A review of machine learning in building load prediction
The surge of machine learning and increasing data accessibility in buildings provide great
opportunities for applying machine learning to building energy system modeling and …
opportunities for applying machine learning to building energy system modeling and …
A review of data-driven fault detection and diagnostics for building HVAC systems
With the wide adoption of building automation system, and the advancement of data,
sensing, and machine learning techniques, data-driven fault detection and diagnostics …
sensing, and machine learning techniques, data-driven fault detection and diagnostics …
A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems
Abstract Faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings
result in significant energy waste in building operation. With fast-growing sensing data …
result in significant energy waste in building operation. With fast-growing sensing data …
[HTML][HTML] Digital twin enabled fault detection and diagnosis process for building HVAC systems
The emerging concept of digital twins outlines the pathway towards intelligent buildings.
Although abundant building data carries an overwhelming amount of information, if not well …
Although abundant building data carries an overwhelming amount of information, if not well …
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 …
models for HVAC systems in the case of incomplete labeled data. However, the relationship …
Feature selection techniques for machine learning: a survey of more than two decades of research
Learning algorithms can be less effective on datasets with an extensive feature space due to
the presence of irrelevant and redundant features. Feature selection is a technique that …
the presence of irrelevant and redundant features. Feature selection is a technique that …
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 …
diagnosis model for building energy system still has good diagnostic performance with …
Challenges and opportunities of machine learning control in building operations
Abstract Machine learning control (MLC) is a highly flexible and adaptable method that
enables the design, modeling, tuning, and maintenance of building controllers to be more …
enables the design, modeling, tuning, and maintenance of building controllers to be more …
Feature extraction-reduction and machine learning for fault diagnosis in PV panels
With the rapid expansion and installation of Photovoltaic (PV) power plants, developing a
proper Fault Detection and Diagnosis (FDD) strategy has become a significant issue within …
proper Fault Detection and Diagnosis (FDD) strategy has become a significant issue within …
A new froth image classification method based on the MRMR-SSGMM hybrid model for recognition of reagent dosage condition in the coal flotation process
W Cao, R Wang, M Fan, X Fu, H Wang, Y Wang - Applied Intelligence, 2022 - Springer
Intelligent separation is a core technology in the transformation, upgradation, and high-
quality development of coal. Realising the intelligent recognition and accurate classification …
quality development of coal. Realising the intelligent recognition and accurate classification …