Bayesian Belief Networks (BBN) and Expert Systems for supporting model based sensor fault detection analysis of smart building systems

J.D. Schagen, A. Taal, Laure Itard

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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    Abstract

    The Hague University in Delft uses an advanced climate control system. All sensors and actuators are monitored and deviations from the sensor data are reported daily. The building manager will have to combine the information from the sensor data in order to draw the right conclusions. In this paper, two possible solutions are described for analyzing the data by a computer program. The first solution is by means of a rule-based program, in which predetermined situations have been defined. The data from the sensors are fed into the program and the program checks whether it matches any of the situations. The second solution is to make use of a Bayesian Belief Network. This is a mathematical model that describes the symptoms and causes of a particular problem. With imported sensor data a computer program calculates the likelihood of particular causes of data symptoms.
    Original languageEnglish
    Title of host publicationCLIMA 2016
    Subtitle of host publicationProceedings of the 12th REHVA World Congress
    EditorsPer Kvols Heiselberg
    PublisherAalborg University
    Pages1-7
    Publication statusPublished - 2016
    EventCLIMA 2016 - 12th REHVA World Congress - Aalborg, Denmark
    Duration: 22 May 201625 May 2016
    http://vbn.aau.dk/en/activities/clima-2016--12th-rehva-world-congress(43019fd3-70a7-4c5c-9176-825addd5913f).html

    Conference

    ConferenceCLIMA 2016 - 12th REHVA World Congress
    CountryDenmark
    CityAalborg
    Period22/05/1625/05/16
    Internet address

    Keywords

    • Bayesian Belief Networks
    • Expert System
    • HVAC system
    • sensor fault detection

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  • Cite this

    Schagen, J. D., Taal, A., & Itard, L. (2016). Bayesian Belief Networks (BBN) and Expert Systems for supporting model based sensor fault detection analysis of smart building systems. In P. K. Heiselberg (Ed.), CLIMA 2016 : Proceedings of the 12th REHVA World Congress (pp. 1-7). Aalborg University.