Chengliang Xu
- : Fault diagnosis for cross-building energy systems based on transfer learning and model interpretation. In: Journal of Building Engineering.
- (2024): An improved transfer learning strategy for short_term cross-building energy prediction using data incremental. In: Building Simulation, v. 17, n. 1 (Januar 2024).
- (2023): Quantile regression based probabilistic forecasting of renewable energy generation and building electrical load: A state of the art review. In: Journal of Building Engineering, v. 79 (November 2023).
- (2023): Interpretation and explanation of convolutional neural network-based fault diagnosis model at the feature-level for building energy systems. In: Energy and Buildings, v. 295 (September 2023).
- (2023): Interpretation of convolutional neural network-based building HVAC fault diagnosis model using improved layer-wise relevance propagation. In: Energy and Buildings, v. 286 (Mai 2023).
- (2022): A spatial-temporal layer-wise relevance propagation method for improving interpretability and prediction accuracy of LSTM building energy prediction. In: Energy and Buildings, v. 271 (September 2022).
- (2021): An improved stacking ensemble learning-based sensor fault detection method for building energy systems using fault-discrimination information. In: Journal of Building Engineering, v. 43 (November 2021).
- (2020): A hybrid data mining approach for anomaly detection and evaluation in residential buildings energy data. In: Energy and Buildings, v. 215 (Mai 2020).
- (2020): Study on deep reinforcement learning techniques for building energy consumption forecasting. In: Energy and Buildings, v. 208 (Februar 2020).
- (2019): Modal decomposition based ensemble learning for ground source heat pump systems load forecasting. In: Energy and Buildings, v. 194 (Juli 2019).
- (2017): An analytical model for deformation and damage of rectangular laminated glass under low-velocity impact. In: Composite Structures, v. 176 (September 2017).
- (2019): Improving prediction performance for indoor temperature in public buildings based on a novel deep learning method. In: Building and Environment, v. 148 (Januar 2019).
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