As cities like Philadelphia strive to meet ambitious greenhouse gas reduction goals, predicting how zoning and development decisions will impact building energy use and emissions is a major challenge. A new study led by Simi Hoque, PhD, professor of civil, architectural and environmental engineering, uses machine learning to address this problem.
The model developed by Hoque’s team leverages a deep-learning program, called Extreme Gradient Boosting (XGBoost), to forecast neighborhood-level energy use based on housing features, demographics and socioeconomics. A Shapley analysis then pinpoints which factors most influenced the predictions.
“Machine learning is well equipped to handle this challenge because the models can iteratively learn and improve through training despite data limitations,” Hoque explained.
In a hypothetical scenario forecasting energy use through 2045, the interpretable model suggested zoning policies like upzoning may increase residential energy use in some areas. For commercial buildings, square footage and employee count were key drivers.
While further testing is required, the research demonstrates machine learning’s potential to inform zoning and development decisions that align with emissions goals. By surfacing high-impact variables, the approach could help cities like Philadelphia customize energy policies as they chart a path toward carbon neutrality.