Poster Abstract: Occupant-driven Diagnostic Bayesian Networks: Incorporating Subjective Feedback for Resilient Operation
Martín Mosteiro-Romero (TU Delft - Architecture and the Built Environment)
Nitant Upasani (Eindhoven University of Technology)
Laure Itard (TU Delft - Architecture and the Built Environment)
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Abstract
This paper presents a Diagnostic Bayesian Network (DBN) for whole-building fault detection and diagnosis (FDD) incorporating occupant feedback as potential symptoms of faulty operation and occupant behaviors as potential faults in building performance. The methodology is applied on a seven-floor office building in Delft, the Netherlands, and the DBN's fault isolation capabilities for three different levels of information are compared.
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