AI-Enhanced Urban Building Energy Modeling for Health-Driven Decarbonization in Vulnerable Communities

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers can leverage the framework for integrating health metrics into building energy modeling and explore transferability to other climates and building stocks.

🏢実務担当者:Sustainability teams can use the method to prioritize retrofit investments that maximize energy savings and health benefits in underserved communities.

🏛政策担当者:Policymakers can adopt the approach to design targeted retrofit programs that address both decarbonization and environmental justice.

Retrofitting existing residential buildings is a critical strategy for achieving urban decarbonization while addressing public health disparities, particularly in communities disproportionately affected by environmental and socioeconomic stressors. This study presents a scalable urban building energy modeling framework that integrates physics-based simulations with machine learning to evaluate and prioritize health-driven retrofit strategies across residential building stocks. Synthetic datasets were generated through parametric simulations of representative building archetypes and retrofit scenarios, capturing variations in envelope performance, HVAC systems, infiltration rates, and ventilation strategies. Machine learning models were trained as surrogate predictors of building energy performance, enabling the rapid evaluation of retrofit impacts. A range of algorithms—including decision trees, random decision forests, gradient-boosting machines, support vector machines, k-nearest neighbors, and artificial neural networks—were evaluated. An artificial neural network implemented as a multilayer perceptron was selected for further analysis due to its strong predictive performance (R2 = 0.94) and ability to capture complex nonlinear relationships among retrofit variables. The final model used the Port optimization algorithm for stable convergence and improved generalization. The framework is applied to Seattle’s Duwamish Valley, a community experiencing disproportionate environmental and health burdens, and is generalizable and transferable to other cities with comparable residential building stocks across a range of climatic and environmental contexts. The results highlight retrofit priorities—particularly infiltration reduction, HVAC upgrades, and improved envelope performance—that deliver co-benefits for energy efficiency, indoor environmental quality, and occupant health. The results demonstrate that machine learning-enhanced physics-based UBEM can significantly accelerate retrofit evaluation while preserving the interpretability of simulation-based approaches. The proposed framework provides a scalable approach for identifying health-informed retrofit pathways that support equitable urban decarbonization.

• crossref https://doi.org/10.3390/architecture6020084first seen 2026-06-04 05:31:19