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

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

Zhihao Ma, Gang Yi Jiang, Jianli Chen

и другие.

Building Simulation, Год журнала: 2025, Номер unknown

Опубликована: Апрель 11, 2025

Язык: Английский

Процитировано

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

Chenglong Xiong, Hu Yunpeng, Guannan Li

и другие.

Energy and Buildings, Год журнала: 2024, Номер unknown, С. 114876 - 114876

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

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

Jiangyan Liu,

Xinxiang Zhao,

Guannan Li

и другие.

Building and Environment, Год журнала: 2025, Номер unknown, С. 112671 - 112671

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

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

Guannan Li, Zhang Le, Lingzhi Yang

и другие.

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112048 - 112048

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

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

и другие.

Journal of Physics Conference Series, Год журнала: 2025, Номер 3001(1), С. 012012 - 012012

Опубликована: Апрель 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

Язык: Английский

Процитировано

Advanced fault detection, diagnosis and prognosis in HVAC systems: Lifecycle insight, key challenges, and promising approaches DOI

Zhanwei Wang, Yi‐Xian Qin, Yifan Kong

и другие.

Renewable and Sustainable Energy Reviews, Год журнала: 2025, Номер 219, С. 115867 - 115867

Опубликована: Май 27, 2025

Язык: Английский

Процитировано

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

и другие.

Building Simulation, Год журнала: 2024, Номер 17(10), С. 1709 - 1730

Опубликована: Авг. 19, 2024

Язык: Английский

Процитировано