Cited by Interpretability assessment of convolutional neural network-based fault diagnosis for air handling units working in three seasons

Neural ordinary differential equations-based approach for enhanced building energy modeling on small datasets DOI

Zhihao Ma, Gang Yi Jiang, Jianli Chen

et al.

Building Simulation, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

Language: Английский

Citations

Model Interpretation and Interpretability Performance Evaluation of Graph Convolutional Network Fault Diagnosis for Air Handling Units DOI

Guannan Li, Zhang Le, Lingzhi Yang

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112048 - 112048

Published: Feb. 1, 2025

Language: Английский

Citations

Feature-engineering-enhanced multiple simultaneous fault detection in residential air conditioners DOI

Jiangyan Liu,

Xinxiang Zhao,

Guannan Li

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112671 - 112671

Published: Feb. 1, 2025

Language: Английский

Citations

A graph neural network-based fault detection framework for combined building-integrated photovoltaics, energy storage, and building flexibility control systems DOI

Xiaoyue Yi,

Haotian Li, Llewellyn Tang

et al.

Journal of Physics Conference Series, Journal Year: 2025, Volume and Issue: 3001(1), P. 012012 - 012012

Published: April 1, 2025

Abstract Fault detections among building-integrated photovoltaics (BIPV), battery energy storage (BES), and building flexibility (BEF) systems are essential during the phase of operation maintenance. Despite linkage these three systems, most existing fault detection methods focused on individual neglecting interconnection between BIPV, BES, BEF systems. These make results relatively isolated lack reliability, which might cause additional labour cost. This study presented a framework that illustrates way applying graph neural network (GNN) to potentially detect failure infer based an ontology established according standards. The structure was built topology ontology, system operational data were input into corresponding nodes edges. mapped then pre-processed sent GNN model, with edges maintaining structure. After processing by each node be used failures proposed offers valuable insights management practices within combined

Language: Английский

Citations

Effects of various information scenarios on layer-wise relevance propagation-based interpretable convolutional neural networks for air handling unit fault diagnosis DOI

Chenglong Xiong, Guannan Li, Ying Yan

et al.

Building Simulation, Journal Year: 2024, Volume and Issue: 17(10), P. 1709 - 1730

Published: Aug. 19, 2024

Language: Английский

Citations

Interpretability assessment of convolutional neural network-based fault diagnosis for air handling units working in three seasons DOI

Chenglong Xiong, Hu Yunpeng, Guannan Li

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: unknown, P. 114876 - 114876

Published: Oct. 1, 2024

Language: Английский

Citations