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Leveraging graph convolutional networks for semi-supervised fault diagnosis of hvac systems in data-scarce contexts

建筑模拟(英文) 2023年第8期16卷 1499-1517页

作者： Cheng Fan Yiwen Lin Marco Savino Piscitelli Roberto Chiosa Huilong Wang Alfonso Capozzoli Yuanyuan Ma Key Laboratory for Resilient Infrastructures of Coastal Cities Ministry of EducationShenzhen UniversityShenzhenChina Sino-Australia Joint Research Center in BIM and Smart Construction Shenzhen UniversityShenzhenChina College of Civil and Transportation Engineering Shenzhen UniversityShenzhenChina Department of Energy TEBE research groupBAEDA LabPolitecnico di TorinoTorinoItaly

The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart hvac fault detection and diagnosis.In practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data.To tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings.More specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault classifications.Data experiments have been designed to evaluate the values of the methods proposed.Three datasets on hvac air-side operations have been used to ensure the generalizability of results obtained.Different data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures.The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.

关键词： fault detection and diagnosis graph convolutional networks semi-supervised learning hvac systems machine learning

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Comparative analysis of technical requirements for Heating,Ventilating,and Air Conditioning(hvac)systems in high-biocontainment facility standards

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Biosafety and Health 2023年第1期5卷 1-7页

作者： Lei Liang Xinfang Hong Bing Lu Xuedong Yu Guoqing Cao Xin Feng Quan Zhou Yanqin Wang Donglai Wu Rong Wang China Academy of Building Research Co. Ltd.Beijing 100013China Second Medical Center of PLA General Hospital Beijing 100853China Academy of Military Sciences Beijing 100071China Guangzhou Laboratory Guangzhou 510320China Chinese Academy of Agricultural Sciences Harbin Veterinary Research Institute Harbin 150069China Tianjin Key Laboratory of Indoor Air Environmental Quality Control School of Environmental Science and EngineeringTianjin UniversityTianjin 300072China

In this study,standards of high-biocontainment facilities(including laboratories and large-scale production facilities)formulated by China and other countries were analyzed and compared,and the technical points and requirements for Heating,Ventilating,and Air Conditioning(hvac)systems in different series of standards were discussed.The main focus was on expounding the design and verification of the containment area’s indoor parameters,ventilation system,filter design,integrity test,fan standby,airflow pattern,and system reliability.This study expects suggestions and opinions on the construction and development of biosafety facilities in China and the possible future revision of relevant national standards.

关键词： High-biocontainment facilities hvac systems Standards Technical requirements

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Predictive functional control based on fuzzy T-S model for hvac systems temperature control

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控制理论与应用（英文版） 2007年第1期5卷 94-98页

作者： Hongli LU Lei JIA Shulan KONG Zhaosheng ZHANG School of Control Science and Engineering Shandong University Jinan Shandong 250061 China School of Mathematics Science Qufu Normal University Qufu Shandong 273165 China

In heating, ventilating and air-conditioning （hvac） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control hvac systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in hvac systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.

关键词： T-S fuzzy model Predictive functional control Least squares method hvac systems

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Environmental parameters assessment of a new diffuser for air cooling/heating system:Measurements and numerical validation

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Building Simulation 2022年第6期15卷 1111-1132页

作者： Fabio Nardecchia Laura Pompei Fabio Bisegna Department of Astronautic Electric and Energy EngineeringSapienza UniversityVia Eudossiana 18RomeItaly

Air distribution of hvac systems is the most popular type used in the building sector,having a relevant impact on indoor air quality and occupant wellness.Many types of research developed optimal solutions for the hvac system’s design,focusing on specific components of the distribution system,on the airflow and geometry of ducts,on the size of ducts,on the shape and position of air diffusers.However,few works in literature proposed a globally experimental and simulation analysis of an air distribution system with a variable mass flaw rate.Along this line,the presented research investigates the potentialities of a new ceiling diffuser,installed in an exhibition room.This system provides a variable mass flow rate thanks to its configuration,providing adequate thermal comfort.A warm wall is chosen as the heating system.Several tests are carried out,six for cooling and two for heating with different volumetric air rates and supply air temperature of the diffusers.The combination of two methods,the measurement campaigns and the computational fluid dynamics(CFD)technique represent a suitable approach to examine the thermal indoor environment.In general,results show a strong capability of this diffuser to provide a uniform temperature and velocity field inside the room.Moreover,experimental and numerical data are significantly comparable with an average deviation of 1%for the velocity and lower 1%for the temperature,guaranteeing an optimal distribution of the understudy environmental parameters on the vertical and horizontal planes.

关键词： environmental parameters CFD field measurements INDULINE hvac systems thermal comfort

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Personalized Thermal Comfort Modeling based on Support Vector Classification

Personalized Thermal Comfort Modeling based on Support Vecto...

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第36届中国控制会议

作者： Muhammad Javed Ning Li Shaoyuan Li Department of Automation Shanghai Jiao Tong University

ISBN: (纸本)9781538629185

Heating Ventilation and Air Conditioning（hvac） systems are designed to provide a comfortable indoor thermal environment for the occupants.Conventionally,Predicted Mean Vote（PMV） model is used to represent thermal comfort which is only an average model and it cannot reflect the individual differences in thermal sensation.This paper proposes a personalized thermal comfort model based on a machine learning classification algorithm called Support Vector Classification（SVC）.We designed an Android application to collect feedback from occupants about their thermal sensation of the indoor environment.The feedback data-set is then used to train the personalized thermal comfort model.The model accuracy has been verified by comparison between the predicted thermal sensation based on the proposed model and actual thermal sensation feedback.Accuracy of the proposed thermal comfort model shows that the proposed model has a potential to save energy as well as capturing the individual differences in thermal comfort.

关键词： Personalized Thermal Comfort Modeling hvac systems Support Vector Classification Android Application

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