A Predefined-Time Consensus Effectiveness-Evaluation Strategy for Nerworked Air Conditioning Systems

The growing complexity of modern intelligent buildings necessitates advanced consensus effectiveness-evaluation strategies for networked air conditioning systems (NACSs) to address dynamic operational demands while ensuring quantifiable system-wide effectiveness metrics. Traditional distributed optimization methods, though capable of balancing energy-comfort objectives, suffer from unpredictable convergence delays, severely limiting their applicability in time-critical scenarios requiring guaranteed evaluation deadlines and precise load coordination. To bridge this gap, this paper proposes a predefined-time consensus effectiveness-evaluation framework that synergizes distributed convex optimization with time-constrained consensus protocols. The framework guarantees rigorous convergence to unified evaluation outcomes within user-specified time bounds, independent of initial states, thereby enabling time-predictable thermal load allocation and adaptability assessment. By integrating time-critical gradient dynamics and topology-aware consensus mechanisms, the algorithm ensures compliance with strict scheduling requirements for HVAC performance evaluation. Theoretical proofs leveraging Lyapunov stability theory and algebraic graph analysis formally establish the predefined-time convergence, explicitly characterizing its dependency on network topology and edge weight configurations. In the end, simulations are conducted to validate the performance of the proposed algorithm.