Автор: Hanlong Wan, Tao Cao,Yunho Hwang, Se-Dong Chang, Young-Jin Yoon
Год: 2020 Язык: Английский Тип: Статьи
Conventionally, researchers conducted field tests and modeling works for the Variable Refrigerant Flow system separately. In this study, we used the compressor model as a case study to illustrate a novel ap- proach to integrate field tests and modeling works. Field tests in an office building were conducted to collect data. For mass flow rate prediction, three traditional models, including the 20-coefficient model, the efficiency-based model, and the efficiency-based 20-coefficient model, and three machine-learning- based models including Support Vector Regression, Neural Network, and Random Forest, were investi- gated and compared. We found that the efficiency-based 20-coefficient model and the Support Vector Regression model had a higher accuracy (the mean relative errors were 0.11% and 0.15%, and the co- efficient of variation of the root mean-square-error were 0.20 and 0.23) and lower uncertainty within 0.2 g •s −1 for steady-state operation prediction. However, all six models failed to predict accurately the beginning part of the transient process. A tens-of-seconds delay existed between the predicted values and the experiment values. We applied the Convolutional-Neural-Network-based model to address this problem. The mean relative error of this model is reduced to 2% for dynamic simulation. In summary, we recommend the efficiency-based 20-coefficient model, and the Support Vector Regression model for the steady-state compressor model development, while the Convolutional-Neural-Network-based model is recommended for transient model development. For the power consumption prediction, 20-coefficient and Neural Network models can predict transient data well. The process of predicting the capacity is the same as the process of mass flow rate prediction.