Mitigating Energy Efficiency Inequities Using Integrated Data-Driven and Parametric Energy Modeling

With a warming climate and existing inequities in the built environment, it's critical to examine pathways for reducing energy consumption and heat stress on city residents, especially in disadvantaged communities who bear the brunt of climate change. Modeling the urban context remains a challenge for accurate performance prediction in urban building energy models (UBEMs). We build upon existing research by leveraging open-access data to describe the urban context. First, we use socioeconomic data to develop archetypical UBEMs that describe disparities across a city. To capture features of the environment often ignored in UBEMs, we introduce an "urban context vector" created from satellite data. In doing so, microclimatic and urban heat island effects are captured in the weather file, providing insight on how the urban context affects building performance. This paper demonstrates a generalizable model to produce multi-scale predictions of retrofit impacts on energy consumption and heat stress. By leveraging the interpretability of UBEMs with open-access contextual data, cities will be better equipped to develop informed policies to reduce energy inequities and heat stress.