A combined model-based and data-driven approach for monitoring smart buildings

16 pages•Published: January 6, 2018

Abstract

This 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.

Links:	https://easychair.org/publications/paper/cWm2
	https://doi.org/10.29007/g44l

BibTeX entry

@inproceedings{DX'17:combined_model_based_data,
  author    = {Hamed Khorasgani and Gautam Biswas},
  title     = {A combined model-based and data-driven approach for monitoring smart buildings},
  booktitle = {28th International Workshop on Principles of Diagnosis (DX'17)},
  editor    = {Marina Zanella and Ingo Pill and Alessandro Cimatti},
  series    = {Kalpa Publications in Computing},
  volume    = {4},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/cWm2},
  doi       = {10.29007/g44l},
  pages     = {21-36},
  year      = {2018}}

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