Huanxin Chen
- (2023): Refrigerant charge fault diagnosis strategy for VRF systems based on stacking ensemble learning. Dans: Building and Environment, v. 234 (avril 2023).
- (2022): A robust VRF fault diagnosis method based on ensemble BiLSTM with attention mechanism: Considering uncertainties and generalization. Dans: Energy and Buildings, v. 269 (août 2022).
- (2021): Research on diagnostic strategy for faults in VRF air conditioning system using hybrid data mining methods. Dans: Energy and Buildings, v. 247 (septembre 2021).
- (2020): A review on machine learning forecasting growth trends and their real-time applications in different energy systems. Dans: Sustainable Cities and Society, v. 54 (mars 2020).
- (2018): Utility companies strategy for short-term energy demand forecasting using machine learning based models. Dans: Sustainable Cities and Society, v. 39 (mai 2018).
- (2013): Parametrical analysis on characteristics of airflow generated by fabric air dispersion system in penetration mode. Dans: Energy and Buildings, v. 67 (décembre 2013).
- (2020): Simulated Annealing Wrapped Generic Ensemble Fault Diagnostic Strategy for VRF System. Dans: Energy and Buildings, v. 224 (octobre 2020).
- (2016): A statistical training data cleaning strategy for the PCA-based chiller sensor fault detection, diagnosis and data reconstruction method. Dans: Energy and Buildings, v. 112 (janvier 2016).
- (2016): A sensor fault detection and diagnosis strategy for screw chiller system using support vector data description-based D-statistic and DV-contribution plots. Dans: Energy and Buildings, v. 133 (décembre 2016).
- (2017): Optimization of support vector regression model based on outlier detection methods for predicting electricity consumption of a public building WSHP system. Dans: Energy and Buildings, v. 151 (septembre 2017).
- (2018): Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches. Dans: Energy and Buildings, v. 166 (mai 2018).
- (2020): Study on deep reinforcement learning techniques for building energy consumption forecasting. Dans: Energy and Buildings, v. 208 (février 2020).
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