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[1] Across working conditions fault diagnosis for chillers based on IoT intelligent agent with deep learning model Energy and Buildings, 2022,
[2] Applying deep learning-based regional feature recognition from macro-scale image to assist energy saving and emission reduction in industrial energy systems Journal of Advanced Research, 2022,
[3] Knowledge-infused deep learning diagnosis model with self-assessment for smart management in HVAC systems Energy, 2023,
[4] Endowing data-driven models with rejection ability: Out-of-distribution detection and confidence estimation for black-box models of building energy systems Energy, 2023,
[5] Machine learning enhanced inverse modeling method for variable speed air conditioning systems Méthode de modélisation inverse améliorée par apprentissage machine pour les systèmes de conditionnement d'air à vitesse variable International Journal of Refrigeration, 2020,
[6] Deep learning-based image recognition method for on-demand defrosting control to save energy in commercial energy systems APPLIED ENERGY, 2022,
[7] Partial domain adaption based prediction calibration methodology for fault detection and diagnosis of chillers under variable operational condition scenarios BUILDING AND ENVIRONMENT, 2022,
[8] Digital twins model and its updating method for heating, ventilation and air conditioning system using broad learning system algorithm ENERGY, 2022,
[9] Across working conditions fault diagnosis for chillers based on IoT intelligent agent with deep learning model Energy and Buildings, 2022,
[10] Diagnosis for multiple faults of chiller using ELM-KNN model enhanced by multi-label learning and specific feature combinations Building and Environment, 2022,
[11]duzhimin. 基于分析的空调系统运行控制的多目标优化 制冷学报, 2016,
[12]duzhimin. 基于数据包络分析的空调系统控制策略评估 暖通空调, 2018,
[13] Effect of common faults on the performance of different types of vapor compression systems Applied Thermal Engineering, 2016,
[14] 基于长短期记忆神经网络的数据中心空调系统传感器故障诊断 化工学报, 2018,
[15]duzhimin. 基于?流分析的空调冷水系统设计匹配水平评估方法研究 暖通空调, 2019,
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