[6] [6] YOUNGHOON K, JUNG-HO H. Development of a method of real-time building energy simulation for efficient predictive control[J]. Energy Conversion and Management, 2016, 113: 220-229.

[7] [7] CHEN Y X, YANG C H, PAN X, et al. Design and operation optimization of multi-chiller plants based on energy performance simulation[J]. Energy and Buildings, 2020, 222: 110100.

[8] [8] ABDULLA A, AHMAD K S. Combined heat and power system based on solid oxide fuel cells for low energy commercial buildings in Qatar[J]. Sustainable Energy Technologies and Assessments, 2021, 48: 101615.

[9] [9] NG L C, DOLS W S, EMMERICH S J. Evaluating potential benefits of air barriers in commercial buildings using NIST infiltration correlations in EnergyPlus[J]. Building and Environment, 2021, 196: 107-114.

[14] [14] CHEN Z W, DENG Q, REN H, et al. A new energy consumption prediction method for chillers based on GraphSAGE by combining empirical knowledge and operating data[J]. Applied Energy, 2022, 310: 118410.

[15] [15] PANAHIZADEH F, HAMZEHEI M, FARZANEH-GORD M, et al. Evaluation of machine learning-based applications in forecasting the performance of single effect absorption chiller network[J]. Thermal Science and Engineering Progress, 2021, 26: 101087.

[20] [20] WANG Y Z, LI Z F, CHEN H X, et al. Research on diagnostic strategy for faults in VRF air conditioning system using hybrid data mining methods[J]. Energy and Buildings, 2021, 247: 111144.

[22] [22] JANG J H, HAN J M, LEIGH S B. Prediction of heating energy consumption with operation pattern variables for non-residential buildings using LSTM networks[J]. Energy and Buildings, 2022, 255: 111647.