From Optimization to Stability Preservation: A Self-Healing, Action-Bearing Digital Twin for Climate-Stressed Building Energy Systems

Climate change exposes building energy systems to conditions beyond conventional control assumptions, where heatwaves, occupancy shifts, sensor drift, and degradation erode stability over time. This study proposes a self-healing digital twin that preserves system behavior within a defined stability envelope by integrating physics-based modeling, data assimilation, and adaptive response mechanisms. Self-healing is defined as preventing instability under non-stationary uncertainty. The framework is validated in closed-loop simulations across multiple stress scenarios, demonstrating robust stability under compound disturbances. Performance is benchmarked against rule-based control, classical model predictive control (MPC), and conventional digital twin supervision using a locked paired evaluation protocol with 30 matched realizations per scenario. Under compound uncertainty, the proposed framework reduces cumulative thermal comfort violation from 27.8 ± 3.6 (MPC) to 13.9 ± 1.9 , corresponding to a 50.0% reduction. The time outside the comfort band decreases from 11.2% to 4.6%. Drift accumulation, measured through residual energy, is reduced by 56.2%, while control smoothness improves by 39.5%. Total energy consumption is reduced by 3.38%, and mean recovery time following major disturbances decreases from 11.8 ± 1.8 h to 4.5 ± 0.9 h. Ablation and failure analyses show that stability preservation arises from the coordinated interaction of data assimilation, envelope-based stabilization, and structural adaptation. The results indicate that resilience-oriented stability management provides a more reliable operational paradigm than optimization-centric control for building energy systems under climate-driven uncertainty.