Behravan, Ali
[Author]
;
Obermaisser, Roman
[Contributor]
Diagnostic classifiers based on fuzzy Bayesian belief networks and deep neural networks for demand-controlled ventilation and heating systems ; Diagnoseklassifikatoren auf Basis von Fuzzy Bayesian Belief Networks und Deep Neural Networks für bedarfsgesteuerte Lüftungs- und Heizungsanlagen
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Media type:
Electronic Thesis;
E-Book;
Doctoral Thesis;
Text
Title:
Diagnostic classifiers based on fuzzy Bayesian belief networks and deep neural networks for demand-controlled ventilation and heating systems ; Diagnoseklassifikatoren auf Basis von Fuzzy Bayesian Belief Networks und Deep Neural Networks für bedarfsgesteuerte Lüftungs- und Heizungsanlagen
Contributor:
Behravan, Ali
[Author]
imprint:
Universität Siegen; Department Elektrotechnik - Informatik, 2021-01-01
Footnote:
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Description:
The building sector and its embedded control systems, especially the Heating, Ventilation, and Air-Conditioning (HVAC) systems, consume a considerable part of the global energy and produce gaseous emissions such as CO2. On the other hand, the air exchange based on natural ventilation is a cost-efficient method to improve indoor air quality, dilute indoor CO2concentration and odors, or remove pollutants or airborne virus particles (e.g., Covid-19) from the building zones. This air exchange during the cold seasons accounts for a heating load for the heating system that causes an increase in energy consumption. Therefore, optimization of HVAC systems to decrease harmful emissions considering potential energy saving is vital. Moreover, if the CO2 generated by human metabolism is not correctly controlled to some limits, it can degrade indoor air quality, reduce the occupants’ efficiency, lead to severe mental problems, or considerably impair the thinking ability. Thus, implementing a robust ventilation control system for the buildings particularly crowded office buildings is momentous. Demand-Controlled Ventilation (DCV) systems are promising solutions that control and optimize the ventilation rates based on thermal comfort and indoor air quality demands with a high potential in energy saving. Many researchers in the literature study DCV systems or adaptive thermal control separately while a comprehensive model containing both DCV and thermal control strategies is missing. Therefore, this thesis contains the combination of the DCV and heating systems with embedded sensors and actuators with the fault injection capabilities in a simulation framework to study such a complex system due to its numerous functions, inputs, and outputs for an in-depth assessment of the involved components’ functionality and effective parameters, especially in case of component failures. Indoor air quality and comfort parameters in an office building can be monitored and controlled in real-time for various architectures based on a high-level ...