine the urban canopy temperature of neighborhoods in different climate zones and determine the varying urban heat island effects associated with the locations. This article was authored by Fitsum Tariku and Afshin, Garib Mambini.