Demand-controlled ventilation through a decentralized mechanical ventilation unit for office buildings
Bonato Paolo1, D’Antoni Matteo2 and Fedrizzi Roberto3
1Eurac Research, Institute for
Renewable Energy
Bolzano, Italy
[email protected]
2Eurac Research, Institute for
Renewable Energy
Bolzano, Italy
[email protected]
3Eurac Research, Institute for
Renewable Energy
Bolzano, Italy
[email protected]
ABSTRACT
The paper focuses on the energetic and economic
profitability of demand-controlled ventilation (DCV)
strategies implemented in a decentralized ventilation
application for tertiary office buildings.DCV consents to
modulate the ventilation rate over time and meet the
ventilation demand of the zone minimizing the energy-
penalty of overventilation. Its cost-effectiveness is
however not always guaranteed, since it depends on
buildings use, climate, HVAC features and it should be
assessed for each application [1].Numerical simulations
are carried out for a set of representative European
climates to compare the effectiveness of three sensor-
based demand-controlled ventilation strategies with a
baseline control strategy. To this end, numerical models
of the reference zone and the ventilation unit are
developed in TRNSYS and the occupancy profile is
defined with a stochastic approach to provide a more
realistic user behavior.It is found that the energy savings
(heating, cooling and fan energy consumption) achieved
with sensor-based demand-controlled ventilation are very
limited for the selected application in all scenarios.
Energy efficiency measures – such as the use of a high-
efficiency heat recovery unit, the exploitation of free
cooling or the installation of an high-performance
ventilation unit – can significantly limit the energy
savings achieved with DCV.
Author Keywords
Decentralized ventilation; Ventilation strategies; Product
development; TRNSYS; Multifunctional facades.
ACM Classification Keywords
Design; Experimentation; Performance
1 INTRODUCTION
The design of modern buildings pursue energy efficiency by
improving thermal insulation and air-tightness of the
envelope. As the heat losses across the building envelope
decrease, the adoption of effective ventilation technological
solutions and control strategies is crucial to further lower the
building energy demand while satisfying thermal and IAQ
comfort standards.
In this regard, decentralized ventilation has recently gained
the attention of the market and technological solutions are
commercially available for both residential and tertiary
building sectors. The air exchange is realized across the
building envelope and multiple devices are installed to
satisfy the ventilation demand of individual spaces.
Decentralized ventilation benefits from several advantages
over traditional centralized installations, such as lower
power consumption (up to 50% lower average specific fan
power [2]) and reduced technical spaces (from 30 cm [3] to
60 cm [4] lower floor-to-floor height). In addition, the
downsizing of the ductwork can lead a higher degree of
compartmentalization of the spaces with a consequent better
control over the spread of pests, noise, odors, fire and smoke
[5]. Decentralized ventilation systems are however typically
more expensive to maintain because of the high number of
installed devices [6] and complementary solutions have to be
found to ventilate the core of the building. In addition, the
noise emission and the regulation of indoor humidity may be
relevant issues.
With respect to control strategies, demand-controlled
ventilation (DCV) reduces the energy penalty due
overventilation by controlling the outdoor airflow rate intake
based on occupancy level [7]. DCV has already been studied
in literature [1,8] and is widely recognized as a viable
ventilation strategy in standards and norms, e.g. ASHRAE
62.1 [9], EN 15251 [10]. In addition, DCV is valorized as an
energy efficiency measure in different building standards,
including LEED [11] and WELL [12] certification
programs.
Demand-controlled ventilation can be operated based on the
input of sensors, such as CO2 tracers or passive infrared
(PIR) sensors. CO2 tracers measure the concentration of
carbon dioxide, which is considered a predictable indicator
SimAUD 2018 June 05-07 Delft, the Netherlands © 2018 Society for Modeling & Simulation International (SCS)
of human occupancy. They should be located at the normal
breathing zone height [13] and away from doorways, heat
sources, people and air inlet devices [14] or, in alternative,
in the ventilation system return ductwork [15]. Different CO2
control strategies have been studied in the last decades, from
the simple on/off and proportional control strategies to the
more sophisticated PID algorithms [16] and fuzzy logic. PIR
sensors detect infrared light radiation from objects in the
field of view (typically 6-10 m distance, up to 360° angle).
They are typically inexpensive and easily integrated into
artificial lighting, heating and alarm systems. Other options
may be the interaction with light switches or with the radio
frequency identification systems that provide access to
locked building doors.
Concerning energy-related issues, demand-controlled
ventilation is likely to reduce the energy demand (heating,
cooling or fan energy use) in nearly all buildings and
climates [8], but most authors report that the cost-
effectiveness of the solution largely depends on climate,
occupancy, building thermal load, energy prices and HVAC
system features [1,8,17]. The International Energy Agency
in EBC Annex 18 [18] reported that the highest energy
savings are achieved in buildings where the occupancy is
highly variable and unpredictable such as sport centers (up
to 60%), assembly halls (up to 50%) and department stores
(up to 70%). The potential energy savings are instead lower
in office buildings (20-30% for 40% average presence and
3-5% for 90% average presence). In other works, the
implementation of DCV in office spaces allowed to reduce
the gas consumption of 30% for different locations [17] or
the total annual energy load (heating and cooling) of roughly
20% [19].
These topics are faced within the framework of an R&D
collaboration with a building façade manufacturer (Stahlbau
Pichler Srl) and an HVAC manufacturer (Bini Clima Srl).
The project aims to develop new concepts of unitized
curtain-wall façade modules for tertiary office buildings. In
this context, a decentralized ventilation unit is conceived and
a mock-up is assembled and tested in laboratory.
At this stage of the project, the optimization of control
strategies is a key step to enhance the performance of the
ventilation unit and improve the management of the
airflows: DCV can be embedded in decentralized ventilation
units with limited efforts, as no extra investment for VAV
boxes is required.
Nevertheless, the energy savings achieved with DCV in
decentralized ventilation concepts in office buildings are not
clear-cut:
• Decentralized ventilation units typically operate at lower
air pressure drops and specific fan power with respect to
centralized solutions. As consequence, the maximum
electricity savings achievable with DCV are limited.
• The installation of a high-efficiency heat recovery unit in
the ventilation unit in compliance with ErP Directive
2009/125/EC [20] is expected to reduce the energy penalty
of overventilation and thus also the maximum space
heating energy savings achievable with DCV.
• In literature, it is reported that the savings achievable with
DCV in office buildings are lower with respect to other
building typologies because the occupancy schedule is not
as unpredictable and variable.
As discussed above, DCV is an interesting solution but the
energy benefits need to be accurately quantified to
understand at which extent DCV is profitable for this
specific application. For this purpose, system simulation can
effectively assist the product design process. As seen, a
variety of sensors with different degrees of accuracy can be
used to detect the level of occupancy. In order to define the
range of profitability, three DCV strategies based on an
occupancy sensor, a people counter and a CO2 tracer are
developed and expected energy and cost savings for a typical
office cell are evaluated in comparison to a baseline scenario.
2 FAÇADE CONCEPT
The façade module is a pre-fabricated unitized curtain-wall
façade module that integrates a ventilation unit designed to
provide ventilation (up to 300 m3/h fresh air) and space
heating/cooling to typical office cells (Figure 1). The
ventilation unit is hosted in the parapet of a façade module
and is connected to the exterior by two apertures realized on
the side and on the bottom of the lower infill element. The
ventilation unit is wrapped in a layer of thermal insulation
material to reduce the thermal transmittance of the façade
module.
Figure 1. View of the reference office zone.
A simplified layout of the ventilation unit is presented in
Figure 2. The combined electrical consumption of the two
EC fans (M1 and M2 in Figure 2) ranges between 0.09
Wh/m3 to 0.4 Wh/m3 depending on the operation mode. The
ventilation unit is equipped with a counter-flow heat
recovery unit (HRU) with an effectiveness ranging between
83% and 89% in compliance with ErP Directive
2009/125/EC that can be entirely bypassed when convenient.
A F5 class filter and a G4 class filter (F1a, F1b and F2) are
mounted respectively in the fresh and return air ducts. A two-
way valve (V1) controls the water flow in the air-water heat
exchanger, whereas three dampers outline the air circuits in
the ventilation unit. More specifically, damper S1 and S3
control the bypass of the HRU, whereas damper S4 regulates
the recirculation of indoor air.
Figure 2. Simplified layout of the ventilation unit.
3 NUMERICAL MODELLING
A numerical model of the energy concept is developed in the
TRNSYS simulation environment [21]. Yearly simulations
are carried out by using a simulation time-step of 5 minutes.
The modelling activities can be summarized as follows:
• definition of reference thermal zone and climates;
• development of a numerical model of the ventilation unit
and calibration with laboratory measurements;
• definition of stochastic occupancy profiles reproducing the
fluctuation of the time of arrivals, departures etc.;
• development and implementation of three sensor-based
DCV strategies and a baseline ventilation strategy.
3.1 Ventilation unit
The ventilation unit is conceived as a grey-box numerical
model where each component is modelled by Types from the
standard or TESS [22] TRNSYS libraries.
The supply and return fans are modelled as variable speed
fans (Type 111b) that can maintain any outlet volumetric
flow rate between zero and a rated value. As the Type cannot
directly handle pressure drop effects, the power consumption
of the fans is calculated separately using polynomials that
describe the dependence of the electrical consumption on the
volumetric flow rate for each aeraulic circuit.
The HRU is modelled as an air-to-air heat exchanger by Type
760, which implements a “constant effectiveness - minimum
capacitance” calculation approach. The effectiveness of the
HRU is defined as a function of the volumetric air flow rates.
The sensible and latent heat transfer rates in the water coil
(Type 987) are defined according to a performance map
where water and air flow inlet conditions are used as
independent variables.
Airflow diverters (Type 11e) direct the air stream to different
outlets depending on the control signal and tee pieces (Type
11g) mix two inlet streams at different temperature or
humidity. Their combination consents to arrange the aeraulic
circuits defined by the active working scheme. Leakages and
thermal bridges across the air ducts are not accounted in the
current model.
Laboratory tests are carried out on a first prototype of
ventilation unit and the thermal and electrical performances
of individual components are measured to tune the TRNSYS
model. In particular, the following tests are conducted:
• The electricity absorbed by the supply and return fans is
measured with the use of energy meters for a set of fan
speeds in all possible system configurations. A map of the
electrical consumption is then drawn for the design
working conditions of the ventilation unit and provided to
the TRNSYS model.
• The performance maps of HRU and water coil provided by
the manufacturers are counter-checked for a number of
points in heating and cooling working modes. More
specifically, it was verified that the experimental measures
of the heat transfer between the fluid streams (air-to-water
or air-to-air) correspond to the expected values. The
thermal behavior of HRU and water coil is modelled in
TRNSYS with the use of the validated performance maps
of the installed components.
• As the ventilation unit is integrated in the building
envelope, it is important to characterize its performance as
passive thermal “layer”. To this end, the lumped thermal
resistance of the ventilation unit is evaluated under
stationary conditions in a climatic chamber with the “hot
box” method. The thermal resistance amounts to 0.53
(m2K)/W and is accounted in the current TRNSYS model
as a resistive layer in the spandrel panel stratigraphy.
3.2 Reference thermal zone
Because of the potentially high computational efforts, the
energy analysis of high-rise office buildings by means of
transient simulations needs adequate simplifications in the
development of a numerical model. To this end, common
praxis is to use a reference office cell for the assessment of
the energy demand of office buildings [23,24].
The reference zone is identified as a typical office cell
(Figure 1) that is 4.5 m wide, 6 m deep and 3 m high,
resulting in 27 m2 floor surface and 81 m3 gross air volume.
The façade wall has south exposure and is composed of three
1.5 m wide façade modules, of which one integrates the
decentralized ventilation unit. Each façade module
delimiting the thermal zone has a 60% window-to-wall ratio
and consists of a non-openable window and a lower infill
element. It is assumed that internal walls, floor and ceiling
do not exchange heat with adjacent zones (adiabatic
conditions), whereas the exterior surface of the façade is
exposed to the external environment.
The thermal behavior of the office room is modelled with
Type 56. Thermal characteristics of the building envelope
(opaque and transparent structures), internal gains,
ventilation and infiltration rates, shading factor and energy
generation and distribution efficiencies are listed in Table 1.
A setback strategy allows thermostat temperature variations
equal to -2 K (heating) and +1 K (cooling) during the closing
hours of the building.
Building assemblies – Glazing
g-value (glass)
Stockholm 0.63 -
Stuttgart 0.59 -
Rome 0.33 -
U-value (glass)
Stockholm 0.81 W/(m2K)
Stuttgart 1.40 W/(m2K)
Rome 1.29 W/(m2K)
U-value (window frame) 1.18 W/(m2K)
Building assemblies – Opaque surface
U-value
Stockholm 0.26 W/(m2K)
Stuttgart 0.33 W/(m2K)
Rome 0.57 W/(m2K)
Ventilation and infiltrations
Design vent. rate 11 L/s/pers
Night cooling vent. rate 1.5 1/h
Infiltration rate 0.15 1/h
Solar shadings
Shading factor (when closed) 70 %
Beam radiation limit for activ. 150 W/m2
Internal gains [26]
Persons – Latent gains 0.08 kg/h/pers
Persons – Sensible gains 70 W/pers
Appliances 7 W/m2
Artificial lighting 15.9 W/m2
Occupancy
Full occupancy 3 people
Crowding index 0.11 people/m2
Setpoint temperatures
Heating 20 °C
Cooling 26 °C
Energy generation and distribution
Avg. efficiency (gas boiler) 90 %
SEER (compression chiller ) 3.5 -
Thermal losses in pipework 10 %
Table 1. Office zone boundary conditions.
Three reference climatic conditions are selected for
simulation purposes. Stockholm (Sweden), Stuttgart
(Germany) and Rome (Italy) are chosen as they portray a
significant range of European climatic conditions and
represent relevant markets for the product under
development. The climate datasets used in the simulations
are generated using the database Meteonorm 7. They contain
hourly values of ambient air temperature, humidity,
pressure, solar radiation, cloudiness and other
meteorological parameters for a one-year period. For
comparison purposes, Table 2 shows average climatic
parameters for the selected locations, including:
• Ig,h and Ib,h are the annual global and beam solar irradiation
on horizontal surface;
• ϑamb,min and ϑamb,max are the annual minimum and maximum
dry-bulb ambient temperature;
• HDD12/20°C are the Heating Degree Days;
• CDD18°C are the Cooling Degree Days.
Parameter Unit Sto
ckh
olm
Stu
ttg
art
Ro
me
Ig,h kWh/(m2y) 953 1104 1637
Ib,h kWh/(m2y) 478 524 1013
ϑamb,min °C -16.3 -12.4 -1.4
ϑamb,max °C 29.6 32.3 33.1
HDD Kd 3998 3220 1355
CDD Kd 92 163 639
Table 2. Annual climatic parameters.
The indoor air quality is evaluated by calculating the CO2
concentration with a single-node model based on equation
(1) [1]. A number of assumptions is made:
• The measure of the CO2 concentration is accurate enough
to neglect the inaccuracy of the sensor (typically ±75 ppm
[26]);
• The indoor air volume is well-mixed;
• The outdoor CO2 concentration Cout and emission rate E
are fixed to 400 ppm and 0.31 L/min/person [8,9],
respectively.
e
V
EpCeCC out 11061 4 (1)
where:
C(τ) := indoor CO2 concentration at time-step τ [ppm];
Δτ := length of the time-step [h];
E := CO2 emission rate [L/min/person];
Cout := outdoor CO2 concentration [ppm];
p := number of occupants [person];
λ := air change rate (ventilation and infiltration) [1/h];
V:= room volume [m3].
3.3 Occupancy profile
Occupants can actively influence the surrounding
environment with interactions such as window opening,
shading control, lighting control and use of electrical
appliances.
One of the most common ways of modelling occupancy in
buildings is by repeating one or more daily occupancy
profiles, which may be specific to the building use or to the
occupant type. In this case, the variability of the occupancy
patterns of all users is replaced with an averaged behavior,
with the underlying assumption that simulation outcomes are
not significantly affected by this simplification.
As the presence of occupants directly affects the ventilation
unit operations, a more realistic characterization of the room
occupancy is required. To this end, a stochastic approach is
used to model the fluctuation in time of arrivals, departures
and breaks, so that a more natural spread of occupancy
patterns is composed around the reference average daily
occupancy profile.
In this work, the office room occupancy pattern is defined
through the stochastic approach formulated by Page et al.
[27] based on an inhomogeneous Markov chain interrupted
by periods of absence (weekends and holidays). During
regular working days, the state of presence (1-present or 0-
absent) at a given time-step (τ) only depends on the state of
presence at the previous time-step (τ-1). The probability of
transition (i.e. present to absent or vice versa) is time
dependent and is calculated according to a probability
presence profile and a “mobility parameter”. Average
occupancy profiles are commonly provided in energy codes
or standards for most building types. The mobility parameter
describes instead the probability of a change of state over
that of no change.
Figure 3. Average occupancy profile during working days.
In this work, the reference occupancy profile shown in
Figure 3 [25] and a constant parameter of mobility equal to
0.5 are used to generate a time series of the state of presence
for each user. The presence of multiple occupants sharing the
same zone is simulated by simply adding their presence
patterns, disregarding any possible interdependence. During
weekends and holidays (weeks number 1, 33, 34 and 52) the
office room is assumed to be empty.
3.4 Ventilation control strategy
The control logic of the ventilation unit is designed to
guarantee thermal comfort and a ventilation rate of 11
L/s/person, as prescribed for office spaces in UNI 10339 [28]
in compliance with UNI EN 15251 [10].
The fresh air intake is kept at the hygienic ventilation rate
(120 m3/h at full occupancy), unless the free cooling
potential is exploited. The supply ventilation rate can be
increased up to 300 m3/h recirculating indoor air to enhance
the heat transfer from the water coil to the office room.
The HRU is used to transfer heat between the fresh and
return air streams, but is by-passed when its use is not
convenient, typically summertime. The formation of ice in
the HRU during wintertime is prevented by mixing
recirculated indoor air with the fresh airflow before the inlet
of the HRU so that condensation does not occur in the return
air section of the HRU.
The control logic of the ventilation unit requires three inputs
that are (1) ambient air temperature, (2) indoor air
temperature and (3) occupancy level. The control logic is
formalized with a clear hierarchical approach, as shown in
Figure 4. Controllers elaborate feedback signals acquired by
temperature and presence sensors in Boolean format. The
operation schemes of the ventilation unit are defined as an
algebraic calculation of such Boolean variables. The control
signals are elaborated as combination of operation schemes
and modulation coefficients, which in turn can be constant
values or function of the measured variables.
Figure 4. Structure of the control logic.
Three advanced ventilation strategies (St2, St3 and St4) are
compared to a baseline strategy (St1), as described below:
• Fixed time schedule (St1, baseline). The ventilation unit
logic solely relies on a fixed time schedule that records the
opening hours of the building. The ventilation unit cannot
detect whether the room is occupied or not and thus
operates providing the design hygienic ventilation rate for
full occupancy (120 m3/h) during the opening hours of the
building;
• Occupancy sensor (St2). The ventilation unit detects
whether a zone is occupied or not (i.e. motion detectors,
light switch), but is unable to distinguish the occupancy
level. Consequently, the design hygienic ventilation rate
for full occupancy is provided whenever the presence of
one or more users is detected;
• People counter (St3). The ventilation unit detects the
number of people in the room (e.g. thermal counters) and
the hygienic ventilation rate for the full room is scaled
proportionally to the occupancy level;
• CO2 tracer (St4). A gas tracer measures the indoor
concentration of CO2, which is considered a predictable
indicator of occupancy. The hygienic ventilation rate is
adapted to the actual ventilation demand, which is
determined comparing the CO2 concentration in the office
room to a threshold. A multistage control is implemented
and the hygienic ventilation rate is varied between 0 and
120 m3/h in a discreet number of steps according to the
stage level.
In all scenarios, a purge cycle equivalent to 2 1/h air change
rates is performed before the opening hours of the building
as recommended in UNI EN 15251 [10] to reduce any
appreciable buildup of non-occupant-related contaminants.
4 RESULTS
Table 3 shows the simulation results for the three
representative locations and the four ventilation strategies
(St1, St2, St3 and St4). Annual space heating and cooling
energy demand and fans electrical consumption are
provided.
Location Strategy Qheat Qcool Wel
[kWh/(m2y)]
Stockholm
St1 19.9 10.8 4.6
St2 19.0 10.8 4.5
St3 17.7 10.8 4.4
St4 17.0 10.8 4.3
Stuttgart
St1 17.5 14.8 4.8
St2 16.6 14.8 4.7
St3 15.6 14.7 4.6
St4 15.0 14.7 4.5
Rome
St1 4.5 30.2 5.5
St2 4.1 30.0 5.5
St3 3.9 29.7 5.3
St4 3.8 29.7 5.3
Table 3. Annual space heating (Qheat) and cooling (Qcool) energy
demand and fans electrical consumption (Wel).
As expected, space heating and cooling demand respectively
increase and decrease with latitude. The space heating
demand of the office zone ranges from 4.5 kWh/(m2y) in
Rome to 19.9 kWh/(m2y) in Stockholm, whereas the space
cooling demand ranges from 30.2 kWh/(m2y) in Rome to
10.8 kWh/(m2y) in Stockholm. The space heating energy
savings achieved with DCV are largely dependent on the
climate, with decreasing savings from colder climates (up to
2.9 kWh/(m2y) in Stockholm) to warmer climates (up to 0.7
kWh/(m2y) in Rome). Space cooling energy savings are
instead visible only in Rome (around 0.5 kWh/(m2y)), where
DCV allows to reduce the fresh air intake during hot days in
summertime. The fans electrical consumption is rather
constant among climates (4.5-5.5 kWh/(m2y)) and savings
up to 0.3 kWh/(m2y) can be achieved with DCV. Comparing
the three different DCV strategies, ventilation strategies St4
and St2 achieve respectively the highest and the lowest
energy savings in all locations.
Figure 5. Fresh air intake and CO2 concentration (strategy St1).
Figure 6. Fresh air intake and CO2 concentration (strategy St2).
Figure 7. Fresh air intake and CO2 concentration (strategy St3).
Figure 8. Fresh air intake and CO2 concentration (strategy St4).
Concerning IAQ, the indoor air quality falls within class
IDA2 or better during all occupation hours in all scenarios,
according to the classification provided in EN 13779 [29].
Figures 5 to 8 show the fresh air intake and the CO2
concentration in the office room during the same working
day for all ventilation strategies.
5 DISCUSSION
Overall, the energy savings achieved with the sensor-based
DCV strategies are modest and in general lower than the
results seen in literature. The reasons of such deviation can
be motivated by three factors.
Firstly, the electricity savings that can be achieved are
limited. As already mentioned, decentralized ventilation
units typically profit from modest pressure drops and low
fans power and thus the margin to reduce the electrical
consumption is limited. In addition, the ventilation unit is
sized for a design air rate of 300 m3/h that is needed to deliver
enough space heating and cooling power to the office cell.
As consequence, the electricity savings achieved adjusting
the ventilation rate in the range 0..120 m3/h are low.
Furthermore, the efficiency of the EC fans is not constant but
tend to decrease at low-very low airflows. Such an effect can
be hardly accounted with simpler models based on the use of
constant specific fan power values, but it can be appreciated
when the fans electrical consumption is tuned to the
performance of real components, as in this case.
Secondly, an intensive exploitation of free cooling during
occupancy hours is observed in all locations. When free
cooling is exploited, DCV is not fruitful as the fresh air
intake is increased over the minimum hygienic ventilation
rate to maximize the valuable cooling effect of ventilation.
This aspect is especially relevant for modern office
buildings, where the good thermal insulation reduces the
heat losses through the building envelope and the high
internal gains might cause overheating over peak hours also
during mid-seasons.
Thirdly, the use of a high-efficiency HRU has a significant
impact on the success of DCV. In order to support this thesis,
an additional set of simulations is performed assuming a
lower heat recovery efficiency equal to 50%. Table 4 shows
the energy results for ventilation strategies St1 and St4.
Location Strategy Qheat Qcool Wel
[kWh/(m2y)]
Stockholm St1 28.3 10.6 4.4
St4 22.3 10.7 4.2
Stuttgart St1 24.0 14.6 4.5
St4 19.1 14.6 4.3
Rome St1 6.5 29.9 5.2
St4 4.9 29.5 5.1
Table 4. Annual space heating (Qheat) and cooling (Qcool) energy
demand and fans electrical consumption (Wel). (HRU eff: 50%).
The annual space heating energy savings achieved by St4 are
much more significant when the sensible efficiency of the
HRU is lower. Comparing the low-efficiency scenario to the
high-efficiency scenario, the space heating energy savings
achieved by St4 double in all the analyzed climates and
increase from 2.9 to 6.1 kWh/(m2y) in Stockholm, from 2.4
to 4.9 kWh/(m2y) in Stuttgart and from 0.7 to 1.6 kWh/(m2y)
in Rome. A modest reduction of fans electrical consumption
and space cooling energy demand can be observed with
respect to the data shown in Table 3. This is due to a slightly
lower average temperature in the office cell that reduces the
use of the cooling coil and of the free cooling operation
mode.
To conclude, an economic analysis is conducted to evaluate
the profitability of DCV in the analyzed scenarios, assuming
a purchase cost of energy equal to 0.20 €/kWh for electricity
and 0.10 €/kWh for gas. Table 5 shows the energy-related
operation costs and savings for all scenarios.
Location Strategy Cost [€/y] Saving [€/y]
Stockholm
St1 109.7 -
St2 106.3 3.3
St3 101.2 8.5
St4 98.4 11.2
Stuttgart
St1 109.4 -
St2 106.1 3.3
St3 102.0 7.4
St4 99.9 9.6
Rome
St1 96.8 -
St2 94.5 2.3
St3 92.9 3.9
St4 92.3 4.5
Table 5. Annual energy-related operation costs and savings.
The cost savings are low in scenarios, especially in Rome
where they remain under 4.5 €/y. Savings up to 9.6 €/y and
11.2 €/y are achieved in Stuttgart and Stockholm. It is also
observed that ventilation strategy St4 achieves the highest
cost savings with respect to the other strategies in all the
considered climates.
6 CONCLUSIONS
Based on the analyzed building and weather file, one of the
preliminary conclusion is that the implementation of a
demand-controlled ventilation (DCV) strategy in a
decentralized ventilation unit such as the one developed does
not lead to significant energy or cost savings. With respect
to the baseline scenario, the highest cost savings are
achieved with the use of a CO2 tracer (strategy St4). In this
case, the energy bill can be reduced of 4.5 €/y in Rome, 9.6
€/y in Stuttgart and 11.2 €/y in Stockholm.
The simulation work allowed to verify how energy
efficiency measures such as the use of high-efficiency
HRUs, the exploitation of free cooling or the installation of
ventilation units with low specific fan power are not synergic
to the use of DCV and may reduce its cost-effectiveness.
Future studies could investigate the link between average
occupancy level and energy savings and define at which
conditions the implementation of DCV in the developed
decentralized ventilation unit is profitable. Moreover, a
sensitivity analysis could be carried out to understand how
design choices such as thermostat temperatures, façade
assemblies or building use affect the overall effectiveness of
DCV.
REFERENCES
1. Fisk, W. J., De Almeida, A. T. Sensor-based demand
controlled ventilation: a review. Energy and buildings
29 (1998), 35-45.
2. Merzkirch, A., Maas, S., Scholzen, F., Waldmann, D.
Field tests of centralized and decentralized ventilation
units in residential buildings – Specific fan power,
heat recovery efficiency, shortcuts and volume flow
unbalances, Energy and Buildings 116 (2016), 376-
383.
3. Mahler, B. Himmler, R. Results of the evaluation
study DeAL, Decentralized façade integrated
ventilation systems, Proc. 8th ICEBO (2008).
4. Franzke, U. Heidenreich, R., Ehle, A., Ziller, F.
Comparison between Decentralized and Centralized
Air Conditioning Systems, ILK Dresden (2003).
5. CMHC. Assessment of Suite Compartmentalizing and
Depressurization in new High-Rise Residential
Buildings, Technical Series 05-112 (2005).
6. Berkel, S. Residential ventilation: a review of
established systems and preliminary laboratory
investigation of an innovative fire wire heat recovery
ventilator, Proc. 14th conference of building science
and technology.
7. Schell, B., Turner, S.C., Shim, R.O., Application of
CO2-based demand-controlled ventilation using
ASHRAE standard 62: Optimizing Energy Use and
Ventilation, ASHRAE Transactions 104 (1998),
1213-1225
8. Emmerich, S. J., Persily, A. K. State-of-the-Art
Review of Cow Demand Controlled Ventilation
Technology and Application, NISTIR 6729 (2001).
9. ASHRAE. ASHRAE Standard 62.1-2016: Ventilation
for Acceptable Indoor Air Quality.
10. European Commission. EN 15251:2008 Indoor
environmental input parameters for design and
assessment of energy performance of buildings
addressing indoor air quality, thermal environment,
lighting and acoustics.
11. U.S. Green Building Council. LEED Reference Guide
for Green Building Design and Construction, (2009).
12. International WELL Building Institute. WELL
Building Standard, Version 1.0 (2014).
13. Stymne, H., Sandberg, M., Mattsson, M., Dispersion
pattern of contaminants in a displacement ventilated
room – implications for demand control, Proc. 12th
AIVC (1991).
14. Stymne, H., Sandberg, M., Dispersion pattern of
carbon dioxide from human sources - a factor to
consider in demand controlled ventilation systems,
Demand Controlled Ventilating Systems – Case
Studies, IEA Annex 18 (1992).
15. Reardon, J. T., Shaw, C. Y., Vaculik, F., Air change
rates and carbon dioxide concentrations in a high-rise
office buildings, ASHRAE Transactions 100 (1993),
pp. 1251-1263.
16. Chao, C.Y.H., Hu J.S, Development of a dual-mode
demand control ventilation strategy for indoor air
quality control and energy savings, Building and
Environments 39 (2003), 385-397.
17. Brandemuehl, M. J., Braun, J. E. The impact of
Demand-Controlled and Economizer Ventilation
Strategies on Energy Use in Buildings. ASHRAE
Transaction 105(2), Annual meeting 1999, USA.
18. International Energy Agency (IEA). A summary of
IEA Annex 18 - Demand Controlled Ventilating
Systems (1997).
19. Persily, A., Musser, A., Emmerich, S., Taylor, M.,
Simulation of Indoor Air Quality and Ventilation
Impacts of Demand Controlled Ventilation in
Commercial and Institutional Buildings, NISTIR 7042
(2003).
20. EU Commission Regulation No 1253/2014 of 07 July
2014 implementing Directive 2009/125/EC.
21. Klein, S.A., Beckman, W.A., Mitchell, J.W., Duffie,
J.A., Duffie, N.A. et al., TRNSYS 17, transient
system simulation program. University of Wisconsin,
Madison, WI, USA (1979), www.trnsys.com.
22. TESS. TESS Component Libraries: General
Descriptions, http://www.trnsys.com/tesslibraries/
TESSLibs17_General_Descriptions.pdf.
23. Erhorn, H., et al., BESTFACADE, WP 4 Report
“Simple calculation method”, (2007).
24. CISBE, TM35: Environmental Performance Tooling
for Glazed Facades, (2004).
25. SIA. SIA 2024:2015: Raumnutzungsdaten für die
Energie- und Gebäudetechnik.
26. ASHRAE, ASHRAE User’s Manual. Standard 62.1-
2004: Ventilation for Acceptable Indoor Air Quality,
(2005).
27. Page, J., Robinson, D., Morel, N., Scartezzini, J. L. A
generalized stochastic model for the simulation of
occupant presence. Energy and buildings 40 (2008),
8398.
28. Italian National Unification Body (UNI). UNI
10339:1995 Impianti aeraulici a fini di benessere.
29. European Commission, EN 13779:2007 Ventilation
for non-residential buildings – Performance
requirements for ventilation and room-conditioning.
