How Digital Twin Technology Reshapes the Resilience of Building Energy Systems: A New Paradigm from Optimization to Self-Healing

This article discusses a self-healing digital twin framework for building energy systems under climate stress, with stability maintenance as the core goal. Through the synergy of physics-informed models, data assimilation, and model predictive control, it achieves energy consumption reduction and thermal comfort guarantee under extreme conditions, promoting a paradigm shift from 'optimization priority' to 'resilience priority'.

The frequency of global extreme weather events is increasing. Traditional building energy management systems, which focus on optimization, expose vulnerabilities under extreme conditions: model mismatch amplifies errors, sensor drift and disturbances lead to hidden degradation, and the lack of explicit consideration of stability makes them prone to unstable states. Existing digital twin research mostly focuses on optimization, ignoring the problem of stability maintenance under extreme conditions.

The new framework takes stability maintenance as its primary goal and includes three key layers: 1. Physics-informed neural networks integrate prior knowledge such as heat conduction equations with data-driven approaches, balancing flexibility and physical consistency; 2. Data assimilation technology continuously fuses sensor data to correct model parameters, enabling online adaptation; 3. Closed-loop intervention with stability constraints ensures that control actions keep the system state within a stable region.

Experiments in heatwave scenarios show: total energy consumption reduced by 11.2%, thermal comfort violation time reduced by 56.2%; stable operation was maintained under multi-compound disturbance scenarios; ablation experiments indicate that data assimilation contributed the most, followed by physics-informed modeling, and stability constraints improved reliability (with a slight loss of optimality).

The self-healing capability is divided into three layers: 1. Detection layer: monitors residuals between the physical system and the virtual model, identifies anomalies when thresholds are exceeded; 2. Diagnosis layer: estimates the type, location, and severity of anomalies through data assimilation; 3. Repair layer: achieves adaptive repair via online parameter updates, control strategy adjustments, or logic reconstruction.

Practical significance: provides proactive adaptation solutions for building energy systems under climate uncertainty, and offers references for resilience design in fields such as power grids and transportation; future directions: explore issues like coordinated control of building clusters, trade-offs among multiple stakeholders, and integration of human experience into automated decision-making.