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Science and Technology for the Built Environment Volume 29, 2023 - Issue 10
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Research Articles

Empirical analysis of the prevalence of HVAC faults in commercial buildings

Eliot Crowe1 Lawrence Berkeley National Laboratory, Berkeley, CA, USACorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-8644-8348
ORCID Icon,
Yimin Chen1 Lawrence Berkeley National Laboratory, Berkeley, CA, USAORCID Iconhttps://orcid.org/0000-0003-3818-1655
ORCID Icon,
Hayden Reeve2 Pacific Northwest National Laboratory, Richland, WA, USAORCID Iconhttps://orcid.org/0000-0001-9884-1569
ORCID Icon,
David Yuill3 University of Nebraska–Lincoln, Lincoln, NE, USAORCID Iconhttps://orcid.org/0000-0001-6237-6781
ORCID Icon,
Amir Ebrahimifakhar4 Delos Labs, New York, NY, USAORCID Iconhttps://orcid.org/0000-0001-6853-7953
ORCID Icon,
Yuxuan Chen3 University of Nebraska–Lincoln, Lincoln, NE, USAORCID Iconhttps://orcid.org/0000-0002-9056-3821
ORCID Icon,
Lucas Troup2 Pacific Northwest National Laboratory, Richland, WA, USA
,
Amanda Smith5 Project Drawdown, San Francisco, CA, USAORCID Iconhttps://orcid.org/0000-0003-2990-2190
ORCID Icon &
Jessica Granderson1 Lawrence Berkeley National Laboratory, Berkeley, CA, USAORCID Iconhttps://orcid.org/0000-0002-4536-9560
ORCID Icon show all
Pages 1027-1038 | Received 23 Feb 2023, Accepted 21 Jun 2023, Published online: 18 Oct 2023

Figures & data

Fig. 1. Fault naming framework used in the study.

Fig. 1. Fault naming framework used in the study.

Fig. 2. Percent Time Faulted (PTF) calculation concept (“1” represents a day on which a fault was reported).

Fig. 2. Percent Time Faulted (PTF) calculation concept (“1” represents a day on which a fault was reported).

Fig. 3. Illustration of method to calculate percent of equipment operating in a faulted state.

Fig. 3. Illustration of method to calculate percent of equipment operating in a faulted state.

Table 1. Mean number of reported faults per month (AHU and ATU).

Fig. 4. Most commonly reported faults for AHUs, based on Pct_Affected value (chart shows faults 20% or higher).

Fig. 4. Most commonly reported faults for AHUs, based on Pct_Affected value (chart shows faults 20% or higher).

Fig. 5. Most commonly reported faults for ATUs, based on Pct_Affected value (chart shows faults 20% or higher).

Fig. 5. Most commonly reported faults for ATUs, based on Pct_Affected value (chart shows faults 20% or higher).

Fig. 6. Mean Percent Time Faulted, for AHUs experiencing a given fault at least once (MPTF_Affected). Chart shows faults with MPTF_Affected of 20% or greater, i.e., a given fault is reported on 20% or more of the days across which a piece of equipment was monitored.

Fig. 6. Mean Percent Time Faulted, for AHUs experiencing a given fault at least once (MPTF_Affected). Chart shows faults with MPTF_Affected of 20% or greater, i.e., a given fault is reported on 20% or more of the days across which a piece of equipment was monitored.

Fig. 7. Mean Percent Time Faulted, for ATUs experiencing a given fault at least once (MPTF_Affected). Excludes ATUs that never saw a given fault.

Fig. 7. Mean Percent Time Faulted, for ATUs experiencing a given fault at least once (MPTF_Affected). Excludes ATUs that never saw a given fault.

Fig. 8. Scatter chart of Pct_Affected vs. MPTF_Affected for AHU faults, highlighting the six faults that are both common (Pct_Affected ≥20%) and persistent (MPTF_Affected ≥20%).

Fig. 8. Scatter chart of Pct_Affected vs. MPTF_Affected for AHU faults, highlighting the six faults that are both common (Pct_Affected ≥20%) and persistent (MPTF_Affected ≥20%).

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