摘要 : The time delays in the HVAC system are common, which seriously affect time series prediction and pre-control command implementation, so identifying them is promising research. The existing methods of identifying timedelays mainly ... 展开 The time delays in the HVAC system are common, which seriously affect time series prediction and pre-control command implementation, so identifying them is promising research. The existing methods of identifying timedelays mainly focus on establishing physical or dynamical models first and then conducting control variable experiments, which don't facilitate engineering. Therefore, we develop a model-free identification method rooted in an information-theoretic framework by introducing transfer entropy (TE) into the HVAC field. The suggested multivariate TE method can pick out each time-delay characteristic by mining monitoring data. Compared with correlation coefficients, it can filter redundant information between variables and discern the nonlinearity. Among them, to estimate multivariate TE well, a kernel estimator is improved. It performs the precise detection ability, low computational burden and strong robustness, compared with the original and knearest-neighbor (KNN) estimator. Besides, for an unacquainted HVAC system, a hierarchical optimization algorithm combining the Nash-optimization algorithm with a second-order oscillatory particle swarm optimization (SOPSO) algorithm is proposed to identify its time delays, where the accuracy and time cost are improved. Lastly, the above-mentioned methods are validated with simulated and real time series. This work is enlightening and has a further reference to identifying time delays in HVAC systems. 收起