Hybrid model using Bayesian neural network for variable refrigerant flow system
Ki Uhn Ahn, Cheol Soo Park, Kyung‐Jae Kim, Deuk-Woo Kim, Chang-U Chae
Abstract
This study introduces a hybrid model that combines physics and machine learning (ML) models to describe the behaviour of variable refrigerant flow (VRF) systems. The standalone ML model was developed with identical data and conditions for comparison between the hybrid and ML models. A Bayesian neural network (BNN) was used for both the models, and the predictive abilities and uncertainties were investigated. For the experimental dataset, the predictive performances of both models were similar. For example, the predictive performance of the hybrid and ML models showed mean absolute error of 0.73 and 0.78 kW, respectively. However, the epistemic uncertainty of the hybrid model quantified using the BNN was 36.4% lower than that of the ML model. A parametric study showed that the hybrid model combined with the physics model can achieve better generalization performance than the ML model, yielding results that are more reliable and physically explainable.