Zhimin Du 
- (2023): Knowledge-extracted deep learning diagnosis and its cloud-based management for multiple faults of chiller. In: Building and Environment, v. 235 (May 2023).
- (2023): Deep learning GAN-based data generation and fault diagnosis in the data center HVAC system. In: Energy and Buildings, v. 289 (June 2023).
- (2023): Interpretable mechanism mining enhanced deep learning for fault diagnosis of heating, ventilation and air conditioning systems. In: Building and Environment, v. 237 (June 2023).
- (2022): Across working conditions fault diagnosis for chillers based on IoT intelligent agent with deep learning model. In: Energy and Buildings, v. 268 (August 2022).
- (2022): Partial domain adaption based prediction calibration methodology for fault detection and diagnosis of chillers under variable operational condition scenarios. In: Building and Environment, v. 217 (June 2022).
- (2022): Diagnosis for multiple faults of chiller using ELM-KNN model enhanced by multi-label learning and specific feature combinations. In: Building and Environment, v. 214 (April 2022).
- (2021): Adaptive data-driven optimization of chiller loading with domain knowledge. In: Science and Technology for the Built Environment, v. 27, n. 9 (August 2021).
- (2021): Transfer learning based methodology for migration and application of fault detection and diagnosis between building chillers for improving energy efficiency. In: Building and Environment, v. 200 (August 2021).
- (2021): Fault detection and diagnosis for the screw chillers using multi-region XGBoost model. In: Science and Technology for the Built Environment, v. 27, n. 5 (April 2021).
- (2021): Multi-sensor information fusion based control for VAV systems using thermal comfort constraints. In: Building Simulation, v. 14, n. 4 (February 2021).
- (2019): Development and application of hardware-in-the-loop simulation for the HVAC systems. In: Science and Technology for the Built Environment, v. 25, n. 10 (26 November 2019).
- (2015): Coordinated optimization of the variable refrigerant flow and variable air volume combined air-conditioning system in heating conditions. In: Science and Technology for the Built Environment, v. 21, n. 7 (October 2015).
- (2007): Detection and diagnosis for multiple faults in VAV systems. In: Energy and Buildings, v. 39, n. 8 (August 2007).
- (2009): A robot fault diagnostic tool for flow rate sensors in air dampers and VAV terminals. In: Energy and Buildings, v. 41, n. 3 (March 2009).
- (2011): Optimal control strategies for multi-chiller system based on probability density distribution of cooling load ratio. In: Energy and Buildings, v. 43, n. 10 (October 2011).
- (2012): Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal. In: Energy and Buildings, v. 44 (January 2012).
- (2014): Simulation of variable refrigerant flow air conditioning system in heating mode combined with outdoor air processing unit. In: Energy and Buildings, v. 68 (January 2014).
- (2014): Sensor fault detection and its efficiency analysis in air handling unit using the combined neural networks. In: Energy and Buildings, v. 72 (April 2014).
- (2014): The method of evaluating operation performance of HVAC system based on exergy analysis. In: Energy and Buildings, v. 77 (July 2014).
- (2016): The evaluation of operation performance of HVAC system based on the ideal operation level of system. In: Energy and Buildings, v. 110 (January 2016).
- (2018): Evaluation of operation performance of a multi-chiller system using a data-based chiller model. In: Energy and Buildings, v. 172 (August 2018).
- (2019): Fault diagnosis based operation risk evaluation for air conditioning systems in data centers. In: Building and Environment, v. 163 (October 2019).
- (2015): Temperature sensor placement optimization for VAV control using CFD–BES co-simulation strategy. In: Building and Environment, v. 85 (February 2015).
- (2014): Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis. In: Building and Environment, v. 73 (March 2014).
- (2010): A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis. In: Building and Environment, v. 45, n. 12 (December 2010).
- (2007): Fault detection and diagnosis based on improved PCA with JAA method in VAV systems. In: Building and Environment, v. 42, n. 9 (September 2007).
Structurae cooperates with
