Download PDFOpen PDF in browserA combined model-based and data-driven approach for monitoring smart buildings16 pages•Published: January 6, 2018AbstractThis paper combines a residual-based diagnosis approach and an unsupervised anomaly detection method to develop a hybrid methodology for monitoring smart buildings for which complete models are not available. The proposed method combines data mining approach and model-based diagnosis to update a diagnosis reference model and improve the overall diagnostics performance. To estimate the likelihood of each potential fault in complex systems like smart buildings, the dependencies between components and, there- fore, the monitors should be considered. In this work, a tree augmented naive Bayesian learning algorithm (TAN) is used for the classification. We demonstrate and validate the proposed approach using a data-set from an outdoor air unit (OAU) system in the Lentz public health center in Nashville.Keyphrases: combined diagnoser, data driven diagnosis, model based diagnosis, residual analysis, tree augmented bayesian classifiers In: Marina Zanella, Ingo Pill and Alessandro Cimatti (editors). 28th International Workshop on Principles of Diagnosis (DX'17), vol 4, pages 21-36.
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