Quantifying performance degradation of HVAC systems for proactive maintenance using a data-driven approach

Gokmen Dedemen, Semiha Ergan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Poorly maintained and degraded Heating, Ventilating, Air Conditioning (HVAC) systems waste significant amount of energy. Current Facilities Management (FM) practice is mostly based on reactive and scheduled maintenance of HVAC systems instead of proactive maintenance, which aims at detecting anticipated failures before they occur, so that lower life cycle costs can be accomplished. Therefore, current FM practice needs approaches to detect anticipated failures, so that proactive measures can be taken. Building Automation Systems (BASs) in smart buildings provide historical data on HVAC operations, which can be leveraged for detecting performance degradation of HVAC systems. This study provides a data-driven methodology to quantify and visualize performance changes of HVAC systems over the years using historical BAS data. Our results on a case building demonstrated that there are statistically significant differences between the dataset over the years due to behavioral changes in the HVAC system when other factors (e.g., weather) are controlled. The contribution of this work is a computational approach to identify behavioral changes in HVAC equipment over time using custom selected algorithms for the HVAC domain.

Original languageEnglish (US)
Title of host publicationAdvanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings
PublisherSpringer-Verlag
Pages488-497
Number of pages10
ISBN (Print)9783319916347
DOIs
StatePublished - Jan 1 2018
Event25th Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2018 - Lausanne, Switzerland
Duration: Jun 10 2018Jun 13 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10863 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other25th Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2018
CountrySwitzerland
CityLausanne
Period6/10/186/13/18

Fingerprint

Data-driven
Conditioning
Air conditioning
Heating
Maintenance
Degradation
Automation
Life Cycle Cost
Intelligent buildings
Historical Data
Weather
Life cycle
Quantify
Methodology
Energy
Costs

Keywords

  • Data driven approaches
  • HVAC
  • Proactive maintenance

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Dedemen, G., & Ergan, S. (2018). Quantifying performance degradation of HVAC systems for proactive maintenance using a data-driven approach. In Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings (pp. 488-497). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10863 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-91635-4_25

Quantifying performance degradation of HVAC systems for proactive maintenance using a data-driven approach. / Dedemen, Gokmen; Ergan, Semiha.

Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings. Springer-Verlag, 2018. p. 488-497 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10863 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Dedemen, G & Ergan, S 2018, Quantifying performance degradation of HVAC systems for proactive maintenance using a data-driven approach. in Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10863 LNCS, Springer-Verlag, pp. 488-497, 25th Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2018, Lausanne, Switzerland, 6/10/18. https://doi.org/10.1007/978-3-319-91635-4_25
Dedemen G, Ergan S. Quantifying performance degradation of HVAC systems for proactive maintenance using a data-driven approach. In Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings. Springer-Verlag. 2018. p. 488-497. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-91635-4_25
Dedemen, Gokmen ; Ergan, Semiha. / Quantifying performance degradation of HVAC systems for proactive maintenance using a data-driven approach. Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings. Springer-Verlag, 2018. pp. 488-497 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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