Hybrid model using Bayesian neural network for variable refrigerant flow system

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.

KEYWORDS:

• Machine learning
• hybrid model
• Bayesian neural network
• variable refrigerant flow system

Acknowledgement

This research was supported by Samsung Electronics. The authors wish to express their gratitude for the support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport [grant number 21SHTD-B157018-02].