Abstract
A much occurring problem in the Energy Management Systems of existing buildings and HVAC services is that the measurements are unreliable. In this article a methodology is described which can be used to determine the presence of errors in energy monitoring, caused by faulty measurements. These errors can be detected and subsequently diagnosed. Detection of monitoring errors is done based on occurring symptoms. Determination of these symptoms is done using the laws of conservation of energy, mass and pressure. The diagnosis is done by using a statistical method based on Bayesian theory in which the chance of an error occurring is determined based on ( combinations of) the symptoms. The method is built in a Bayesian Belief Network (BBN) software tool. The advantage of BBN is that it is consistent with the working methods of experts in installation technology.
Original language | English |
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Title of host publication | CLIMA 2016 |
Subtitle of host publication | Proceedings of the 12th REHVA World Congress |
Editors | Per Kvols Heiselberg |
Publisher | Aalborg University |
Pages | 1-10 |
Publication status | Published - 2016 |
Event | CLIMA 2016 - 12th REHVA World Congress - Aalborg, Denmark Duration: 22 May 2016 → 25 May 2016 http://vbn.aau.dk/en/activities/clima-2016--12th-rehva-world-congress(43019fd3-70a7-4c5c-9176-825addd5913f).html |
Conference
Conference | CLIMA 2016 - 12th REHVA World Congress |
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Country/Territory | Denmark |
City | Aalborg |
Period | 22/05/16 → 25/05/16 |
Internet address |
Keywords
- FDD
- Bayesian method
- BBN
- fault detection
- fault diagnosis
- systems theory
- Building Energy Management System
- Energy Monitoring System
- sensor faults
- model faults, HVAC equipment