| 1 |
Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving
Guo YB, Tan ZH, Chen HX, Li GN, Wang JY, Huang RG, Liu JY, Ahmad T
Applied Energy, 225, 732, 2018 |
| 2 |
A machine learning bayesian network for refrigerant charge faults of variable refrigerant flow air conditioning system
Hu M, Chen HX, Shen LM, Li GN, Guo YB, Li HR, Li J, Hu WJ
Energy and Buildings, 158, 668, 2018 |
| 3 |
Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions
Li GN, Hu YP, Chen HX, Li HR, Hu M, Guo YB, Liu JY, Sun SB, Sun M
Applied Energy, 185, 846, 2017 |
| 4 |
Experimental validation of the simulation module of the water-cooled variable refrigerant flow system under cooling operation
Li YM, Wu JY, Shiochi S
Applied Energy, 87(5), 1513, 2010 |
| 5 |
Modeling and energy simulation of the variable refrigerant flow air conditioning system with water-cooled condenser under cooling conditions
Li YM, Wu JY, Shiochi S
Energy and Buildings, 41(9), 949, 2009 |
| 6 |
Energy simulation in the variable refrigerant flow air-conditioning system under cooling conditions
Zhou YP, Wu JY, Wang RZ, Shiochi S
Energy and Buildings, 39(2), 212, 2007 |
