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A Novel Approach for Investigating Canopy Heat Island Effects on Building Energy Performance: A Case Study of Center City of Philadelphia, PA

Because of the urban heat island (UHI) effect, an urban agglomeration is typically warmer than its surrounding rural area. Today, UHI effects are a global concern and have been observed in cities regardless of their locations and size. These effects threaten the health and productivity of the urban population, moreover, they alter buildings energy performance. The negative impacts of UHI on human welfare have been confirmed broadly during the past decades by several studies. However, the effects of increased temperatures on the energy consumption of buildings still need a comprehensive investigation. Moreover, considering the UHI effects at the early stages of the design process is still not pervasive due to the lack of straightforward and convenient methodologies to include these effects in the estimation process of buildings’ energy consumption. To fill the mentioned gaps, a novel methodology of coupling the Local Climate Zones (LCZs) classification system and the Urban Weather Generator (UWG) model is proposed in this study to evaluate the UHI impacts on the energy consumption of various building typologies positioned in different climate zones. The methodology is applied to the most populated area of city of Philadelphia, Center City, and modified Typical Meteorological Year (mTMY) data comprising the canopy heat islands effect in the scale of an urban block or a neighborhood are produced in the format of .epw. The initial results of this study show an average of 2.7 °C temperature difference between existing local climate zones of Center City and reference TMY3 weather data recorded at Philadelphia International Airport during three sequential summer days. The generated weather data then were incorporated into an Urban Building Energy Model (UBEM) to simulate the spatiotemporal differentiation of energy demand for cooling and heating end-uses at each building typology under two scenarios of weather data i.e. mTMY and TMY3 data.