Chiller System Modeling using PSO Optimization based NARX approach

The heating, ventilation, and air conditioning(HVAC) system consumes a large amount of energy in buildings. Accurate modeling of the HVAC refrigeration room system is crucial for building temperature control and optimization of energy consumption. In this paper, Levenberg Marquardt (LM) algorithm and particle swarm optimization (PSO) algorithm are used to establish a nonlinear autoregressive neural network model (PSO-NARX) for the modeling of water chillers in HVAC systems. NARX is a model used to describe nonlinear discrete systems. At the same time, using particle swarm optimization algorithm can improve the accuracy of the prediction model. The experimental results show that the PSO-NARX model can effectively model and predict the chiller model, and its performance is better compared to traditional DNN neural networks.