TY - GEN
T1 - Quantifying performance degradation of HVAC systems for proactive maintenance using a data-driven approach
AU - Dedemen, Gokmen
AU - Ergan, Semiha
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Data driven approaches
KW - HVAC
KW - Proactive maintenance
UR - http://www.scopus.com/inward/record.url?scp=85049076102&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049076102&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-91635-4_25
DO - 10.1007/978-3-319-91635-4_25
M3 - Conference contribution
AN - SCOPUS:85049076102
SN - 9783319916347
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 488
EP - 497
BT - Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings
A2 - Domer, Bernd
A2 - Smith, Ian F.
PB - Springer Verlag
T2 - 25th Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2018
Y2 - 10 June 2018 through 13 June 2018
ER -