Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

Q Zhang, H Yan, Y Liu - Renewable and Sustainable Energy Reviews, 2024 - Elsevier
Abstract A Dynamic Bayesian network (DBN) model for solar power generation forecasting
in photovoltaic (PV) solar plants is proposed in this paper. The key idea is to fuse sensor …

Data‐driven fault diagnosis approaches for industrial equipment: A review

AR Sahu, SK Palei, A Mishra - Expert Systems, 2024 - Wiley Online Library
Undetected and unpredicted faults in heavy industrial machines/equipment can lead to
unwanted failures. Therefore, prediction of faults puts paramount importance on maintaining …

An enhanced principal component analysis method with Savitzky–Golay filter and clustering algorithm for sensor fault detection and diagnosis

S Wen, W Zhang, Y Sun, Z Li, B Huang, S Bian, L Zhao… - Applied Energy, 2023 - Elsevier
Sensors are critical components of heating, ventilation, and air-conditioning systems. Sensor
faults can impact control regulations, resulting in an uncomfortable indoor environment and …

Cause analysis of hot work accidents based on text mining and deep learning

H Xu, Y Liu, CM Shu, M Bai, M Motalifu, Z He… - Journal of loss …, 2022 - Elsevier
Hot work accidents have significant consequences. Admittedly, preventing hot work
accidents requires managers to analyze the accident profoundly and learn from the requisite …

Deep learning GAN-based data generation and fault diagnosis in the data center HVAC system

Z Du, K Chen, S Chen, J He, X Zhu, X Jin - Energy and Buildings, 2023 - Elsevier
The automated fault diagnosis and smart management of heating, ventilation and air
conditioning (HVAC) system is essential to the reliability of data centers. The machine …

Experimental study on performance assessments of HVAC cross-domain fault diagnosis methods oriented to incomplete data problems

Q Zhang, Z Tian, Y Lu, J Niu, C Ye - Building and Environment, 2023 - Elsevier
The cross-domain fault diagnosis (CDFD) method can provide accurate fault diagnosis
models for HVAC systems in the case of incomplete labeled data. However, the relationship …

A study on transfer learning in enhancing performance of building energy system fault diagnosis with extremely limited labeled data

Q Zhang, Z Tian, J Niu, J Zhu, Y Lu - Building and Environment, 2022 - Elsevier
Transfer learning has been proved to be a feasible way to ensure that the data-driven fault
diagnosis model for building energy system still has good diagnostic performance with …

Causal discovery-based external attention in neural networks for accurate and reliable fault detection and diagnosis of building energy systems

C Zhang, X Tian, Y Zhao, T Li, Y Zhou, X Zhang - Building and Environment, 2022 - Elsevier
In the era of big data, data-driven models have become the most promising fault detection
and diagnosis solutions to building energy systems, due to their high accuracy and good …

Domain adaptation deep learning and its TS diagnosis networks for the cross-control and cross-condition scenarios in data center HVAC systems

Z Du, X Liang, S Chen, P Li, X Zhu, K Chen, X Jin - Energy, 2023 - Elsevier
Deep learning has the inspiring potential for artificial intelligence (AI) control in data centers
of smart city. However, deep learning-based method is limited when the application …

[HTML][HTML] A multidimensional Bayesian architecture for real-time anomaly detection and recovery in mobile robot sensory systems

M Castellano-Quero, M Castillo-López… - … Applications of Artificial …, 2023 - Elsevier
For mobile robots to operate in an autonomous and safe manner they must be able to
adequately perceive their environment despite challenging or unpredictable conditions in …