Advancing Building Fault Diagnosis Using the Concept of Contextual and Heterogeneous Test

Singh, Mahendra; Kiên, Nguyễn Trung; Najeh, Houda; Ploix, Stéphane; Caucheteux, Antoine

doi:10.3390/en12132510

Cited by 4 publications

(2 citation statements)

References 23 publications

(25 reference statements)

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“…Erroneous all-context models might lead to invalid diagnoses [53]. According to [54], diagnosis reasoning must differ in different scenarios, e.g., fault detection and diagnosis approaches should be different for normal working days and a vacation period.…”

Section: New Challenges For Fault-tolerant Control In Building Systemsmentioning

confidence: 99%

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

et al. 2021

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Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.

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Section: New Challenges For Fault-tolerant Control In Building Systemsmentioning

confidence: 99%

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

et al. 2021

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“…Transient system (TRNsys Simulation Tool, simulation software) with TRNsys Simulation Studio (graphical front-end) and interface TRNBuild have been used to modulate the building and to generate Air Conditioning systems data [39]. Other studies have used TRNSYS software to generate the data [40] and to simulate the buildings faults [41]. TRNBuild interface allows adding the non-geometrical properties such as door and window properties, layer and wall material properties, thermal conductivity and different gains etc.…”

Section: Description Of Air Conditioning Systemsmentioning

confidence: 99%

Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches

et al. 2020

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Fault Detection and Isolation (FDI) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDI framework is required to reduce the energy needs for buildings and improving indoor environment quality. The main goal of this paper is to merge the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers to improve the efficiency of the detection and isolation of Air Conditioning (AC) systems. First, the multivariate statistical features extraction and selection is achieved using the PCA method. Then, the multiscale representation is applied to separate feature from noise and approximately decorrelate autocorrelation between available measurements. Third, the extracted and selected features are introduced to several machine learning classifiers for fault classification purposes. The effectiveness and higher classification accuracy of the developed Multiscale PCA (MSPCA)-based ML technique is demonstrated using two examples: synthetic data and simulated data extracted from Air Conditioning systems.

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Faults and Failures in Smart Buildings: A New Tool for Diagnosis

Najeh

Singh

Ploix

2012

Towards Energy Smart Homes

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Advancing Building Fault Diagnosis Using the Concept of Contextual and Heterogeneous Test

Cited by 4 publications

References 23 publications

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches

Faults and Failures in Smart Buildings: A New Tool for Diagnosis

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