Abstract and Figures

This study evaluates typical faults occurring in demand-controlled ventilation (DCV) system and the impact on three output results: energy use, thermal comfort, and indoor air quality. The methodologies used in this study were qualitative interviews of selected Norwegian Heating Ventilation Air Condition (HVAC) system experts and numerical modeling using the building performance simulation tool IDA ICE. The faults deduced from the qualitative interviews were modeled as the fault's different consequences to account for a large variety of faults. With a Norwegian school classroom as a case study, a local approach applying a one-at-a-time (OAT) simulation was used to perform an analysis of the extreme fault conditions that can occur. The results from the fault modeling demonstrated that greater attention is needed to avoid faults in the HVAC systems due to its impact on the indoor environment quality and energy efficiency of buildings.
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* Corresponding author: kamilla.andersen@sintef.no
Impact of Typical Faults Occurring in Demand-
Ventilation on Energy and Indoor Environment in a Nordic C
Kamilla Heimar Andersen1,2,*, Sverre B. Holøs1, Aileen Yang3, Kari Thunshelle1, Øystein Fjellheim1, and Rasmus Lund
1SINTEF Community, Architectural Engineering, 0373 Oslo, Norway
2Aalborg University, Civil Engineering Department, 9220 Aalborg East, Denmark
3 OsloMet – Oslo Metropolitan University, 0130 Oslo, Norway
This study evaluates typical faults occurring in demand-controlled ventilation (DCV) system
and the impact
on three output results: energy use, thermal comfort, and indoor air quality. The methodologies used in this
study were qualitative interviews of selected Norwegian Heating Ventilation Air Condition (HVAC) system
experts and numerical modeling using the building performance simulation tool IDA ICE. The faults deduced
from the qualitative interviews were modeled as
the fault's different consequences to account for a large
variety of faults. With a Norwegian school classroom as a case study, a local approach applying a one-at-a-
time (OAT) simulation was used to perform an analysis of the extreme fault conditions that can occur. The
results from the fault modeling demonstrated that greater attention is needed to avoid faults in the HVAC
systems due to its impact on the indoor environment quality and energy efficiency of buildings.
1 Introduction
In order to tackle the urgent environmental issues of our
modern society and improve the overall life quality of the
population, the building sector appears as a clear key
. Indeed, the building sectors account for more
than 40% of the total energy needs and a third of the CO2
emissions [2]. In addition, modern humans spend 80-90
% of their time inside an enclosed space [3]. The quality
of their indoor environment has thus a major impact on
their well-being, health, and productivity [4].
Heating Ventilation and Air Conditioning (HVAC)
systems provide occupants with a comfortable indoor
environment, which includes, among others, fresh air. The
latter is a key parameter for a healthy indoor space. In
addition, a traditional HVAC system normally consumes
up to 30 % of the total energy use in a building [5].
In recent years, detecting and preventing faults from
occurring in HVAC systems is raising more and more
attention [6–8] as they have been found to have a
preponderant impact on the building energy needs and the
indoor environment quality [9–11]. However, further
efforts are needed to develop and implement efficient
strategies for the design, commissioning, maintenance,
and repair of HVAC systems, and especially ventilation
The popularity of DCV has strongly increased due to
system flexibility and its potential for energy savings [12–
15]. Nonetheless, there is a lack of studies concerning the
typical faults, errors, or malfunctions occurring in such
systems in Nordic countries.
The aim of this study is to evaluate the energy use,
thermal comfort, and indoor air quality of a Norwegian
school equipped with demand-controlled ventilation
(DCV) when typical faults occur.
The initial phase of the current investigation consists
of interviewing different professionals in the field of
HVAC systems in order to identify typical faults. Based
on the analysis of those qualitative interviews, several
typical faults are
identified. The impact of the identified
faults on the energy use and indoor environment is
examined by performing a sensitivity analysis using the
building simulation tool IDA ICE.
2 Study case
The study case is the Fernanda Nissen (FN) elementary
school located in the center of Oslo, Norway. The school
was completed in 2016, and fully operational in 2017. It
was built in accordance with the Norwegian passive house
standard NS 3701:2012. One can see in Figure 1 an
illustration of the Fernanda Nissen elementary school in
south-east orientation.
The school has balanced mechanical ventilation. The
ventilation is demand-controlled by DCV-dampers,
where the airflow rate is modulated between a minimum
(Vmin) and maximum (Vmax ) value. The DCV-damper
control is performed by adjusting the position of the
damper according to a demand airflow rate that is
calculated based on the indoor environmental quality in
each room (indoor temperature and CO
2 concentration
measurement). A general description of a DCV-damper
can be seen in Figure 2. The measuring cross and the
manometer measure the actual airflow rate in the duct and
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© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0
send this feedback information to the airflow controller.
The airflow controller then sends a control signal (0-10 V)
to the actuator which adjusts the position of the damper,
and thus the airflow supplied to the classroom to match
the setpoint (demand airflow rate) [16].
Fig. 1. Fernanda Nissen elementary school. Image sources:
and https://www.planforum.no/.
Fig. 2. A general schematic illustration of a DCV-damper [15].
Other ways of controlling DVC-dampers are further
described here [15].
3 Methodologies
3.1 Qualitative interviews
To investigate which typical faults can occur in DCV-
systems in schools,
offices, and other types of large
buildings, qualitative interviews were the starting point in
this study. In total, 11 different HVAC system
professionals were interviewed; six representatives from
consulting, two working as central facility managers, and
three contractors. The interview objects criteria were the
following: Minimum ten years of work experience within
the building sector or ventilation industry in Norway as
either a researcher, civil engineer, consultant, contractor,
as an operation facili
ty manager, or as an electrical
3.2 Fault modeling
After the analysis of the qualitative interviews, four faults
were chosen to fault-model based on faults and symptoms
of possible consequences provided from the interview
objects shown in Table 3.
Our fault modeling is based on the many suitable
methodologies suggested by Li et al. [17]. In short, this
fault modeling consists of changing the input building
parameters of the HVAC system to represent faults
suggested as one of the methods by Haves [18].
3.2.1 Numerical simulations in IDA ICE
The numerical model made in IDA ICE was based on a
single classroom from the school. Five surfaces were
treated as internal rooms. One wall was facing outdoors
in north-east orientation. One year was simulated (365
days) with the weather file: Oslo, Fornebu 014880
(IWEC) from EnergyPlus. The geometrical model from
IDA ICE can be seen in Figure 3.
Fig. 3. Geometrical model in IDA ICE.
The DCV implemented in the IDA ICE model uses
temperature and CO2 concentration sensors with a linear
control between Vmin and Vmax (PID-controller). The Air
Handling Unit (AHU) is
simulated with a
pressure difference in both the supply and exhaust ducts.
General inputs in the numerical simulations in IDA ICE
are described in Table 2.
Table 1. General inputs in IDA ICE simulations which are
representative for all IDA ICE models.
Parameter Value
Classroom area 60 m2 (6 x 10 m)
Ceiling height 2.8 m
31 occupants, 1 met
(From the school design)
emission per person
IDA ICE default
360 W (NS 3031:2014)
600 W (NS 3031:2014)
Electric radiator
(Typical heating system
in Norway)
Yes, 2 kW
(Thermostat setpoint 19 °
Solar shading
External blinds
(Activated at 75 W/m
Occupancy schedule
Monday – Friday
08:30 – 11:00
12:00 – 15:00
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Ventilation operation time
(No weekends, national
holidays or vacation
Ventilation airflow
150 m3/h
Vmax 1060 m3/h
(Norwegian building
SFP factor
1,5 kW/(m3/s)
(Treated as
constant, BPS
Heat exchanger efficiency
80 %
Total window area
7 m2
(WWR 42 %)
value external wall
0.11 W/m2 K
(NS 3701:2012)
U-value window
0.8 W/m2 K
(NS 3701:2012)
-value window
at 50 Pa
0.6 h-1
Leak area
External wall: 0.00606 m2
Door: 0.02 m2
(convergence achieved)
0.0833 h
5 minutes interval
Outdoor CO
(Oslo based number)
450 ppm
3.2.2 Local approach
Table 1 shows the chosen parameters to fault-model: (1)
maximum supply airflow, (2) maximum exhaust airflow,
(3) supply air temperature, and (4) CO2 concentration
setpoint. These parameters are based on the consequences
shown in Table 3. The offset values were selected based
on the interviews. A local approach, one-at-a-time (OAT)
simulation was performed to investigate
the consequences
of the offset values.
Firstly, the fault-free (reference) model was created
and simulated. Secondly, eight models were changed
OAT based on their offset value. To make this process
efficient and automatic, internal programming (macro) in
IDA ICE was used for the simulation process. The macro
consisted of a setup of sequential pre-defined parametric
in each of the eight
faulty models (described in
Table 1) in IDA ICE. Lastly, the results consisted of
calculating the differences between the reference model
and the faulty models of the output results: energy use,
thermal comfort, and indoor air quality. All faults were
simulated a whole year (365 days) with a representative
weather file.
Table 2. Local approach varying OAT faults with offset values
deduced from the interview objects
and based on Norwegian
Standards and guidelines.
Fault number
Maximu m supply
airflow (l/s m
Maximu m exhaust
airflow (l/s m
Supply air
temperature (°C)
Temp. curve
setpoint (ppm)
Reference: A well operating and functioning HVAC
system is simulated with the reference values shown in
Table 1. The supply air temperature is controlled with an
outdoor temperature compensation curve. The HVAC
system is designed to supply fresh air at 17 °C when the
outdoor temperature is 20 °C and above, and supply with
21 °C with outdoor temperatures lower than 10 °C. The
CO2 concentration setpoint was set to 800 ppm, and the
airflow rate is balanced with 4.9 l/s m2.
Fault 1 & 2 Maximum supply- and exhaust airflow
rate: The maximum supply- and exhaust airflow (Vmax)
was varied 30 % positive and negative of the reference
value. This to simulate situations with over- and under
pressure, in addition to less or more air supplied to the
classroom. As shown in Table 2, the minimum supply-
and exhaust airflow rate (Vmin) was kept constant at the
designed value. When unbalance is simulated, IDA ICE
will compensate
by either increase the infiltration or
exfiltration in the classroom. Therefore, leak areas have
been defined in the model.
Fault 3 Supply air temperature: The supply air
temperature is normally controlled by a compensated
outdoor curve during the cooling season if cooling is
installed. During the heating season, a constant supply air
temperature of 21 °C is often implemented since cooling
is rarely needed during the heating season in Nordic
countries. However, for
this fault
, constant ventilation
cooling or heating was implemented at either 17 °C or 25
°C (low and high fault).
Fault 4 CO2 concentration setpoint: The CO2
concentration setpoint is normally set to 800 ppm in
classrooms in Norway (can differ, usually depends on the
municipality). However, varying this setpoint, the CO2
can exceed the chosen setpoint, supplying
lower- or higher airflow.
4 Results and discussion
4.1 Qualitative interview results
Often, symptoms and faults are coinciding, as one
symptom may be associated with a handful of faults, or
one fault may have many different symptoms. In our
study, we do not distinguish between faults and symptoms
as both causes and consequences are analyzed. The top 10
faults from the qualitative interviews are described in
Table 3, ranked based on their occurrence mentioned by
the interview objects. The
causes and the consequences of
these faults are also shown in Table 3. The top five faults
from the qualitative interviews were (1) Ventilation
unbalance, (2) Incorrect or unsuitable placement of CO2
concentration and/or temperature sensor, (3) Noticeable
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noise, (4) No access to DCV-damper and (5) Lower or
higher airflow than designed supplied to a room.
Adopting the fault definitions from Annex 25 [5],
installation faults can be defined as, for example
ventilation installation with wrong control logic or
incorrect implementation. Generally, the majority of the
faults found in this study are due to improper installation
based on their possible causes. Installation faults can lead
to both gradual or sudden faults, where the HVAC system
would, in the worst-case scenario, require downtime to fix
components or parts of the HVAC system. However,
many installation faults may be prevented if protocols,
commissioning, or load-tests were performed correctly.
Using qualitative interviews to investigate what
typical faults can occur in the HVAC system has shown
to be a reliable methodology and has also been used in
other similar studies on fault modeling [19]. For example,
Qin et al. [10] asked ten professionals to assess the top 10
faults occurring in
mechanical ventilation
systems, of
which poor IAQ, deviation in room temperature, and the
difference in actual air volume flow were some of the
faults mentioned. Literature reviews are also a way to
discover typical faults and have been applied to several
studies [9] [20-22]. However, as there are many different
possibilities to control the DCV-dampers, it was exigent
to figure out what type of damper control (CO2
concentration, temperature, or combined) and ventilation
principle the ventilation system
utilized in the
evaluated studies. Nevertheless, both methodologies seem
to agree with our study, despite geographical differences
Fig. 4. Reference and eight faulty models presented with the annual energy use. Lights and equipment are not shown in the figure. The
annual energy use (including lights and equipment) is presented above each histogram. Fault 1: Maximum supply airflow, Fault 2:
Maximum exhaust airflow, Fault 3: Supply air temperature, Fault 4: CO
concentration. Electricity is not included in the graph as they
are constant values.
4.2 Energy use
The energy use (kWh/m2 year) investigated in this study
consists of fans and pumps, ventilation heating and
cooling, and space heating (electric radiator).
Annual energy use for lights and equipment was
estimated to 7 and 11.6 kWh/m2 year, respectively. These
parameters are kept constant and not further investigated
in this study. However, they are included in Figure 4.
Figure 4 illustrates the annual energy use divided into the
mentioned categories above, each histogram describes the
representative fault, and the annual energy use for each
category is presented above the histograms. The faults
providing the highest energy use are the following: (1)
Fault 1 high supply airflow, (2) Fault 2 low exhaust
airflow, and (3) Fault 3 high supply air temperature.
Fault 1 high (supply airflow) and Fault 2 low (exhaust
airflow) are in general simulated with overpressure, either
by supply-
or exhaust airflow.
These two faults increased
the energy use by 45 and 35 kWh/m2 year, (77 % and 60
%) respectively. This is because of the need for higher
ventilation heating due to increased exfiltration when
underpressure (Fault 1 low). Exfiltration leads to
increased heating or cooling demand, as a smaller
proportion of the airflow passes the heat recovery unit.
Also, during cold outdoor conditions, exfiltration
increases interstitial condensation risk. Thus, infiltration
is considered less problematic than exfiltration in cold or
cool climates.
High Low High Low High Low High
Fault 1 Fault 2 Fault 3 Fault 4
Difference -14% 77% 60% 5% -20% 34% 9% -10%
Space heating 1
1 1 2,4 0,7 0,8 1 0,9
Ventilation cooling 0,6 0,4 0,8 0,6 0,6 0,7 0 0,7 0,4
Ventilation heating 20,2 12,9 62,1 56,8 19,3
40,6 23,2 23,2
Fans and pumps 16,9 14,7 19,2 14,7 19,2 17 16,9 18,7 8,4
Annual energy use [kWh/m2
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Causes of faults and symptoms
- DCV-damper (either supply or exhaust) are mounted after
rehabilitation, no balanced ventilation or commissioning is
- Rooms with large deviations increased the pressure
- Wear and tear of the system
- Not sufficient or satisfactory commissioning (commissioned
with noticeable over- or under pressure)
- Load testing of the ventilation system improper or neglected
- Complex ventilation system
- Cracks or punctures in duct system (airtightness test not
Overpressure or under pressure have
occurred, ventilation airflows not
balanced), fan needs to work at a higher
– increasing the energy
consumption, lower or higher supply of
air which can make the occupants feel
, too warm and will decrease
- No calibration of the sensors in DCV-damper and the room
- Defective component or controller failure
- Improper installation
- Room structure not optimal for sensor placement
- Wrong component connection (no insulation/airtightness in
the cables so CO2 concentration sensor measures outdoor
Deviating supply air temperature and
supply airflow, higher CO
concentration, unsatisfied occupants,
draft may also occur if the combined
sensor shows higher temperature and
2 concentration than actual room
- Sound silencer/insulation not mounted with DCV-damper
(forgotten or neglected)
- Wear and tear of fan bearings
- Wrong placement of DCV-damper which provides incorrect
actuator point
Noise will be noticeable and
bothersome, unsatisfied occupants
- Low ceiling, DCV-damper does not fit properly
Design of DCV-damper
No cleaning hatch for removing dust and dirt from the
measuring cross.
- The ceiling is hard to remove/require demolition
Deviating supply- and exhaust airflow if
measuring cross is dusted, higher CO2
concentration due to dust and dirt on
measuring cross, unsatisfied occupants
due to the aforementioned reasons
- Electrical error or component error which makes the fire
valve close
- Frozen DCV-damper sensor
- Low fan speed
- Clogged, damaged or dirty coils
- Wrong choice of duct dimensions
Deviating supply
and exhaust airflow
from designed value,
higher CO2
concentration, unsatisfied occupants
- Wrongly designed airflow rate
- Non-strategically placement of room sensors contributing to
the wrong reading to damper or not connected to BMS at all
- Sensors have not been calibrated providing the wrong
- DCV-dampers is placed to close after bend which provides a
wrongly measured airflow rate
- Not optimal design of air intake (placed in the sun or
exposed to wind)
- No ventilation cooling is installed
- Broken heating- or cooling coil
- Components wrongly connected during commissioning or
- Higher occupancy load than designed
- Malfunction/fouling in the control valve of the heating and
cooling coil
- Wrong duct size which provides low-pressure differences
Deviating supply air temperature,
unsatisfied occupants increased energy
use because of increased ventilation
cooling or heating, deviating supply-
and exhaust airflow from the designed
- Not designed Vmin and Vmax (AHU operates as a constant
volume ventilation strategy)
- Lights are left on 24/7 (light sensor or schedule might not be
- Cooling and heating coils operate on/off from wrong
installation or wear and tear
- Abnormal user-behavior
- The heating system in the room is set to max (heating 24/7)
- Windows are frequently opened
Additional energy cost may increase, the
building may not reach energy goal if
part of an energy/sustainability scheme
No cleaning or change of filters
No access to the DCV-damper
Deviating supply- and exhaust airflow,
air feels heavy due to lower supply of
- No ventilation documentation provided or missing/non-
existing FDV
Improper installation
PID coefficients in DCV-damper not calibrated
damper pressure control frozen, or poor/wrong system
operating setpoints
Deviating supply air temperature and
supply airflow, unbalance, fouling
components in the HVAC system,
increased energy use
Not optimal BMS
Wrong choice of BMS for building operation
Deviating supply air temperature and
supply airflow, unbalance, fou
HVAC system
Table 3. Results from the qualitative interviews. Faults and symptoms, causes and consequences presented in each row.
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Fault 3 low, lower supply air temperature of 17 °C,
obviously demonstrates that less energy is needed to heat
lower supply airflow, and that the energy demand for
supply fan is also smaller at low-temperature airflow.
However, it would be expected that space heating
increases when supplying with low supply air
temperatures. This is due to the electric radiator setpoint,
which is set to 19 °C and is not exceeded even
when this
fault occurs.
The differences between the reference model and
Fault 3 low are low in general. As the supply temperature
curve also provides the classroom with 17 °C when the
outdoor temperature exceeds 20 °C, the differences is due
to this.
Clearly, Fault 3 high (supply air temperature) will
increase ventilation heating since the supply air
temperature is 4 °C higher than the reference.
Although most of the modeled faults increased the
annual energy use, two of the faults resulted in decreased
energy use. These are Fault 1 low (supply airflow) and
Fault 3 low (supply air temperature), which decreased the
annual energy use by 2 and 6 kWh/m2, respectively.
Some investigated faults did not have a significant
impact (less than 10 %) on energy use. These faults were
Fault 2 (exhaust airflow) high, and Fault 4 low and high
(CO2 concentration setpoint).
4.3 Thermal comfort
The results from the fault modeling on thermal comfort
measured in operative temperature can be found in Figure
5. The operative temperature was evaluated after the
Norwegian building regulations recommendations
The threshold values are hours < 19 °C and > 26 °C. In
addition, the range between 19 and 26 °C were divided
into categories ranging from IV+ and IV- and are
described in the legend in Figure 5. The impact of the
offset values on the operative temperature is not of
especially important since the operative temperature
intervals are always above 19 °C and below 26 °C.
Clearly, Fault 3 high increased the time above 24.5 and 26
°C with 10 % due to the higher constant supply air
temperature of 25 °C.
4.4 Indoor air quality
The indoor air quality, measured
as CO2 concentration, is
evaluated after the Norwegian labor inspection, report
444, which recommends keeping the CO2 concentration
level below 1000 ppm [23]. The threshold category values
are described in the legend in Figure 6.
As seen in Figure 6, 60% of the occupied hours in the
classroom were below the threshold value of Category I,
and 40% of the occupied hours were below the threshold
value in Category II. Nevertheless, Fault 4 high achieved
above Category III, which is far above the
Norwegian Labor Inspection recommendations.
Obviously, increasing the CO2 concentration setpoint will
result in a lower airflow supplied to the classroom.
4.5 Strengths and limitations
To the best of our knowledge, this study is one of the few
studies which has assessed fault modeling in a DCV-
system in a Nordic climate. Our fault modeling approach
is well suited, as shown by the evaluated literature. We
chose to select faults based on the interview objects, as
they possess expert and hands-on knowledge about
HVAC systems and can associate typical faults for
various ventilation systems in Norway. Thus, the selected
faults can be considered more relevant than from a
literature review. A wider span of interview objects could
provide a broader understanding of the causes of faults
and symptoms. However, measures were taken to achieve
quality results, such as the interview criteria. A single
classroom with one surface towards the outside and the
remaining surfaces were treated with no heat exchange
simulated. In a real building, some rooms have
larger external surfaces than others, adjoining rooms may
0% 20% 40% 60% 80%
IV- (< 19 °C)
III- (> 19 ≤ 20
II- (> 20 ≤ 21 °C)
I (> 21 ≤ 23 °C)
II+ (> 23 ≤ 24.5 °C)
III+ (> 24.5 ≤ 26 °C)
IV+ (> 26 °C)
F1 low,
supply airflow
F2 low,
exhasut air flow
F3 low,
supply air temperature
F4 low,
CO2- concentration
F1 high,
supply airflow
F2 high,
exhaust air flow
F3 high,
supply air temperature
F4 high,
Fig. 5. Distribution of the percentage of occupied hours
within each thermal comfort (operative temperature)
category for reference and the eight faulty models.
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have different usage and temperatures, and the
specific fan power will depend on the total ventilation
rates in the AHU. Also, internal walls in a building have
leak areas, such as cracks under doors, through internal
wall constructions and openings. Only one surface and a
door were designed with a leak area in IDA ICE, except
the external wall. In addition, since the local fault
modeling approach is based on a numerical approach,
some deviation from real life may occur.
The chosen extreme values in the local OAT approach
are an uncertain factor and are based on information
received from the interview objects.
The presented causes of faults and symptoms may
represent a large number of other faults not investigated
or stated in this study and thus affect that installation
faults are the majority of fault occurrence.
Furthermore, this study only investigates the impact of the
fault with the same occupancy load, lights, equipment,
and schedule every day (deterministic). In reality, the
actual occupancy load and schedule may frequently vary
both during the day and during the week, as classrooms
are used differently. For example, the lower grades rarely
have a fixed time schedule. Thus, the consequences in a
real situation will deviate from those simulated with these
5 Conclusion
This study aimed to investigate how typical faults in a
DCV-system could influence energy use, thermal
comfort, and indoor air quality in a classroom located in a
Nordic country. The faults with the highest impact
increased energy use by 77 % and 60 %, respectively.
Furthermore, the faults also influenced both thermal
comfort and indoor air quality.
Our findings demonstrate that faults in DCV-systems
can have considerable consequences for energy use and
indoor environment. To design, build, and operate healthy
and energy-efficient buildings using DCV-system, further
efforts are recommended to identify where, when, why,
and how often such faults occur.
As a continuation of this study, statistical analyses on
fault probability and occurrence would allow for more
investigations regarding fault impact on various output
parameters. In addition, Monte Carlo simulations may be
performed to analyze how higher-order faults interact
with each other.
This paper is based on the master thesis by Kamilla Heimar
Andersen and was a part of the BEST VENT project. BEST
VENT is funded by the Research Council of Norway EnergiX
program under Grant 255375/E20 together with the industry
partners: Undervisningsbygg Oslo KF, GK Inneklima AS, DNB
Næringseiendom AS, Erichsen & Horgen AS, Hjellnes Consult
AS, Multiconsult AS, Interfil AS, Camfil Norge AS, Swegon
, Belimo Automasjon Norge AS, NEAS AS, and Norsk VVS
Energi- og Miljøteknisk Forenings Stiftelse for forskning.
We would like to thank the 12 interview participants who
contributed with the content of this study, hence the faults and
symptoms, causes, and consequences of faults occurrence in
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0% 20% 40% 60% 80%
III (> 1000 ppm)
II (> 800 ≤ 1000 ppm)
I (< 800 ppm)
F1 low,
supply airflow
F2 low,
exhasut air flow
F3 low,
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F4 low,
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F1 high,
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F2 high,
exhaust air flow
F3 high,
supply air
F4 high,
Fig. 6 Distribution of the percentage of occupied hours
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... As heating, ventilation, and air conditioning (HVAC) units are measured to account for up to 50% of the total energy use in the buildings [5], this system is a foremost lead. Poor design, installation mistakes, and faults that arise due to equipment wear can increase energy use significantly and degrade the indoor environment [6], especially when faults go undetected for several years. Actually, in the United States of America, typical faults in buildings are estimated to account for 103 to 500 TWh of additional yearly energy use [7]. ...
... Andersen et al. investigated typical faults occurring in demand-controlled ventilation. Here, they modeled the faults' impact on energy use and indoor environmental quality (IEQ) in a Nordic climate with the building performance simulation tool IDA ICE [6]. ...
This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository.
Full-text available
Buildings consumed about 40% of primary energy and 70% of the electricity in the U.S. It is well known that most buildings lose a portion of their desired and designed energy efficiency in the years after they are commissioned or recommissioned. Majority of the Heating, Ventilation, and Air-Conditioning (HVAC) systems have multiple faults residing in the systems causing either energy, thermal comfort, or indoor air quality penalties. There are hundreds of fault detection and diagnostics (FDD) algorithms available, but there is lacking a common framework to assess and validate those FDD algorithms. Fault modeling is one of the key components of such a framework. In general, fault modeling has two purposes: testing and assessment of FDD algorithms, and fault impacts analysis in terms of building energy consumption and occupants’ thermal comfort. It is expected that fault ranking from the fault impact analysis can facilitate building facility managers to make decisions. This paper provides a detailed review of current state-of-the-art for the fault modeling of HVAC systems in buildings, including fault model, fault occurrence probability, and fault simulation platform. Fault simulations considering fault occurrence probability can generate realistic faulty data across a variety of faulty operating conditions, and facilitate testing and assessment of different FDD algorithms. They can also help the fault impact study. Three research gaps are identified through this critical literature review: (1) The number of available fault models of HVAC systems is still limited. A fault model library could be developed to cover all common HVAC faults for both traditional and non-traditional HVAC systems. (2) It is imperative to include the fault occurrence probability in fault simulations for a realistic fault impacts analysis such as fault ranking. (3) Fault simulation platforms need further improvements to better facilitate the fault impact analysis.
Full-text available
DCV stands for Demand-Controlled Ventilation. That is to say, ventilation systems that automatically regulate the ventilation rate according to a demand measured at room-level. DCV systems must therefore have a sensor in the room, which gives a measure of the indoor air quality, and this signal is used to control the ventilation rate to achieve the desired indoor air quality. There are large differences between different DCV systems, both in terms of functionality and cost. There are also significant differences in performance between DCV systems and simpler systems which, for example, vary the airflow rate with preset air damper positions, or which use a single sensor for several rooms. In order to verify that a DCV system fulfills the expectations in terms of indoor climate and energy use, one must specify measureable objectives of performance. Therefore, we recommend setting specific performance requirements for DCV. These are given in Chapter 2. It must be possible to control the specified requirements. The most important control points are presented on the figure below. All air handling units (AHU) should go through a functional check as part of commissioning. We recommend an automated load test with minimum and maximum supplied airflow rates to all the rooms, for maximal and reduced AHU airflow rate. If it is not possible to perform an automated load test, because of the chosen components and/or programming, we recommend checking all the rooms by measuring the ventilation rate for maximum and minimum fan speed, for maximum and reduced system load. This manual functional check should be documented with a completed VAV-control form. Moreover, we recommend setting requirements to the following points: 1. Specific Fan Power (SFP) for maximal and reduced airflow rate 2. changes in airflow rate at room level should result in the same change in the total airflow rate through the AHU 3. documentation in the form of a functional description and a DCV system diagram (both electrical and duct system) 4. balancing and control of the airflow rates (completed VAV-control form) 5. accuracy, calibration specifications, and lifespan for the chosen sensors and DCV dampers 6. SFP shall be measured such that power losses in Variable Speed Drives are included, preferably using a suitable 3-phase energy analyzer, or by direct reading on the AHU In addition, pressure-controlled systems shall be balanced in order to: • verify that the location of the pressure sensor is suitable • set the appropriate pressure set point • adjust any fixed balancing dampers in relation to the motorized control dampers Deviations during commissioning are normal and should be expected. Therefore, it is important to either forecast time to improve the system, or to create a model for economic compensation to take into account the deviations from the requirements which affect the energy consumption. Furthermore, new discrepancies will occur during the operational phase of the building. It is essential that the automatic controls and the Building Management System (BMS) make it easy to detect faults. It is also important that the control components are accessible for inspection, service and replacement.
Conference Paper
Full-text available
This paper presents a comparison between two model based diagnostics methodologies that can be used to detect and diagnose various faults that occur in Air Handling Units. The process from model development to inference diagnostics is highlighted with emphasis on the requirements for implementing a successful model based diagnosis solution. Comparative results of both methodologies on an air handling unit are presented and thoroughly discussed using as a benchmark the rule-based approach known as air-handling unit performance assessment rule-set.
Operational faults are common in the heating, ventilating, and air conditioning (HVAC) systems of existing buildings, leading to a decrease in energy efficiency and occupant comfort. Various fault detection and diagnostic methods have been developed to identify and analyze HVAC operational faults at the component or subsystem level. However, current methods lack a holistic approach to predicting the overall impacts of faults at the building level—an approach that adequately addresses the coupling between various operational components, the synchronized effect between simultaneous faults, and the dynamic nature of fault severity. This study introduces the novel development of a fault-modeling feature in EnergyPlus which fills in the knowledge gap left by previous studies. This paper presents the design and implementation of the new feature in EnergyPlus and discusses in detail the fault-modeling challenges faced. The new fault-modeling feature enables EnergyPlus to quantify the impacts of faults on building energy use and occupant comfort, thus supporting the decision making of timely fault corrections. Including actual building operational faults in energy models also improves the accuracy of the baseline model, which is critical in the measurement and verification of retrofit or commissioning projects. As an example, EnergyPlus version 8.6 was used to investigate the impacts of a number of typical operational faults in an office building across several U.S. climate zones. The results demonstrate that the faults have significant impacts on building energy performance as well as on occupant thermal comfort. Finally, the paper introduces future development plans for EnergyPlus fault-modeling capability.
An air handling unit’s energy usage can vary from the original design as components fail or fault – dampers leak or fail to open/close, valves get stuck, and so on. Such problems do not necessarily result in occupant complaints and, consequently, are not even recognized to have occurred. In spite of recent progress in the research and development of diagnostic solutions for air handling units, there is still a lack of reliable, scalable, and affordable diagnostic solutions for such systems. Modeling limitations, measurement constraints, and the complexity of concurrent faults are the main challenges in air handling unit diagnostics. The focus of this paper is on developing diagnostic algorithms for air handling units that can address such constraints more effectively by systematically employing machine-learning techniques. The proposed algorithms are based on analyzing the observed behavior of the system and comparing it with a set of behavioral patterns generated based on various faulty conditions. We show how such a pattern-matching problem can be formulated as an estimation of the posterior distribution of a Bayesian probabilistic model. We demonstrate the effectiveness of the approach by detecting faults in commercial building air handling units.
Demand control is particularly energy efficient and reliable when combined with displacement ventilation (DCDV). In order to investigate how much DCDV in practice reduce the ventilation air volumes and the energy demand, two Norwegian schools with CO2-sensor based demand controlled displacement ventilation (DCDV-CO2), Jaer School and Mediå School, are analysed and compared with traditional constant air volume (CAV) mixing ventilation. During daytime operation with normal school activity, DCDV-CO2 reduces the ventilation air volume by 65–75% in both schools compared to CAV. For Mediå School, both the airflow rates and the energy performance were analysed through measurements and use of a detailed, calibrated simulation model. The analysis period was 11–17 November, 2002. It was found that during this week, DCDV-CO2 daytime operation weekdays reduce the total heating energy demand by 21%, the amount of unrecovered heat in the exhaust ventilation air by 54%, and the average airflow rate by 50%. Presuming constant fan efficiency it was also found that DCDV-CO2 daytime operation weekdays reduce the fan energy consumption by 87% the analysed week.
One hundred and fifty-seven classrooms for fourth form pupils were inspected at 81 randomly selected schools in Oslo, Norway. Primary school classrooms in Oslo have on average 22 occupants present, while the maximum capacity is 30. Classrooms are typically used 4h daily for normal school activities. The corresponding ventilating air volume and energy use has been analysed for three ventilation strategies: (a) constant air volume [CAV], (b) CO2 sensor based demand-controlled ventilation [DCV-CO2], and (c) infrared occupancy sensor based demand-controlled ventilation [DCV-IR].DCV-CO2 and DCV-IR reduce the energy use due to ventilation in the average classroom to 38% and 51%, respectively, compared to the corresponding energy use for a CAV system operating with full airflow from 7:00am to 5:00pm.
This paper presents the results of a site survey study on the faults in variable air volume (VAV) terminals and an automatic fault detection and diagnosis (FDD) strategy for VAV air-conditioning systems using a hybrid approach. The site survey study was conducted in a commercial building. 20.9% VAV terminals were ineffective and 10 main faults were identified in the VAV air-conditioning systems. The FDD strategy adopts a hybrid approach utilizing expert rules, performance indexes and statistical process control models to address these faults. Supported by a pattern recognition method, expert rules and performance indexes based on system physical characteristics are adopted to detect 9 of the 10 faults. Two pattern recognition indexes are introduced for fault isolation to overcome the difficulty in differentiating damper sticking and hysteresis from improper controller tuning. A principal component analysis (PCA)-based method is developed to detect VAV terminal flow sensor biases and to reconstruct the faulty sensors. The FDD strategy is tested and validated on typical VAV air-conditioning systems involving multiple faults both in simulation and in situ tests.
The effects of faults on an air handling unit (AHU) are often poorly evaluated (assuming indoor air quality (IAQ) problems), and under-estimated in consumption balances. Based on an experimental site, this paper presents a quantitative study of some different faults that can appear on AHUs.Through a systemic analysis and a suitable modelling, the effects of several faults are simulated, using graphical software. Three families of faults are studied: control of a three-way valve, mixing box dampers (flow problems), and sensor inversion. Most of them find expression in an increase of the system consumption. The IAQ, seen here as the CO2 rate, is studied thanks to the developed piece of software; the IAQ impact of faults is not accessible without such tool.The software is then applied to a combination of several faults, in order to bring out major tendencies.
IEA Annex 25: Building Optimization and Fault Diagnosis Source Book
  • J Hyvarinen
  • S Karki
J. Hyvarinen and S. Karki, IEA Annex 25: Building Optimization and Fault Diagnosis Source Book, (1996)