This paper presents a building performance simulation-based investigation to better understand the energy and comfort performance benefits of early detection of common sensor and actuator faults. Five types of air-handling unit faults and four types of zone-level faults were implemented to the energy management system application of the building performance simulation tool EnergyPlus. During 50-year simulation periods, the faults were randomly permitted to affect 75 different components of an archetype medium-sized office building model. The sensitivity of the simulation results with respect to three variables was studied: fault recurrence period, fault repair period, and discomfort threshold for simulated complaints. The results indicate that the energy use intensity and the predicted percentage of dissatisfied exhibit a power–law relationship with time, in which most of the performance reductions occur in the first 10 years. If the work-orders are issued only upon occupant complaints, the faults were estimated to cause a 16–62% increase in the energy use intensity for heating, ventilation, and air-conditioning and a 11–38% increase in the predicted percentage of dissatisfied at the end of the 50-year simulation periods. The results indicate that if the faults can be detected within a month after their first appearance, almost all their detrimental effects on a building’s energy and comfort performance can be mitigated.
Practical application: The methodology and results presented in this article are of practical use for those who study on-going commissioning, fault detection and diagnostics, and energy management systems in buildings. The simulation-based parametric analysis approach can be used to estimate the range of energy and comfort savings expected through early detection of common sensor and actuator faults in commercial buildings. Insights gathered from such an analysis can be used in planning the frequency of retro-commissioning and investments for automated fault detection and diagnostics systems.
ArensEFederspielCCWangDet al.How ambient intelligence will improve habitability and energy efficiency in buildings. In: WeberWRabaeyJMAartsE (eds). Ambient intelligence, Berlin: Heidelberg: Springer Berlin Heidelberg, 2005, pp. 63–80.
2.
GunayHB. Improving energy efficiency in office buildings through adaptive control of the indoor climate, Ottawa: Carleton University, 2016.
3.
GunayBShenWYangC. Characterization of a building’s operation using automation data: a review and case study. Build Environ2017; 118: 196–210.
4.
PrivaraSVáňaZŽáčekováEet al.Building modeling: selection of the most appropriate model for predictive control. Energ Build2012; 55: 341–350.
5.
CorbinCDHenzeGPMay-OstendorpP. A model predictive control optimization environment for real-time commercial building application. J Build Perform Simulat2013; 6: 159–174.
6.
May-OstendorpPHenzeGPCorbinCDet al.Model-predictive control of mixed-mode buildings with rule extraction. Build Environ2011; 46: 428–437.
7.
PatanKKorbiczJ. Nonlinear model predictive control of a boiler unit: a fault tolerant control study. Int J ApplMath Comput Sci2012; 225: 738–743.
8.
YangZBecerik-GerberB. The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use. Energ Build2014; 78: 113–122.
9.
GunayHBO’BrienWBeausoleil-MorrisonI. Development of an occupancy learning algorithm for terminal heating and cooling units. Build Environ2015; 93: 71–85.
10.
GoyalSIngleyHABarooahP. Occupancy-based zone-climate control for energy-efficient buildings: complexity vs. performance. Appl Energ2013; 106: 209–221.
11.
Agarwal Y, Balaji B, Gupta R, et al. Occupancy-driven energy management for smart building automation. In: Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building, 2010, Zurich, Switzerland, 3–5 November 2010, pp.1–6. New York, NY: ACM.
12.
Pardasani A, Armstrong M, Newsham G, et al. Intelligent management of baseboard heaters to level peak demand. In: Electrical power and energy conference (EPEC), Ottawa, Ontario, Canada, 12–14 October 2016, pp.1–6. IEEE.
13.
Kjærgaard MB, Arendt K, Clausen A, et al. Demand response in commercial buildings with an assessable impact on occupant comfort. In: 2016 IEEE international conference on smart grid communications (SmartGridComm), Sydney, Australia, 6–9 November 2016, pp.447–452. IEEE.
14.
PalenskyPDietrichD. Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inform2011; 7: 381–388.
15.
Piette MA, Watson D, Motegi N, et al. Automated demand response strategies and commissioning commercial building controls. Berkeley: Lawrence Berkeley National Laboratory, 2006.
16.
GunayBShenW. Connected and distributed sensing in buildings: current state and future challenges. IEEE SMC Mag2017; 3: 27–34.
17.
GunayBShenWYangCet al.A preliminary study on text-mining operator logbooks to develop a fault-frequency model. ASHRAE Trans2017; 124: 1–14.
18.
WangDFederspielCArensE. Correlation between temperature satisfaction and unsolicited complaint rates in commercial buildings. Indoor Air2005; 15: 13–18.
19.
Standard 55-2013. Thermal environmental conditions for human occupancy. Atlanta: The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE).
20.
Federspiel C, Martin R and Yan H. Thermal comfort models and complaint frequencies. Berkeley: University of California, 2003.
21.
FederspielCC. Predicting the frequency and cost of hot and cold complaints in buildings. HVAC&R Res2000; 6: 289–305.
22.
Claridge D, Haberl J, Liu M, et al. Can you achieve 150% of predicted retrofit savings? Is it time for recommissioning? In: Proceedings of the 1994 ACEEE summer study, 1994, pp.5.73–75.88. Washington, DC: ACEEE.
23.
CostaAKeaneMMTorrensJIet al.Building operation and energy performance: monitoring, analysis and optimisation toolkit. Appl Energ2013; 101: 310–316.
24.
RostekKMorytkoŁJankowskaA. Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks. Energy2015; 89: 914–923.
25.
PadillaMChoinièreDCandanedoJA. A model-based strategy for self-correction of sensor faults in variable air volume air handling units. Sci Tech Built Environ2015; 21: 1018–1032.
26.
YuYWoradechjumroenDYuD. A review of fault detection and diagnosis methodologies on air-handling units. Energ Build2014; 82: 550–562.
27.
DuZFanBJinXet al.Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis. Build Environ2014; 73: 1–11.
28.
ZhaoYWangSXiaoF. A statistical fault detection and diagnosis method for centrifugal chillers based on exponentially weighted moving average control charts and support vector regression. Appl Therm Eng2013; 51: 560–572.
29.
KatipamulaSBrambleyMR. Review article: methods for fault detection, diagnostics, and prognostics for building systems – a review, Part II. HVAC&R Res2005; 11: 169–187.
30.
KatipamulaSBrambleyMR. Review article: methods for fault detection, diagnostics, and prognostics for building systems – a review, Part I. HVAC&R Res2005; 11: 3–25.
31.
40 A. Commissioning tools for improved energy performance. International Energy Agency Energy in Buildings and Communities Program Annex 40, 2004.
32.
34 A. Technical synthesis report: computer aided evaluation of HVAC system performance. International Energy Agency Energy in Buildings and Communities Program Annex 34, 2006.
33.
25 A. Building optimization and fault diagnosis source book. International Energy Agency Energy in Buildings and Communities Program Annex 25, 1996.
34.
WenJLiS. ASHRAE RP-1312– tools for evaluating fault detection and diagnostic methods for air-handling units, Atlanta: ASHRAE, 2012.
35.
LKNJAWRABet al.ASHRAE RP-1020 – demonstration of fault detection and diagnostic methods in a real building, Atlanta: ASHRAE, 2002.
36.
KimWKatipamulaS. A review of fault detection and diagnostics methods for building systems. Sci Tech Built Environ2017; 24: 3–21.
37.
Haves P and Khalsa S. Model-based performance monitoring: review of diagnostic methods and chiller case study. In: Summer study on energy efficiency in buildings, efficiency, and sustainability, California, USA, 20–25 August 2000. ACEEE.
38.
PietteMAKinneySKHavesP. Analysis of an information monitoring and diagnostic system to improve building operations. Energ Build2001; 33: 783–791.
39.
ScheinJBushbySTCastroNSet al.A rule-based fault detection method for air handling units. Energ Build2006; 38: 1485–1492.
40.
CABA. Intelligent buildings and big data, Ottowa: Continental Automated Buildings Association, 2017.
41.
CABA. Intelligent buildings and the impact of the internet of things, Ottowa: Continental Automated Buildings Association, 2017.
42.
Cheung H and Braun JE. Development of fault models for hybrid fault detection and diagnostics algorithm. Report NREL/SR-5500-65030 for the National Renewable Energy Laboratory, 2015. Colorado: NREL.
43.
LeeSHYikFWH. A study on the energy penalty of various air-side system faults in buildings. Energ Build2010; 42: 2–10.
44.
WangLGreenbergSFiegelJet al.Monitoring-based HVAC commissioning of an existing office building for energy efficiency. Appl Energ2013; 102: 1382–1390.
45.
OttoKEisenhowerBO’NeillZet al.Prioritizing building system energy failure modes using whole building energy simulation. IBPSA-USA J2012; 5: 545–553.
46.
ZhangRHongT. Modeling of HVAC operational faults in building performance simulation. Appl Energ2017; 202: 178–188.
47.
Deru M, Field K, Studer D, et al. US Department of Energy commercial reference building models of the national building stock, 2011. Colorado: NREL.
48.
GunayBO’BrienWBeausoleil-MorrisonIet al.Development and implementation of a thermostat learning algorithm. Sci Tech Built Environ2017; 24: 43–56.
Yang C, Shen W, Chen Q, et al. Toward failure mode and effect analysis for heating, ventilation and air-conditioning. In: CSCWD, Wellington, New Zealand, 26–28 April 2017, pp.408–413. New Zealand: IEEE.
ChoSY. The persistence of savings obtained from commissioning of existing buildings, Texas: Texas A&M University, 2002.
53.
Bourassa NJ, Piette MA and Motegi N. Evaluation of retrocommissioning persistence in large commercial buildings. In: 12th National Conference on Building Commissioning, Atlanta, GA, USA, 18–20 May 2004, https://cbs.lbl.gov/publications/evaluation-retrocommissioning (2004, accessed 11 March 2018).
54.
GoinsJMoezziM. Linking occupant complaints to building performance. Build Res Inform2013; 41: 361–372.
55.
Leaman A and Bordass W. Comfort and complexity: unmanageable bedfellows. In: Workplace comfort forum, London, UK, 22–23 March 1995, pp.22–23. London: RIBA.
56.
SchiavonSLeeKH. Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures. Build Environ2013; 59: 250–260.
57.
RijalHTuohyPNicolFet al.Development of an adaptive window-opening algorithm to predict the thermal comfort, energy use and overheating in buildings. J Build Perform Simulat2008; 1: 17–30.
58.
O’BrienWKapsisKAthienitisAK. Manually operated window shade patterns in office buildings: a critical review. Build Environ2013; 60: 319–338.
59.
HaldiFRobinsonD. On the behaviour and adaptation of office occupants. Build Environ2008; 43: 2163–2177.
60.
JakubiecJAReinhartCF. The ‘adaptive zone’ – a concept for assessing discomfort glare throughout daylit spaces. Lighting Res Tech2012; 44: 149–170.
61.
ASHRAE. ANSI/ASHRAE/IES Standard 90.1-2013. Energy standard for buildings except low-rise residential buildings. ASHRAE, 2013.
62.
FerrettiNMGallerMABushbySTet al.Evaluating the performance of diagnostic agent for building operation (DABO) and HVAC-Cx tools using the Virtual Cybernetic Building Testbed. Sci Tech Built Environ2015; 21: 1154–1164.
