Robust building energy retrofit evaluation under uncertainty: An interpretable machine learning approach

Abstract

Improving the energy efficiency and thermal comfort of existing residential buildings is essential for sustainable urban development. However, uncertainties in occupant behaviors and building constructions pose challenges to optimizing retrofit strategies. This study presents an integrated approach combining physics-based energy simulation, interpretable machine learning, and multi-objective optimization to quantify these uncertainties and identify optimal retrofit strategies. Latin Hypercube Sampling was used to generate representative variability in occupant and construction parameters, while Extreme Gradient Boosting (XGBoost) served as a surrogate model to reduce the computational burden of detailed simulations. Shapley Additive exPlanations (SHAP) and standardized regression coefficients were applied to enhance model interpretability and identify key features influencing energy and comfort performance. The approach was applied to representative Canadian single-detached houses across four climate zones and three HVAC systems: natural gas furnaces, air source heat pumps, and ground source heat pumps. XGBoost achieved R 2 values above 0.85 for energy consumption and 0.95 for discomfort hours in most scenarios. Heating setpoint temperature, airtightness, and equipment power density emerged as dominant factors, with their relative importance varying by HVAC types and climate conditions. Passive retrofits were more impactful in colder zones, while behavioral adjustments were more effective in milder climates. The surrogate-based model reduced computation time by 89.7%. Pareto optimal solutions demonstrated up to 48% energy savings and 32% discomfort hour reductions. The proposed method offers actionable insights for policymakers and practitioners to implement targeted, efficient, and adaptive retrofit strategies under uncertainty.

Topics & Concepts