A model-free method for identifying time-delay characteristics of HVAC system based on multivariate transfer entropy

Abstract

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 time-delays 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 k-nearest-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.