[1] [1] WAN H, CAO T, HWANG Y, et al. A review of recent advancements of variable refrigerant flow air-conditioning systems[J]. Applied Thermal Engineering, 2020, 169:114893.

[3] [3] ZHANG G, XIAO H, ZHANG P, et al. Review on recent developments of variable refrigerant flow systems since 2015[J]. Energy and Buildings, 2019, 198: 444-466.

[8] [8] CAO P, ZHANG S, TANG J. Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning[J]. IEEE Access, 2018, 6: 26241-26253.

[9] [9] YU X, CHEN W, WU C, et al. Rolling bearing fault diagnosis based on domain adaptation and preferred feature selection under variable working conditions[J]. Shock and Vibration, 2021, 99: 1-27.

[10] [10] LIU J, ZHANG Q, LI X, et al. Transfer learning-based strategies for fault diagnosis in building energy systems[J]. Energy and Buildings, 2021, 250: 111256.

[11] [11] ZHU X, CHEN K, ANDUV B, et al. Transfer learning based methodology for migration and application of fault detection and diagnosis between building chillers for improving energy efficiency[J]. Building and Environment, 2021, 200: 107957.

[12] [12] FAN C, SUN Y, XIAO F, et al. Statistical investigations of transfer learning-based methodology for short-term building energy predictions[J]. Applied Energy, 2020, 262:114499.

[13] [13] MA J, CHENG J C P, JIANG F, et al. A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data[J]. Energy and Buildings, 2020, 216: 109-114.

[16] [16] LU W, LIANG B, CHENG Y, et al. Deep model based domain adaptation for fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2016, 64(3): 2296-2305.

[18] [18] FANG X, GONG G, LI G, et al. A hybrid deep transfer learning strategy for short term cross-building energy prediction[J]. Energy, 2021, 215: 1-6.

[20] [20] XIE Y, SUN W, REN M, et al. Stacking ensemble learning models for daily runoff prediction using 1D and 2D CNNs[J]. Expert Systems with Applications, 2023, 217:119469.

[21] [21] XU H, LI W, CAI Z. Analysis on methods to effectively improve transfer learning performance[J]. Theoretical Computer Science, 2023, 940: 90-107.

[22] [22] YUAN F, ZHANG Z, FANG Z. An effective CNN and Transformer complementary network for medical image segmentation[J]. Pattern Recognition, 2023, 136: 109228.

[23] [23] ELIKKANAT A, MALLIAROS F D. Topic-aware latent models for representation learning on networks[J]. Pattern Recognition Letters, 2021, 144: 89-96.

[24] [24] QIAO J, WANG G, LI W, et al. An adaptive deep Q-learning strategy for handwritten digit recognition[J]. Neural Networks, 2018, 107: 61-71.