Characterization of a building's operation using automation data: a review and case study

From National Research Council Canada

Download
  1. (PDF, 1 MB)
DOIResolve DOI: https://doi.org/10.1016/j.buildenv.2017.03.035
AuthorSearch for: 1; Search for: 1; Search for: 2
Name affiliation
  1. National Research Council of Canada. Construction
  2. National Research Council of Canada. Information and Communication Technologies
FormatText, Article
Journal titleBuilding and Environment
ISSN0360-1323
1873-684X
Volume118
Pages196–210
Subjectautomated on-going commissioning; fault detection and diagnostics; inverse modelling; greybox modelling; commercial buildings
Abstract
This paper presents a critical review of the automated on-going commissioning (AOGC) methods for air-handling units (AHU) and variable air volume terminal (VAV) units in commercial buildings. The common faults studied in the literature were identified. The diagnostic approaches taken and the characteristics of the fault-symptom datasets utilized were categorized. It was found that the diagnostics methods were vastly fragmented, and most of them employed pure-statistical approaches. Only a few studies attempted to assimilate the automation data within the underlying physical processes. In addition, a large fraction of the reviewed literature has been devoted to physical faults in AHUs. Only a few studies were conducted to diagnose faults-related with controls programming and faults at the zone level. Upon the literature survey findings, an inverse greybox modelling-based AOGC approach was put forward. Its strengths and weaknesses were demonstrated through a case study conducted using the archived building automation system (BAS) data of an office building in Ottawa, Canada. The results of this case study indicate that inverse greybox modelling-based AOGC is a promising method to diagnose both physical and controls programming related faults at AHUs and VAVs.
Publication date
PublisherElsevier
LanguageEnglish
Peer reviewedYes
NPARC number23002028
Export citationExport as RIS
Report a correctionReport a correction
Record identifier38721cdd-c257-4ffb-8353-5a78e71420ea
Record created2017-07-25
Record modified2020-06-04
Report a problem on this page
Date modified: