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HARMONIZING AIR CONDITIONING INSPECTION AND
AUDIT PROCEDURES IN THE TERTIARY BUILDING
SECTOR (EIE-07-132)
Work package No.: 7
Title: Improved Inspection and Audit Simulation Tools
Simulation Tools – Reference Book (Draft 2)
Liege, 2008 September 9th
Editor: Stephane Bertagnolio
University of Liege, Belgium
Associates:
Jean Lebrun
University of Liege, Belgium
Philippe André
University of Liege, Belgium
Pierre-Yves Franck
University of Liege, Belgium
Jules Hannay
University of Liege, Belgium
Cleide Aparecida Silva
University of Liege, Belgium
Cleide Aparecida Silva
University of Liege, Belgium
First name, Last name
Institution, Country
First name, Last name
Institution, Country
First name, Last name
Institution, Country
First name, Last
Institution, Country
Page 2
PROJECT TEAM
The sole responsibility for the content of this document lies with the authors. It does not
represent the opinion of the Community. The European Commission is not
responsible for any use that may be made of the information contained therein.
Association pour la Recherche et le
Développement des Méthodes et
Processus Industriels - ARMINES
France
MacWhirter Ltd
UK
Politecnico di Torino
Italy
National and Kapodistrian
University of Athens
Greece
Welsh School of
Architecture/Cardiff
University
UK (Project co-ordinator)
Building Research
Establishment - BRE
UK (Subcontractor)
Austrian Energy Agency
Austria
Univerza v Ljubljani
Slovenia
Université de Liège
Belgium
Instituto de Engenharia
Mecanica e Gestao Industrial -
INEGI
Portugal
Page 3
CONTENTS
Scope of the work .................................................................................................................................. 5
Introduction ............................................................................................................................................ 6
1 Simulation for auditing .................................................................................................................. 7
1.1 Simulation for benchmarking .................................................................................................. 7
1.2 Simulation for auditing and ECOs evaluation ......................................................................... 8
1.3 Simulation assisted audit methodology ................................................................................... 9
1.3.1 BENCHMARK ................................................................................................................ 9
1.3.2 SIMAUDIT .................................................................................................................... 10
2 Modeling bases ............................................................................................................................ 12
2.1 Building modeling ................................................................................................................. 13
2.1.1 List of symbols ............................................................................................................... 13
2.1.2 Indoor conditions ........................................................................................................... 14
2.1.3 Wall thermal model ........................................................................................................ 16
2.1.4 Sensible heat gains ......................................................................................................... 20
2.1.5 Ventilation, infiltration and exfiltration sensible enthalpy flow rates ........................... 20
2.1.6 Validation ....................................................................................................................... 20
2.2 HVAC System Modeling ...................................................................................................... 25
2.2.1 BENCHMARK: Basic “Reference” HVAC System ..................................................... 25
2.2.2 SIMAUDIT: Range of Common HVAC Systems ......................................................... 33
3 Implementation and Interface ...................................................................................................... 34
3.1 Implementation ...................................................................................................................... 34
3.2 BENCHMARK: Inputs/Outputs/Parameters ......................................................................... 35
3.2.1 Outputs ........................................................................................................................... 35
3.2.2 Inputs .............................................................................................................................. 36
3.2.3 Parameters ...................................................................................................................... 36
3.3 SIMAUDIT: Inputs/Outputs/Parameters ............................................................................... 40
4 Example of Application ............................................................................................................... 41
4.1 Case study description ........................................................................................................... 41
4.1.1 Building design .............................................................................................................. 41
4.1.2 HVAC system ................................................................................................................ 42
4.1.3 Occupancy and operating profiles ................................................................................. 42
4.1.4 Nominal heat losses ....................................................................................................... 42
4.1.5 Nominal heat gains ........................................................................................................ 43
4.1.6 Control strategy .............................................................................................................. 43
4.1.7 Fans and air distribution network .................................................................................. 44
4.2 Recorded data analysis .......................................................................................................... 44
4.3 Use of simulation tools .......................................................................................................... 46
4.3.1 Benchmarking ................................................................................................................ 46
4.3.2 Calibration and analysis ................................................................................................. 47
4.3.3 Retrofit options .............................................................................................................. 50
4.3.4 Conclusion ..................................................................................................................... 51
Conclusion ........................................................................................................................................... 52
References ............................................................................................................................................ 53
Appendix .............................................................................................................................................. 56
Page 4
Appendix A: Walls Parameters Default Values ............................................................................... 56
External Walls .............................................................................................................................. 56
Internal Walls ............................................................................................................................... 60
Appendix B: List of inputs (BENCHMARK and SIMAUDIT) ...................................................... 61
Appendix C: List of parameters (BENCHMARK) .......................................................................... 62
Appendix D: List of outputs (BENCHMARK) ............................................................................... 66
Page 5
Scope of the work
This WP (WP7: Improved Inspection and Audit Simulation Tools) will review and improve the
AUDITAC modeling tools in the light of their use in WP4, as well as produce appropriate new tools
where possible dedicated to new aspects of the Inspection and Audit procedures to be produced
during the project.
Three tasks are identified in this WP:
Task 1: To review and improve the performance of the modeling tools produced in the
AUDITAC project. During AUDITAC, a number of simulation models devoted to
Inspection and Audit were developed. They include simplified modeling of the building, air-
conditioning equipment, chillers, cooling towers. These models were tested on a limited
number of cases and the purpose of this task is to extensively test these models on new cases,
using the Field Trials and Case Studies selected for the project.
Task 2: Development of further simulation models suitably adapted to the Inspection
and Audit requirements. This will cover approaches for including indoor air quality issues
in modeling. To provide new models where these are shown to be missing during WP4 –
particularly to further address the issues of indoor air quality and hygrothermal conditions
which were not addressed fully in AUDITAC, as the building model proposed in AUDITAC
only included a very rough consideration of indoor air quality and hygrothermal issues. The
modeling will couple building related aspects (contaminant balance in the zones) as well as
AHU aspects: economizer, filtering of contaminants, humidification and dehumidification.
Another important issue that will be tackled is the computation of networks (air ducting, fans,
dampers…).
Task 3: Production of executable files, documentation and practical ways to tune the
parameters of the A/C systems inspected on the basis of the (incomplete) technical
information likely to be available. From the models developed, tested and validated in tasks
1 and 2, practical tools will be produced with the objective of being able to give useful
guidance to the auditor in a short period of time. The issue of parameters’ tuning will be
analyzed in detail as this step will remain one of the major tasks of the auditor. It is
anticipated that default values will be systematically proposed and that the tool allows for
progressive inputs of case specific values, in conjunction with the availability of more
comprehensive information.
The main expected outcomes are:
Tested simulation models devoted to the Inspection and Audit of Air-Conditioning Systems;
Newly developed simulation models for the assessment of indoor air quality and
hygrothermal behavior in air-conditioned buildings;
New practical simulation-based tools, devoted to Inspection and Audits with facilities for
parameters tuning from limited available information.
Page 6
Introduction
Audit is required, among others, to identify the most efficient and cost-effective Energy
Conservation Opportunities (ECOs), consisting in more efficient use or in (partial or global)
replacement of the existing components.
Four audit stages are generally distinguished (Adnot, 2007):
1. The “benchmarking” helps in deciding if it is necessary to launch a complete
audit procedure; it’s based mainly on energy bills and basic calculations. A direct use
of such global data would not allow the auditor to identify “good”, “average” and
“bad” energy performances. The experimental identification of HVAC consumptions
is often almost impossible: these consumptions are, most of the time, not directly
measured, but “mixed” with other ones (lighting, appliances etc.). Simulation is then
of great help to define some, even very provisory, reference performances (or
“benchmarks”), in view of a first qualification of the current building performances.
Without simulation, some arbitrary normalization had been required before any
comparison of the recorded data on the studied installation with reference values
deduced from case studies or from statistics;
2. The aim of the “pre-audit” (also called “Walk-through Audit” or “Inspection”)
is to identify the main defects and “energy conservation opportunities” (ECO’s). Its
results are supposed to orient the future “detailed” audit. The inspection consists in a
visual verification of HVAC equipment, in an analysis of operating data records and
in a systematic disaggregation of recorded energy consumptions. Thanks to
parametric tuning, the building-HVAC simulation model can be fitted on the records
actually available (very often no more than monthly fuel and electricity bills) in such
a way to become a “baseline” simulation model, allowing the auditor to identify the
main energy consumers (lighting, appliances, fans, pumps, chiller, …) and to analyze
the actual performance of the building;
3. The “detailed” audit consists in a detailed and comparative evaluation of the
ECOs previously selected. At this step even more, simulation is the key tool;
4. The “investment grade” audit concerns the detailed technical and economical
engineering studies, justifying the costs of the retrofits. This fourth audit stage brings
the system (building + HVAC) to a new life cycle: new design, call for tenders,
submissions, evaluations, installations, commissioning, etc.
Page 7
1 Simulation for auditing
For this benchmarking and auditing targeted work, simulation models are required to be sufficiently
reliable, accurate, robust, easy to use and quickly adjusted to the studied installation (Adam et al.,
2006a).
Five types of models can be considered (Boysen, 1987):
o Static uni-zone models, involving a very limited number of parameters and
simplified calculations as degree-days calculations, are generally not sufficient for
audit of commercial buildings;
o Static multi-zone models are generally appropriate for the evaluation of ECO’s
related to the building envelope but not adapted to the evaluation of ECO’s related
to intermittent conditioning. These models work with a time step from one day up
to one month and give only average values;
o Dynamic uni-zone models work with a time step of one hour or less and consider
the building interior as uniform. These models can predict peak values and can be
used to evaluate ECO’s related to air conditioning system;
o Dynamic multi-zone models are the most detailed models and allow very accurate
calculation of indoor conditions and heating/cooling demands. Generally, these
models are used for design purposes, require a detailed description of the building
and the coupled system and are not always suited for audit purposes;
o HVAC system models, based on simple static to very detailed dynamic models of
HVAC components.
To suit to the purposes of the audit, it seems rational to use a simple dynamic uni-zone model
coupled to an HVAC system model. The building model would take into account the main physical
phenomena involved in building dynamics (transient heat transfer, ventilation and infiltration rates,
internal generated gains, solar gains…). Bleil de Souza et al. (2006) have shown that the parameters
which affect the most the calculation of indoor conditions are:
o Ventilation and infiltration rates;
o Internals generated gains;
o Transmittance of glass.
The HVAC system model would simulate heat and cool production systems, AHU components, air
and water distribution system and terminal units.
1.1 Simulation for benchmarking
Benchmarking is a very first challenge for the auditor, who must be able to make already a quick
evaluation of the building considered at early audit stage (preliminary inspection). The very first
questions to answer are:
- Is that building a “good” case (as said in medicine)?
- Is it worthwhile to submit it to a more detailed analysis?
- What might be the outcome of this analysis in terms of retrofit?
Answering these questions requires some diagnosis, which has to be established on the basis of the
very scarce information currently available: technical data contained in as-built files actually
Page 8
available, very global recordings of energy consumptions (fuel and electricity) and reference data for
comparison.
While normalization is required to allow comparison between data recorded on the studied
installation and reference values deduced from case studies or statistics; the use of simulation models
allow to assess directly the studied installation, without any normalization needed. Indeed, applying a
simulation-based benchmarking tool allows an individual normalization and allows avoiding size and
climate normalization.
So, rather than looking for very global weather indexes, it seems rational to use a simulation model
and run it over a few thousands of hours corresponding to one typical year. The current capabilities
of computers and simulation tools make this approach very efficient and allow considering the
climate as it is, without any simplification.
The level of detail required for the calculation of heating and cooling demands can be very different
(Adam et al., 2006a). For heating calculations, the major issues are a correct description of the
building envelope and a reasonably accurate evaluation of the air renewal (including infiltration and
ventilation). For cooling calculations, the fenestration area and orientation, the level and distribution
of the internal gains, the ventilation rates and the geographical location and the usability of the
thermal mass (if present and accessible) appear as critical issues.
Moving from heating and cooling demands to energy consumption involves modeling the HVAC
system. For benchmarking, the HVAC system can be considered as an “ideal” system ensuring
temperature, humidity and air quality control. This system has to be modeled with an optimal level of
details, corresponding to a compromise between the accuracy of the calculation.
1.2 Simulation for auditing and ECOs evaluation
Global monthly consumptions are insufficient to allow an accurate understanding of the building’s
behavior. Even if some very rough results can be expected from the analysis of monthly fuel
consumption, global electricity consumption records analysis do not allow to distinguish the energy
consumption related to AC from the consumption related to other electricity consumers.
Even if the analysis of the energy bills does not allow identifying with accuracy the different energy
consumers present in the facility, the consumption records can be used to calibrate building and
system simulation models. To assess the existing system and to simulate correctly the building’s
thermal behavior, the simulation model has to be calibrated on the studied installation. Of course,
basic data as building envelope characteristics or the type of HVAC system are easily identified but
infiltration rate or actual ventilation flow rate have to be adjusted. Unfortunately, no general
calibration procedure already exists and the adjustment of the developed simulation model will be
discussed later.
After having been calibrated to the adjusted, the baseline model can be used to identify the main
energy consumers (lights, appliances, fans, pumps…) and analyze the actual performance of the
building. Finally, selected energy conservation opportunities can be implemented, assessed and
compared to each other.
Page 9
1.3 Simulation assisted audit methodology
Early developments of simulation tools were mostly oriented towards supporting system design, i.e.
mainly the selection and sizing of HVAC components (Lebrun and Liebecq, 1988). The usefulness
of simulation tools in further stages of the building life cycle appeared later, among others with the
apparition of friendly engineering equation solvers and of reliable simulation models. Energy
simulation may help all along the building life cycle, from early design until last audit and retrofit
actions. Building and HVAC simulation models should therefore be continuously available, but in
different forms, according to what is expected from the simulation and according to the information
actually available.
Today, the simulation bottleneck is no more the computer, but the understanding of the user.
Simulation models have therefore to be designed in such away to make easier this understanding.
Hopefully the equation solvers presently available (Klein, 2008a) open the way to the development
of fully transparent and fully adaptable simulation models, with all equations written as in a text
book. This means that a simulation program, its user guide and its reference guide can be combined
into only one file, fully readable and directly executable.
Reference and detailed models of HVAC components may help a lot in the commissioning process,
among others for functional performance testing (Visier and Jandon, 2004). These detailed models
may also help a lot in the daily system management, among others for fault detection and diagnosis
(Jagpal, 2006). More global (and simplified) simulation can be used in real time to “emulate”
building energy management systems (Lebrun and Wang, 1993). New simulation capabilities are
also appearing today in the domain of energy audit (Adnot, 2007; Knight, 2008).
Two simulation tools are currently being developed in the frame of the HARMONAC project. The
two tools are called “BENCHMARK” and “SIMAUDIT”:
1.3.1 BENCHMARK
BENCHMARK is used to compute the "theoretical" (or “reference“) consumptions of the building,
supposed to be equipped with a “typical” HVAC system, including air quality, temperature and
humidity control. The building is seen as a unique zone, described by very limited number of
parameters. This first simulation tool should help the auditor in getting, a very first impression about
the performances of the system and a very first interpretation of the recorded consumptions (Figure
1).
Figure 1: use of "BENCHMARK"
The outputs, inputs and parameters must be selected according to the specific needs of the user. As in
some other software’s, as TRNSYS for instance (Klein et al., 2008b), the parameters are here defined
as selected inputs which are not supposed to vary during the simulation.
Page 10
The main outputs of the tool presented here are:
- Air quality and hygrothermal comfort achievements: CO2 contamination, temperature,
humidity, PPD and PMV
- Global power and energy consumptions : Fuel and Electricity consumptions
- HVAC components specific demands
- Performances of the mechanical equipments: COP, efficiencies…
The main inputs are:
- Weather data : hourly values of temperature, humidity, global and diffuse radiations
- Nominal occupancy loads (in W/m²), occupancy and installation functioning rates
- Comfort requirements: air renewal, temperature and humidity set points.
- Control strategies: feedback on indoor temperature and relative humidity, feed forward on
occupancy schedules and calendar.
The main parameters are:
- Dimensions, orientation and general characteristics of the building envelope (e.g.
“heavy”, “medium” or “light” thermal mass and walls U values).
- Sizing factors of the main HVAC components
The main parameters described here above are entered through the control panels. The other
parameters of the model, as HVAC system characteristics, nominal performances and capacities are
automatically computed through a pre-sizing calculation, or defined on the basis of default values,
given in European standards (prEN 13053 and 13779). Other information as weather data and
occupancy rate are provided in tables.
1.3.2 SIMAUDIT
Global monthly consumptions are often insufficient to allow an accurate understanding of the
building’s behavior. Even if some very rough results can be expected from the analysis of monthly
fuel consumption, global electricity consumption records analysis do not allow to distinguish the
energy consumption related to AC from the consumption related to other electricity consumers.
Even if the analysis of the energy bills does not allow identifying with accuracy the different energy
consumers present in the system considered, the consumption records can be used to adjust some of
the parameters of the simulation models (Figure 2). Some basic data, as building envelope
characteristics or the type of HVAC system, are easy to identify, but parameters related to infiltration
and ventilation flow rates, operating and occupancy profiles and performances of HVAC components
need always to be adjusted. To this end, SIMAUDIT offers a larger range of available HVAC
equipment and offers to calibrate the simulation model by tuning these parameters.
Figure 2: use of "SIMAUDIT" – Calibration process
Page 11
The building is still seen as a unique zone. The system includes an equivalent global AHU and
several types of TU (radiators, fan coils, cooling ceiling, etc.).
After having been calibrated to the recorded data, the baseline model can be used to identify the main
energy consumers (lights, appliances, fans, pumps…) and to analyze the actual performance of the
building.
Page 12
2 Modeling bases
For benchmarking, as for further audit actions, the simulation tool must handle with realism:
- building (static and dynamic) behavior,
- weather and occupancy loads,
- comfort requirements and control strategies (air quality, air temperature and humidity),
- full air conditioning process and characteristics of all HVAC system components
(terminal units, Air Handling Units, air and water distribution, plants)
The level of detail required for the calculation of H/C demands can vary a lot from case to case:
- For heating calculations, the major issues are a correct description of the building
envelope and an accurate evaluation of air renewal.
- For cooling calculations, the fenestration area and orientation, the intensity and
distribution of internal gains, the ventilation rates and the geographical location appear as
critical issues.
At benchmarking stage mainly, the simulation tools have also to be usable with a limited quantity of
information only, depending on data actually available. These tools must be easy-to-use, transparent,
reliable, sufficiently accurate and robust. The simulation tool presented here after includes models of
both the building and the HVAC equipment. These models are submitted to different loads and
interact at each time step with a control module (Figure 3).
Figure 3: Model block diagram
The main phenomena involved in building dynamics are considered in order to compute realistic
heating and cooling demands. Indeed, the indoor conditions of the zone come from the equilibrium
established among many different influences. A compromise is made between the number of
influences taken into account and the simplicity of the model: transient heat transfer through walls,
energy storage in slabs, internal generated gains, solar gains through windows, infrared losses and, of
course, ventilation and heating/cooling devices are actually taken into account.
The BENCHMARK tool has been previously described by Bertagnolio and Lebrun (2008a). This
description is presented and completed hereunder. A description of the SIMAUDIT tool follows.
Page 13
2.1 Building modeling
Both BENCHMAK and SIMAUDIT tools are based on the same mono-zone dynamic building
model.
The dynamic behavior of the building is taken into account by a simplified model to limit the
quantity of required data and ensure robustness and transparency. It is based upon a RC network
including five thermal masses, corresponding to a large occupancy zone, surrounded by external
glazed and opaque walls (Figure 4). This scheme corresponds to a typical office building, mainly
composed of lattice structure and slabs. The R-C model was the object of a comparative validation
works carried out using the BESTEST procedure.
Figure 4: Building zone model - RC network
2.1.1 List of symbols
2.1.1.1 Variables
AU: heat transfer coefficient, in W/K,
Q : Thermal power, in W,
W : Electrical power, in W,
V : Volume flow rate, in m³/s,
P : Pressure drop, in Pa,
: Effectiveness or emissivity,
t: temperature, in °C,
w: humidity ratio, in kg/kg,
V: volume, in m³,
C: thermal capacity, in J/K, or proportional control gain,
cp: specific heat, in J/kg-K,
MM: molar mass, in g/mol,
X: volumetric concentration, or control variable,
M: mass, in kg,
h: combined convective – radiative heat transfer coefficient, in W/m²-K, or enthalpy, in J/kg
Page 14
Albedo: albedo,
SF: solar factor
R: thermal resistance, in K/W,
I: solar radiation, in W/m²
2.1.1.2 Subscripts
w: water
a: air
in: indoor
s: sensible
out: outdoor
surf: surface
vent: ventilation
inf: infiltration
1: initial value
su: supply
ex: exhaust
occ: occupant
TU: terminal unit
CO2: Carbone dioxide
n: nominal
sh: shaft
u: useful
c: consumed
2.1.1.3 Greek symbols
τ: time, in sec,
ρ: density, in kg/m³,
2.1.2 Indoor conditions
The indoor conditions (CO2 contamination, global temperature and humidity) are computed by
means of three different mass and energy balances. Indoor comfort indexes (PMV and PPD) are
evaluated at each time step through classical Fanger’s equations (1970).
2.1.2.1 Sensible heat balance
A sensible heat balance is made on the indoor node to compute the combined convective - radiative
indoor temperature.
A mono-zone building model based on an R-C network is used here. The heat flow emitted by the
surfaces of the walls (roof, floor, opaque frontages and windows), the enthalpy flow rate
corresponding to ventilation and infiltration air and the internal sensible gains (including local
heating/cooling and internal generated gains) are summed (eq. 1), in order to compute the energy
storage inside the indoor environment (eq. 2 and 3). This energy storage is computed by the means of
a first order differential equation. A correction factor (Fa,in ) has to be applied to the air capacity in
order to take into account the effect of the vertical air temperature gradient in the zone (Lebrun,
1978; Laret, 1980).
inssventswindowsinsurffrontagesopaqueinsurffloorinsurfroofin
QHHQQQQd
dU,inf,,,,,,,,,
(1)
Page 15
2
1
dd
dUU
inin
1,,,* inainainin ttCU
apaininain cVFC ,, ***
2.1.2.2 CO2 balance
A first mass balance is used to compute the CO2 concentration in the indoor environment.
The CO2 flow rate entering the zone is due to two main contributions (eq. 5):
- CO2 brought by ventilation (eq. 6), positive infiltration and negative exfiltration air flow
rates (eq. 7),
- CO2 produced by the occupants (function of the occupant metabolic rate, eq. 8).
Treated ventilation air CO2 concentration is given as an output of the HVAC system model, while,
for infiltration, it corresponds to outdoor air input data.
inCOCOventCOinCO
MMMd
dM,inf,,
,222
2
inCO
a
COreturnductsuaplyductexCO
a
COplyductexaventCO X
MM
MMMX
MM
MMMM ,,,sup,,sup,,, **** 2
2
2
2
2
inCO
a
CO
exfiltraoutCO
a
CO
aCO XMM
MMMX
MM
MMMM ,,,inf,inf, **** 2
2
2
2
2
tperoccupanCOoccoccCOinCO MnMM ,,, * 222
2
1 22
dd
dMM
inCOinCO
,,
a
COinainCO
MM
MMMC 2
2 *,,
12222 ,,,,, * inCOinCOinCOinCO XXCM
The “CO2 capacity” introduced in equation 10 is here supposed to correspond to the mass of the air
contained in the zone. A correction factor might be also introduced in order to take into account of
CO2 absorption in the building materials (walls and furniture).
2.1.2.3 Water balance
A second mass balance is made to compute the water content of the indoor air. The water flow rate
entering the zone is due to three main contributions (eq. 12):
- water brought by ventilation, positive infiltration and negative exfiltration air flow rates
(eq.16 and 17),
- water produced by the occupants or local humidification devices (eq.18),
- Water condensed by local cooling devices (eq. 18).
Water exchanges between air and walls or materials are modeled in the present software. A
correction factor (Fw,in) is here also applied to the air capacity; it’s supposed to take into account of
the effect of hygroscopic materials contained in the zone (Woloszyn, 1999).
inwwventwinw
MMMd
dM,inf,,
,
(2)
(3)
(4)
(5)
(6)
(11)
(7)
(8)
(9)
(10)
(12)
Page 16
2
1
dd
dMM
inwinw
,,
1,,, * inininwinw wwCM
aininwinw VFC **,,
inreturnductsuaplyductexplyductexaventw wMwMM ** ,,sup,sup,,,
inexfiltraoutaw wMwMM ** ,inf,inf,
coolingTUwheatingTUwoccwinw MMMM ,,,,
2.1.3 Wall thermal model
2.1.3.1 Parameterization
A 1st order “two-port network” model (Figure 5) is associated to each massive wall (Laret, 1980).
The wall network corresponds either to imposed temperature boundary conditions (called
“isothermal” conditions) or to null heat flow boundary conditions (called “adiabatic” conditions).
Figure 5: Two-port network
Isothermal conditions deal with external massive walls, surrounding the zone, including walls in
contact with “cold” neighbor zone, such as outdoor car parks. The adjective “cold” means that the
neighbor zone is not heated so that its temperature is related to the outdoor temperature.
Adiabatic conditions deal with indoor walls in contact with “warm” neighbor zones. The adjective
“warm” means that the neighbor zone (neighbor floor or room) is heated following a schedule similar
to the zone under study.
The parameters of each “two-port network” are tuned through a frequency analysis based on the
computation of the zone admittance matrix (Masy, 2006; Masy, 2008) and chosen to reproduce:
1) the global heat transfer coefficient of the wall,
2) the magnitudes of isothermal admittance and transmittance on a 24h time period, in
isothermal conditions,
3) The magnitude and angle of adiabatic admittance for a 24 hour period, in adiabatic
conditions.
In the case of massive opaque walls in contact with “cold” neighbor zones (as external walls),
isothermal conditions are used. The adjustment allows expressing the values of the three parameters
(Rin, Rout, and C) of the two-port network as fractions of the wall total thermal resistance and
capacity, through factors θ and φ (Figure 6). Factor θ is commonly called accessibility, though its
value decreases as the wall capacity becomes more accessible from the model indoor node. It gives
the position of the capacity on the whole wall resistance. Factor φ defines the proportion of the whole
wall capacity accessed in a 24h time period.
(13)
(14)
(16)
(17)
(18)
(15)
Page 17
Figure 6: Two-port network parameters - Isothermal conditions
In the case of massive walls in contact with neighbor “warm” zones, adiabatic conditions are used.
Considering the fact that temperature variations are similar on both side of the wall, a “null flow
plan” can be defined (Figure 7). This plan divides the wall into two different “two-port R-C
networks”. The parameter characterizes the position of this plan and determines the reparation of
the global resistance and capacity.
Figure 7: Two-port network parameters - Adiabatic conditions
The two resistances and the capacity of each “two-port network” can be expressed as fractions of the
wall total resistance and capacity using the three parameters: θ, φ and . The resistances “(1- θ)*R”
could be erased from the model, as there is no heat flow passing through them (Figure 8).
Figure 8: Reduced two port network - Adiabatic conditions
The adjustment process, the used R-C scheme and examples of use in cases of residential and office
buildings are detailed by Masy (2008).
In the case of walls totally included in the zone (“internal walls”) under study, the equivalent thermal
capacity is simply added to the indoor air thermal capacity (Ca,in).
For the most frequent wall compositions, the adjustment parameters stay in a limited range and
“standard” values are easily generated using the method previously presented. These values are then
used to tune the building model. Masy has shown that the use of standard values of parameters θ and
φ instead of exact values does not alter the quality of the results but simplifies greatly the
parameterization work for the final user (Appendix A: Walls Parameters Default Values).
Page 18
2.1.3.2 Solar gains and long wave exchange
In the case of an “isothermal wall” (massive opaque frontage or roof slab), the absorbed solar
radiation is injected on the outdoor surface node (Figure 6). Global radiative-convective heat transfer
coefficients are used to compute the global heat exchange trough the wall.
Figure 9: External wall - corrected outdoor temperature
A correction heat flow Iirh (Bliss, 1961) varying from 45 to 100 W/m², is also used to take into
account that the sky temperature is below the outdoor air temperature. The hourly values of this
correction heat flow are provided in lookup tables, as inputs for the model. This correction is
necessary because of the use of combined radiative-convective heat transfer coefficient. The effects
of solar and sky radiations are included in the corrected outdoor (“sol-air”) temperature *outt (eq. 19).
outsurf,skysunout
*
out R*QQtt
where,
outwall
outsurf,h*A
1R
and, outh = 17 to 23 [W/m².K] is the outside combined radiative-convective heat transfer coefficient.
A constant value of 23 W/m².K is considered in the model.
The correction heat flow skyQ is defined as follows:
irhwallir,wallsky I*ε*A*2
1Q and,
where, wallir , is the emissivity of the wall (generally around 0.9).
Solar radiation absorbed by the wall is computed as follows:
wallsunwallwallsun IAQ ,** ,
where, wall is the absorbance of the wall (generally around 0.6).
The incident heat flow due to solar radiation is the sum of three contributions: direct radiation,
ground reflected radiation and diffuse radiation. The global incident heat flow, Isun,wall, is calculated
as follows:
diffglobwallbeamwallsun IalbedoIII ***,,2
1
2
1
where,
walldiffglobwallbeam FIII *)(,
(19)
(20)
(21)
(22)
Page 19
and,
100
...F*%F*%F
easteastwall,southsouthwall,
wall
Fwall is the weighted average of the projection factors (Fsouth, Feast...) of all opaque walls facing
different orientations. The hourly values of these projections factors are pre-computed all along the
year as function of the incidence angles (for the latitude considered) and are provided as inputs for
the model.
2.1.3.3 Transient thermal transfer
So, the massive wall heat balance is governed by the following equations:
out
wallcout
outR
ttQ ,
*
insurfin
insurfwallc
inRR
ttQ
,
,,
outinwall
QQd
dU
2
1
dd
dUU
wallwall
1,,,* wallcwallcwallwall ttCU
The indoor surface temperature is computed by means of a supplemental heat balance, taking into
account the short wave solar radiation entering the zone through windows and reaching the indoor
surface of the wall (Figure 10, eq. 29).
Figure 10: Injection of short wave radiation on indoor walls surface
insurf
inainsurf
insurfR
ttQ
,
,,
,
A global radiative-convective heat transfer coefficient is used to compute the combined radiative-
convective indoor temperature ta,in. A constant value of 8 W/m².K for both vertical and horizontal
walls is considered in the model.
insurfwallrin QQQ ,,
To calculate this radiant heat flow, a simplified hypothesis is made: short wave solar heat gains are
supposed to be distributed all over the indoor surfaces proportionally to their respective areas (eq.
30).
(23)
(24)
(25)
(26)
(27)
(28)
(29)
Page 20
windowssun
totwall
wallwallr Q
A
AQ ,
,
, *
where windowssunQ , is computed as follow:
windowssunwindowswindowswindowssun IASFQ ,, **
The solar radiation incident to each window, Isun,windows, is computed in the same way as for massive
walls.
The same method is applied to compute solar heat gains reaching “adiabatic” wall. Each light wall
(windows...) is modeled with only one resistance (Figure 4), connected, on one side, to the indoor
temperature node and, on the other side, to a corrected outdoor temperature node, taking into account
of sky radiation effect.
2.1.4 Sensible heat gains
Except for the solar radiation transmitted through the windows which is “injected” to the indoor
surfaces, all sensible heat gains are injected to the indoor node (Figure 10) through the sensible heat
balance of equation 8. They include three contributions:
- sensible heat generated by electrical devices (lighting and appliances),
- sensible heat generated by occupants (function of the metabolic rate),
- Sensible heat generated or absorbed by heating or cooling terminal units.
This give :
coolingsheatingsappllightoccsins QQWWQQ ,,,,
where heatingsQ , and coolingsQ ,
are output variables of the terminal units models.
2.1.5 Ventilation, infiltration and exfiltration sensible enthalpy flow rates
The air leaving the zone (through ventilation exhaust and/or through exfiltration) is supposed to be at
indoor temperature (due to a perfect “mixing” inside the zone). Ventilation air temperature is given
as an output of the HVAC system model, while infiltrated air is at outdoor temperature.
Supply and exhaust state variables are used to define the CO2 (eq. 2), sensible enthalpy (eq. 33) and
water (eq. 16) flow rates carried by the ventilation and infiltration/exfiltration:
inaapreturnductsuaplyductexaapplyductexavents tcMtcMH ,,,,sup,,,sup,,, ****
inaapexfiltraoutapas tcMtcMH ,,,,inf,inf, ****
2.1.6 Validation
The validation of the building model presented hereunder has been realized by Bertagnolio et al.
(2008b) and Masy (2008).
Judkoff and Neymark (1995) have proposed a complete validation methodology for building
simulation models. It includes analytical, empirical and comparative tests.
The model was tested on the experimental results provided by EMPA test cell (Figure 11) in the
framework of IEA-ECBCS annex 43 (Judkoff, 2007) research project. The cell is composed of tight
insulated steel sandwich boards and the window is removed. The environment is controlled so that
outdoor temperature is known. Internal heat flows are given, as well as corresponding indoor
temperatures.
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(31)
(32)
(33)
(34)
Page 21
Figure 11: EMPA test cell (2.36 m x 2.85 m x 4.63 m)
The indoor temperatures computed by the model for imposed heat flows are compared to measured
indoor temperatures (Figure 12). The RMS of the error related to indoor temperature equals 0.46 K.
Figure 12: Comparison of measured (dotted black line) and computed (red line) indoor temperatures
The model is also tested by comparison with a more detailed model based on a convolution process
using heat transfer functions. The comparison is performed on an office room defined in the
framework of IEA-SHC Task 27 (Köhl, 2005) research project. Masy (2008) has already detailed the
results of this analytical validation of the building zone model.
As mentioned above, analytical and experimental tests have been made by comparing the RC
network model to the transfer function method and to experimental results.
However, comparative testing is very useful to identify the main differences existing among the
simplified model and some reference detailed codes.
The BESTEST procedure is a comparison method used to evaluate building simulation models. This
“comparative testing” approach is based on the use of benchmark test cases, generated with reference
codes (BLAST, DOE-2, ESP, SRES/SUNCODE, SERIRES, S3PAS, TASE and TRNSYS). These
reference models do not necessarily represent “truth” but are representative of what is commonly
accepted as the current state-of-the-art in building energy simulation. (Judkoff and Neymark, 1995).
Indeed, the aim of BESTEST procedure is to identify errors in the models and to guarantee
consistency between them.
Two types of test-cases are proposed in BESTEST procedure:
- Diagnostic cases, attempting to isolate the effects of individual algorithm by varying
parameters one by one,
- Qualification cases, representing a set of realistic lightweight and heavyweight buildings.
Only the results dealing with the qualification cases are presented hereafter.
Page 22
Figure 13: BESTEST - test cell
The basis of the different test cases is a rectangular room (Figure 13). The envelope characteristics,
orientation of windows and temperature set points vary between the different test cases.
The main characteristics of the test cell are given in Table 1 for the base test-case (C600).
Dimensions 8 x 6 x 2.7 m
Walls Uwall = 0.514 w/m².k; c = 14534 j/m².k (indoor insulation)
Roof Uroof = 0.318 w/m².k; c = 18170 j/m².k
Floor Ufloor = 0.039 w/m².k; ground coupling neglected
Windows 2 x 6 m²; south oriented;
Uwindow = 3 w/m².k ; solar factor = 0.78
Infiltration 0.5 vol/h
Internal gains 200 w (60% rad., 40% conv.)
System Perfect unlimited system; 100% convective heating and cooling. Table 1: Test cell characteristics - C600
Basing on the C600 test-case, several variations are proposed. The main qualification cases are
described in Table 2. All the proposed cases have not been considered because of the limitations of
the model. Test-cases involving solar shading and equal cooling and heating set points have not been
applied.
The meteorological data used for the simulations correspond to a cold winter (min. outdoor
temperature: -24.4°C) and a dry and hot summer (max. outdoor temperature: 35°C).
C600 Base case
C620 C600 with one window East oriented
and one window West oriented
C640 C600 with night set back to 10°C
between 23:00 and 7:00
C900 - 940 C600 - 640 with heavy walls (outdoor
insulation) Table 2: Test cases
The integrated annual heating and cooling demands are shown, respectively, in Figure 14 and Figure
15. The building model (“EES”) provides good results for the cases 600 to 640. Some little
discrepancies appear when looking at heavy-weight cases (C900 to 940).
These errors could be explained by the limited order of the wall model. An accurate estimation of the
interaction between indoor ambient and heavy outdoor insulated walls would require the use of a
more detailed wall model, using more than one thermal mass. However, these errors stay limited
(max. 10%) and the results stay in good accordance with those provided by more detailed models.
Page 23
Figure 14: C600 to C940 – Annual Heating Demands
Figure 15: C600 to C940 – Annual Cooling Demands
The differences could also be partially explained by the use of an imperfect control for the
heating/cooling system. Indeed, the heating and cooling powers must be controlled by means of
proportional control laws to maintain the indoor set-point.
Some hourly data are also provided for comparison. These data correspond to the 4th
January of the
simulated year. Calculated hourly values of heating/cooling powers are shown for C600 and C900,
respectively, in Figure 16 and Figure 17. Heating power is plotted in positive values while cooling
power is plotted in negative values.
The simplified model provides consistent results. For the lightweight case, the calculated heating and
cooling powers curves are superposed with those provided by the reference models.
As expected, for heavy inertia walls, the results are less good and, even if the global shape of the
curve is respected, the superposition is not perfect. As mentioned above, this discrepancy could be
explained by the low order of the wall model.
Figure 16: C600 – 4th January, H/C powers
Page 24
Figure 17: C900 – 4th January, H/C powers
BESTEST procedure provides also hourly values for cases C600 and C900 with floating
temperature. The temperature profiles generated by the model for the test case C900 are shown in
Figure 18. Once again, the results provided by the simplified models are in good accordance with
those provided by reference models.
Figure 18: C900 – 4th January, Floating temperature
The model provides results included in the range, rather close to the high limit, generated by more
sophisticated models. The effects induced by inputs and parameters variations are of the same order
of magnitude as for the other building codes.
This comparative validation shows that the developed simplified building model predicts cooling and
heating demands with sufficient accuracy. This simplified model is well adapted to simplified
building and system simulation tools.
Page 25
2.2 HVAC System Modeling
Two different modeling levels are distinguished for the HVAC system (Adam et al., 2006b; Lebrun
et al., 2006a):
- So-called “mother” (“first principle”, or “mechanistic”) models, containing all the (present)
understanding of the physical phenomena, are used as references;
- So-called “daughter” (“simplified” and very often polynomial) model, generated with help of
the help of the previous ones, are preferred to simulate large system on long time periods.
“Mother” models are preferred in some other domains of use, as, for instance, to support
experimental work (design of the experiment and analysis of experimental results) or to characterize
some specific equipment. “Daughter” models only are used in BENCHMARK and SIMAUDIT.
BENCHMARK and SIMAUDIT mainly differ concerning the HVAC system, which is “ideal” in
Benchmark (Allowing air quality, humidity and temperature control) and characterized by the
following functionalities:
Hygienic ventilation rate
Humidity Control in AHU
Temperature Control by TUs (similar to fan coils)
HVAC system parameters are automatically computed trough « sizing calculation »
With the same functionalities, SIMAUDIT is able to take into account a larger range of available
HVAC components and control strategies.
2.2.1 BENCHMARK: Basic “Reference” HVAC System
The system model actually available includes most of the classical HVAC components currently
used (fans, air-to-air static recovery systems, coils, fan coils, pumps…).
Considering that the building model is a mono-zone model, most of components (AHUs, TUs,
pumps…) are aggregated into “global” components. The different locations of the terminal units and
of the air diffusers are not yet considered.
2.2.1.1 Air Handling Unit
The AHU considered may include a return fan, a return filter, a recovery system, a supply filter, a
preheating coil, an adiabatic humidifier, a cooling coil, a post heating coil, a main fan and a steam,
humidifier (Figure 19). Of course, these components are usually not included all together (for
example, both adiabatic and steam humidifiers don’t have to be selected at same time).
In “Benchmark”, the CAV AHU is supposed to provide a constant hygienic flow rate to the zone and
to ensure humidity control (by humidification and/or dehumidification). Temperature control is then
ensured by terminal units, as described hereafter.
Page 26
Figure 19: Typical double flux AHU
2.2.1.1.1. Fans
The different pressure drops of the components are taken into account to compute the fans
consumptions and the corresponding air heating-up.
The equations related to the return (or extraction) fan give, for instance:
returnfans
returnfanreturnfanreturnfan
PVW
,
*
where,
eryreturnreomizerreturneconerreturnfiltreturnductreturnfan PPPPP cov
and,
returnfana
returnfanreturnfansuareturnfanexa
C
Wtt
,
,,,,
Similar equations are used to model the main supply fan.
2.2.1.1.2. Heating Coils
Heating coils are simulated by using a simplified ε-NTU model. In the classical ε-NTU model, the
simultaneous calculations of air and water evolutions would consume more computational time. The
simplification proposed here consists in not taking into account what happens on the water side; the
heating coil is characterized by its air-side effectiveness only.
When the control valve is fully open and for a constant air flow rate, the air-side effectiveness is
expressed as:
a
lheatingcoilheatingcoiaC
C
min
, *
For a constant air flow rate, the maximal air exhaust temperature ta,ex,heatingcoil,max is supposed to be
reached when the valve is fully open:
(35)
(36)
(37)
Page 27
lheatingcoisualheatingcoisuwlheatingcoialheatingcoisualheatingcoiexa tttt ,,,,,,,max,,, *
The control variable is then used to compute the required exhaust temperature ta,ex,heatingcoil:
lheatingcoisualheatingcoiexalheatingcoilheatingcoisualheatingcoiexa ttXtt ,,max,,,,,,, *
where X is the control variable. The heating power actually provided by the coil is then computed:
)(* ,,,,, lheatingcoisualheatingcoiexalheatingcoialheatingcoi ttCQ
This simplification allows avoiding the computation of the thermal exchange on water side. Only the
characteristics of the coil in nominal regime are required to compute the air-side effectiveness.
2.2.1.1.3. Cooling coil
Lemort et al. (2008) have used the same approach to build a simplified cooling coil model. They
propose to do as if the cooling coil exhaust air temperature was controlled by its contact temperature
(in place of the refrigerant flow rate). This control is described by equation 41.
The exhaust air temperature is first compared to its set point. The control variable (comprised
between 0 and 1) is then used to adjust the contact temperature.
min,,,,,,,, . coilccoilsuacontrolcoilcoilsuacoilc ttXtt
For given supply air temperature, contact effectiveness and contact temperature, the exhaust air
temperature can be defined as follows:
coilccoilsuacoilccoilsuacoilexa tttt ,,,,,,,, .
The coil contact effectiveness is defined by the equation 43.
)exp(1 ,,,, wetcoilcwetcoilc NTU
with
coilacoila
wetcoilcCR
NTU,,
,,.
1
This means that (as Ra and aC ), the contact effectiveness is only depending on the air flow rate. The
exhaust air humidity is determined by equation 45.
wetcoilccoilsucoilccoilsucoilex WWMAXWW ,,,,,, .,0
Once the exhaust air temperature and humidity are known, the sensible and latent heat outputs of the
coil can be calculated. The minimal value of the contact temperature (corresponding to the full
opening of the control valve) must be determined as a function of both supply temperatures: the
temperature of the refrigerant and the (dry bulb or wet bulb) temperature of the air (according to the
regime: dry or wet):
(38)
(40)
(41)
(42)
(43)
(44)
(45)
(39)
Page 28
).( ,,,,
,
,,
,,min,,, coilsurcoilsua
coilc
drycoila
coilsuadrycoilc tttt
).( ,,,,
,
,,
,,min,,, coilsurcoilsuwb
coilc
wetcoila
coilsuwbwetcoilc tttt
In these equations, εa,coil,dry and εa,coil,wet are the “air-side” effectiveness’s, defined in dry and wet
regimes respectively. In each regime, the “air side” effectiveness is related to the actual coil
effectiveness:
drya
drycoil
drycoildrycoilaC
C
,
,min,
,,, .
fa
wetcoil
wetcoilwetcoilaC
C
,
,min,
,,, .
with,
coilfapcoilafa cMC ,,,,, *
The fictitious specific heat, cp,af is defined by the equation 51 (Lebrun et al., 1990).
wetcoilexwbcoilsuwb
wetcoilexacoilsua
coilfapTT
hhc
,,,,,
,,,,,
,,,
These effectiveness’s are defined with the valve fully open. If the air flow rate is varying, its effect
has to be pre-identified with the help of the reference model. The coil is supposed to work in dry
regime, if the dew point temperature at cooling coil supply is lower than the minimum contact
temperature. If not, it is supposed to be in wet regime:
If wetcoilccoilsudp tt min,,,,, ,
drycoilccoilc tt min,,,min,,
If wetcoilccoilsudp tt min,,,,, ,
wetcoilccoilc tt min,,,min,,
Lemort et al. (2008) have shown that the agreement between results of both reference and simplified
models is very good. A comparative test between the reference and the simplified models proofs that
the simplified model can be used without any significant loss of accuracy on the cooling load
calculation. Moreover the calculation time dramatically reduced and no numerical instability is
encountered.
2.2.1.1.4. Humidifiers
Both adiabatic and steam humidification are modeled in the present simulation tool.
Adiabatic humidification is supposed to be controlled by means of the preheating coil and is modeled
by the following equations:
adiabhumsuadiabhumtwbsadiabhumadiabhumsuadiabhumex wwww ,,,,, *
(46)
(47)
(48)
(49)
(50)
(51)
(52)
Page 29
adiabhumsuaadiabhumsuwbadiabhumadiabhumsuaadiabhumexa tttt ,,,,,,,, *
Steam humidification is modeled by means of water and energy balances:
steamhuma
steamhumsusteam
steamhumsusteamhumexM
Mww
,
,,
,,
steamhumsusteam
steamhuma
steamhumsusteam
steamhumsuasteamhumexa hM
Mwh ,,
,
,,
,,,, *
The exhaust air humidity ratio is then calculated by using the following control law :
max,,,, * steamhumsteamhumsteamhumexsteamhumex wXww 0
With Xsteamhum as control variable (see hereafter)
2.2.1.2 Terminal Units
In Benchmark, room sensible heating and cooling are ensured by a classical fan coil unit.
In first and simple approach, this unit is characterized by its heat transfer coefficient only. The fan
electrical power is defined as a percentage of the nominal heating/cooling power. The main outputs
of this model are the heating/cooling power actually delivered and the related fan consumption.
In heating mode, for example, the heating power delivered by the fan coil is defined as follow:
)(** ,,,,,,, inaFCUheatingsuwFCUheatingFCUheatingFCUheating ttKXQ
Kheating,FCU is the equivalent heat transfer coefficient of the fan coil unit:
min, *CK FCUheating
This term is defined for the nominal water flow rate (valve fully open) and for the fan rotation speed
considered (usually selected by the occupant). Xheating,FCU is the control variable (this control is
supposed to consist in a tuning of the water flow rate).
The cooling power is defined in the same way. Possible water condensation inside the fan coil is not
yet taken into account in Benchmark, but it could be easily included by using a model similar to the
one used for the cooling coil of the Air Handling Unit.
2.2.1.3 Hot and Cold Water Distribution
Water networks are modeled in such a way to take both pressure drops and heat exchanges into
account. In Benchmark, a first approximation consists in characterizing each water distribution
network with only one equivalent thermal efficiency.
Equivalent nominal water flow rates are defined on the basis of nominal heating and cooling powers
and of nominal temperature variations. For the hot water distribution, for example:
nntheatingplaww
nntheatingpla
strhotwaterdiwtc
QM
,,
,
,*
(53)
(54)
(55)
(56)
(57)
(58)
(59)
Page 30
The so-defined “primary” water flow rate is kept constant in the simulation.
Both heating-up through the pump and heat exchanges along the distribution network are taken into
account. For hot water distribution, for example, this gives:
wnstrhotwaterdiw
lossstrhotwaterdimphotwaterpush
sthotwaterdisuwstrhotwaterdiexwcM
QWtt
*,,
,,
,,,,
andheatingdemstrhotwaterdilossstrhotwaterdi QQ *, 1
The primary water pump consumption is defined as follows:
mphotwaterpushsw
strhotwaterdiw
nstrhotwaterdiwmphotwaterpush
PMW
,,
,
,,,*
*
mphotwaterpu
mphotwaterpush
mphotwaterpu
WW
,
Similar equations are used to model the chilled water network and its pump.
2.2.1.4 Heat Production
In the present version of Benchmark, heat is supposed to be produced by a classical fuel-oil boiler
with ON/OFF control.
The “daughter” boiler model used here is derived from the reference (“mother”) model developed by
Bourdouxhe et al. in the ASHRAE HVAC1 Toolkit (Bourdouxhe et al., 1999).
In the mother model, the boiler is represented by an assembly of a supposed to be adiabatic
combustion chamber and two (gas-water and water-environment) heat exchangers (Figure 20).
At constant water supply temperature, the fuel oil boiler submitted to ON/OFF control has a very
linear behavior as shown in Figure 21. This suggests that a double linear correlation can be used as
simplified model. Consumed power is plotted as function of useful power and a linear fit is made on
this curve.
Figure 20: Boiler model block diagram
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(61)
(62)
(63)
Page 31
Figure 21: Boiler consumption as function of its useful thermal power at constant water supply temperature
(ON/OFF control)
Applying the same methodology to the same boiler for different values of water supply temperature
(e.g. from 50°C to 80°C) , and using reduced variables, the following correlation is established (eq.
66).
onnu
uredu
Q
,,
,
onnc
credc
Q
,,
,
reduredc qCCq ,, * 10
The values of C0 and C1 can be correlated with the water supply temperature as follows:
boilersuwtCCC ,,,, *10000
boilersuwtCCC ,,,, *11011
If there is no mixing valve or if it’s fully open, the boiler water supply temperature corresponds to
the heating plant return water temperature:
wstrhotwaterdiw
andheatingdem
strhotwaterdiexwntheatingplareturnwcM
Qtt
*,
,,,,
In Benchmark, the boiler is only submitted to weather control: the exhaust water temperature set
point is fixed according to the outdoor temperature.
Of course, it may occur that this set point stay above the maximal temperature achievable; in such
case, the boiler is running in full load.
2.2.1.5 Cold Production
For the chiller, various simplified “daughter” models are available (Lebrun et al., 2006a). The
simplest one consists in expressing the chiller “capacity” (full load cooling power) and the
corresponding electrical consumption as polynomial functions of two independent variables:
1) the temperature of the secondary fluid supplying (or leaving) the evaporator
2) the temperature of the secondary fluid supplying the condenser (Figure 22).
(64)
(65)
(66)
(67)
(68)
(69)
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The part load regime can be described with the help of Heating, Cooling and Electrical load factors
(eq. 70, 71 and 72):
,maxcd
cd
Q
QHLF
max,ev
ev
Q
QCLF
max,el
el
W
WELF
Figure 22: Cooling capacity as function of secondary fluids temperatures
Simple polynomial laws can also be used to correlate electrical and cooling load factors to each other
(Figure 23).
Figure 23: Possible relationship between electrical and thermal load factors
A better, but still very simple, approach consists in using the evaporation and condensation
temperatures (in place of the secondary fluid temperatures) in the two first polynomial laws (which
then concern the compressor and the refrigeration cycle only). Separate semi-isothermal heat
exchanger models can then be used to deal with the evaporator and with the condenser. The
definitions of the part load factors stay the same as for the first model. Till now, the first approach is
preferred in Benchmark.
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In ideal control conditions, the simulation will determine the chilled water supply temperatures
“required” by both air handling and terminal units. These temperatures will be generated by the (real
or fictitious) proportional controls of both units. The minimum between these two temperatures will
become the set point of the chiller control.
Of course, it may occur that this set point stay below the minimal temperature achievable, in such
case, the chiller is running in full load.
2.2.1.6 HVAC System Control
When having to deal with a complex system simulation, a good engineer approach consists in
starting with idealistic hypotheses and going progressively to more realism, according to what is
looked for. Ideal control allows using simpler simulation models gives easier access to benchmarks
and indicates clearly the maximal performances that could be reached.
A simple proportional control laws is used for each component, with a non dimensional control
variable Xcontrol, varying between 0 and 1 in proportion of the difference observed between the set
point and the controlled variable:
)))(*,(,( int ttCMAXMINX tsepocontrolcontrol 01
The control “gain”, Ccontrol, is arbitrarily fixed as a realistic compromise between accuracy and
robustness.
2.2.2 SIMAUDIT: Range of Common HVAC Systems
To be continued…
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3 Implementation and Interface
3.1 Implementation
The presented model is implemented on an Engineering equation solver (Klein, 2008). This
implementation ensures also a full transparency for the user and makes easier the continuous
improvement and development of the tools. Indeed, model’s equations are directly readable and easy
to modify by any user (Figure 24). It is also very easy to develop additional HVAC components
models and to connect them to the existing ones. Moreover, the present equation solver is very well
adapted to solve differential equations systems as used to model the thermal behavior of the building
zone.
Of course, the use of an equation solver to solve complex equation systems implies longer
computation time than other simulation software’s, but the continuous increase of computer
performances tends to reduce this inconvenience. At present time, about 20 minutes are necessary to
simulate a mono-zone building and its complete HVAC system (including AHU, terminal units and
heat and cold production and distribution) hour by hour on one year with a classical computer
equipped with a 2.00GHz processor.
Figure 24: Formatted equations as appearing in the software
Page 35
3.2 BENCHMARK: Inputs/Outputs/Parameters
“BENCHMARK” is used to compute the "theoretical" (or « reference ») consumptions of the
building, supposed to be equipped with a “typical” HVAC system allowing air quality, temperature
and humidity control. The building is seen as a unique zone, described by much reduced number of
parameters. The use of this first simulation tool should help the auditor in getting, a very first
impression about the actual energy efficiency of the considered system.
The ventilation rate is supposed to be the hygienic ventilation rate. Ventilation and humidity control
are ensured by the AHU (equipped with recovery system or not). Temperature control in the building
is ensured by terminal units (supposed to be similar to fan coil units).
Lists of inputs, parameters and outputs are given in appendix B, C and D, respectively.
3.2.1 Outputs
The main outputs of the tool are given in Figure 25.Monthly fuel and electricity consumptions are
given in child windows. Hourly values of temperatures, electric powers or heat flows can also be
easily plotted.
Figure 25: Outputs panel
Page 36
3.2.2 Inputs
The main inputs of the model are weather and climate data. Hourly values of outdoor temperature,
relative humidity, diffuse and global radiation are provided in lookup tables. Other inputs, as
correction sky radiation heat flow and projection factors are also provided in lookup tables. The first
table “sun_data” include all the projection factors used to compute direct solar radiation. The other
table, “weather_data” include hourly values of temperature, humidity, solar radiation and correction
sky radiation.
A library of weather data files will be provided with the tools. A procedure to implement additional
weather data files will also be given.
3.2.3 Parameters
In Benchmark, the parameters asked to the user have to be provided by means of control panels. A
main control panel (Figure 26) is used to access the other ones.
Figure 26: Main control panel (BENCHMARK v1.2.)
The simulation control panel (Figure 27) is used to fix the simulation time and to choose between the
default location (Brussels) and the location of the user. When selecting “user”, the software uses data
given in tables “sun_data_user” and “weather_data_user” generated with the “Climate Data Tables
Generation” tool.
Page 37
Figure 27: Simulation parameters control panel
The “Building description” panel (Figure 28) is used to fix the geometry of the building and the
internal gains (occupancy, lighting and appliances). Basing on the values, given in appendix A, the
characteristics of the envelope have to be fixed in the lookup table “walls_data” (Figure 29).
Figure 28: Building description control panel
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Figure 29: Lookup table "walls_data"
The “HVAC System Description” panel (Figure 30) is used to access the control panels related to
each HVAC components (AHU, TU, plant...).
Figure 30: HVAC System main control panel
The “Air Handling Unit” panel (Figure 31) is used to select the main characteristics of this
subsystem (existence of a mechanical ventilation, hygienic ventilation flow rate, type of
humidification system, water nominal temperature regimes and nominal fan efficiencies).
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Figure 31: AHU control panel
Typical values of AHU components pressure drops and performance are provided in PrEN13779
(2007) and PrEN13053 (2003).
Figure 32: AHU components pressure drops (source: PrEN13779)
The temperature and humidity set points and the control laws can be modified in the “HVAC System
Control” panel (Figure 33). Four heating and cooling set points have to be defined for occupancy and
non-occupancy hours. HVAC components (AHU coils and TUs) are controlled with proportional
control laws. For each control strategy, a set point (temperature or humidity) and a control gain (C)
are asked. Maximal values of outdoor temperature (tout,max) are defined to reduce the field of
intervention of the different heating/cooling/humidifying/dehumidifying systems.
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Figure 33: HVAC system regulation control panel
3.3 SIMAUDIT: Inputs/Outputs/Parameters
To be continued…
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4 Example of Application
An application of the developed simulation tools to a Belgian case study (Bertagnolio et al., 2008c)
has been presented in ICEBO 2008 conference (International Conference for Enhanced Building
Operations) and is shown hereunder.
4.1 Case study description
4.1.1 Building design
The considered building is an existing medium-size office building (around 26700 m² of air-
conditioned floor area), erected in Brussels at the end of the sixties (Lebrun et al., 2006b). The
building is composed of three blocks, has a “H” shape (Figure 34) and is North-South oriented. Eight
storeys of the building include landscaped offices and meeting rooms. The five underground levels
are dedicated to cars parking.
Figure 34: Case study building (left) and envelope module (right)
The frontages of the lobby are made of single-glazed windows. The rest of building envelope is made
of about 1000 double-glazed modules, equipped with external solar protection (Figure 34). The main
characteristics of the envelope are given in Table 3.
Case Study Building
Conditioned floor area [m²] 26700
Number of storey’s [-] 9
Building height [m] 30
Main orientation - N/ S
Geographical location - Brussels
External walls area [m²] 7090
Number of occupants [occ] 1100
Lighting power [W/m²] 15
Appliances power [W/m²] 20
Opaque frontages area [m²] 2570
Opaque frontages U
value
[W/m²-
K] 1.15
Roof deck area [m²] 2970
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Roof deck U value [W/m²-
K] 0.32
Double-Glazed
frontages area [m²] 3020
Double-Glazed
windows U value
[W/m²-
K] 3.6
Single-Glazed frontages
area [m²] 1440
Single-Glazed windows
U value
[W/m²-
K] 5.8
Table 3: Main characteristics of the building
4.1.2 HVAC system
About 1000 four-pipes heating and cooling induction units are installed in the offices. The CAV Air
Handling Units provide together a total of about 190000 m³/h of fresh air per hour, 75 hours per
week, to the conditioned zones (from level 0 to 8). Vitiated air is rejected in the underground storeys
to ensure ventilation of the parking. This ventilation flow rate corresponds to about 2.4 air renewals
per hour. According to the weather conditions, the supplied air can be heated and adiabatically
humidified, or cooled and dehumidified.
Heat production is ensured by four fuel-oil boilers, giving together a nominal heating capacity of
about 4 MW. Chilled water production is ensured by four water cooled chillers, coupled to two
cooling towers, giving a total cooling capacity of about 2.1MW.
4.1.3 Occupancy and operating profiles
The building is occupied by about 1100 people, between 8 am and 18 pm, 5 days per week. The
ventilation is maintained approximately 75 hours per week. Electrical appliances and artificial
lighting are switched on between 7 am and 21 pm from Monday to Friday. Temperature and
humidity setpoints are maintained between 8 am and 20 pm. The operating and occupancy profiles
are summarized in Figure 35.
Figure 35: Typical day operating profiles
4.1.4 Nominal heat losses
Using the information given in Table 3, the global heat transfer coefficient of the whole envelope can
be estimated as follows:
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Another heat transfer coefficient corresponds to the mechanical ventilation:
In nominal heating conditions (outdoor : -10°C/RH 90%; indoor : 20°C/RH50%), with ∆t = 30 K,
this gives a sensible power demand of:
If having to maintain 50% of relative humidity at 20 °C in the same nominal conditions the
corresponding latent heat demand would be of about:
i.e. 920 kW in the present case; this would bring the total heat demand around 3.52 MW. This very
rough estimate of the total heat demand can be compared to the power installed in the heating plant
(around 4 MW): the agreement is not bad and there seems to remain a fair reserve of heating power.
4.1.5 Nominal heat gains
The envelope and ventilation heat transfer coefficients identified in heating mode could also be used
for heat gains calculations. In nominal cooling conditions (outdoor : 30°C/RH 50%; indoor :
25°C/RH50%), with ∆t = 5 K, the sensible cooling power demand is estimated to 430 kW.
Other sensible heat gains are coming from sunshine, occupants (sensible) metabolism and electricity
consumed inside the building.
Solar heat gains are here very reduced, thanks to efficient external solar protection. They are
estimated to no more than 50 W/m2 of window area, i.e., for the whole glazed area (4 460 m2),
around 220 kW.
The sensible metabolism of 1100 occupants is also a limited contribution and is of about 80 kW. The
electricity consumed inside the building dominates this sensible heat balance and is estimated to
about 800 kW. Consequently, the total sensible heat gains were estimated to 1.53 MW.
The latent cooling demand associated to air drying had to be added. This term is estimated to:
i.e. 540 kW, bringing the total cooling demand around 2.07 MW. A satisfactory agreement is found
between this rough estimation and the cooling power actually installed in the building (2.1 MW).
4.1.6 Control strategy
The building is equipped with a classical BEMS with two levels: a set of local control units and a PC
for supervisory management.
For all the office zones, the dry bulb temperatures are controlled in feedback, thanks to about 500
double thermostatic valves: these valves are modulating the water flow rates supplying the heating
and cooling coils of the induction units.
The air humidity control is achieved in the AHU, thanks to a (on/off) control of the pump supplying
the adiabatic humidifier and to a modulating valve supplying the cooling coil. There is also a feed
back control of the mixing valve supplying the pre-heating coil; the AHU exhaust air temperature set
Page 44
point is displaced in relationship with the outside air temperature, in such a way to make the
adiabatic humidification possible, when required, and also to bring some sensible heating or cooling
to the zone, when required. The primary air is only supplied during pre-heating and occupancy time.
Out of that time, if the weather is very cold, the induction units are still used in free convection
mode, by supplying hot water to the heating coils. The re-starting time of the installation in the
morning is fixed by the BEMS, according to weather conditions and to the week day. In average, the
total running time is of about 75 h/week. The chilled water temperature regime is 6/12 °C in nominal
conditions.
4.1.7 Fans and air distribution network
The as-built files (completed by a quick inspection) give a fair estimate of all ventilation airflow
rates. According to that information, the total fresh airflow rate supplied in the whole building is of
about 170 m³/h per occupant, when the building is fully occupied. But this fresh air is supplied on 75
h per week, although the building is fully occupied 50 h per week, only. This means that, in average,
the building is supplied with an average rate of 255 m³/h per person!
The energy impact of such generous ventilation is triple:
1. It increases the sensible and latent energy consumed to bring the fresh air to required
temperature and water content;
2. It increases the fans consumptions;
3. It increases the internal loads, almost in proportion to the fans consumptions.
By supposing a global fan efficiency of about 75%, the total electricity demand of the fans is of the
order of 214 kW, i.e. of about 8 W/ m2.
4.2 Recorded data analysis
Monthly records of fuel consumptions are available from 1971 to 2006 and monthly records of
electricity consumption are available from November 2004 to February 2006. The records made
from November 2004 to February 2006 are plotted in Figure 36.
Figure 36: Electricity and Fuel Consumptions
The average electricity consumption of this period is floating around 520 MWh/month ± 10 %. No
seasonal variation is noted. The fuel oil energy consumption shows much larger variations, around an
average of 440 MWh/month.
Figure 37 shows the distribution of fuel oil power defined in montly average (from January 2004 to
March 2006), as function of the external dry bulb temperature. This thermal signature allows to
identify a meaningful linear regression. The plotted linear regression shows that the heating demand
tends to zero when the outside temperature is around 23°C. The slope of this law (about 50 kW/K)
Page 45
should be of the same order of magnitude as the average heat transfer coefficient of the building. In
order to calculate an average heat transfer coefficient, it is necessary to take the ventilation
intermittency into account. This gives:
Both values are in good agreement and confirm that the fuel consumption is quite well explained.
Theoretically, it should have been a little higher, because of the latent heat power consumed to
humidify the air which is not taken into account in this average heat transfer coefficient. The
remaining error found in this building “signature” is very probably due to the effect of the inside
temperature control (the temperature inside the building is “sliding” down slowly with the outside
temperature).
Figure 37: Building Thermal Signature
The dispersion of the recorded points and the poor correlation coefficient (R²=66%) are explained by
the fact that the fuel consumption is not only influenced by the outdoor temperature but also by other
parameters. The other influences could be:
- The solar gains;
- The heterogeneous use of HVAC equipment (due to variable comfort requirements, variable
occupancy rate, variable internal loads,…);
However, considering the fact that the building is equipped with very efficient solar protections, the
first influence can be neglected and the discrepancies in the recorded data should be due to the
variable occupancy, variable internal loads associated and variable way of using the HVAC system.
The interpretation of the electrical consumption is much more difficult. A very first step is to
distinguish the peak hours and off-peak hours consumptions. But both orders of magnitude are very
high: they correspond to 22 and 34 W/m2 of conditioned floor area! The difference between these
two power levels can be explained by a much higher rate of use of the HVAC system during peak
hours. Indeed, the peak supplement is of the same order of magnitude as the total fan power (about 8
W/m²), which should be the most important contribution in the HVAC electrical consumption.
The very high electricity consumptions are probably (but not completely) due to the computation and
lighting equipements. As seasonal variations are insignificant, it is not possible to identify the impact
of the chillers. More detailed records would be required to go further in this analysis: hourly records
and/or separate records for HVAC and non-HVAC consumptions.
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4.3 Use of simulation tools
It has been decided to compare the computed fuel and electricity consumptions to averaged data
because of the following reasons:
The very heterogeneous use of the HVAC equipment and the important dispersion of the
recorded data (Figure 37) make the calibration to one-year data very difficult and arbitrary;
There are some uncertainties in the fuel consumption recording schedule.
The use of energy records averaged on up to 35 years should make the consumption profiles
smoother, hide the heterogeneities in the building operation an tend to minimize the errors due to the
use of a typical weather data set. Indeed, actual, recent and complete weather data are not available
in the present case and typical hourly weather data are used all along this analysis. So, the aim of the
calibration performed here is not to represent the behavior of the building for a given year but in
average.
4.3.1 Benchmarking
The first software presented above (BENCHMARK) is applied to the nine conditioned storeys of the
building (from level 0 to 8), simulated as a unique zone. Parking zone is not considered in the study.
The actual characteristics of the building envelope, the estimated internal generated gains, the actual
occupancy schedules and temperature and humidity setpoints are entered in the software. The first
approach consists in supposing that the studied building is coupled to a “typical” HVAC system
(very different of the actual one) ensuring efficient air quality, temperature and humidity control. The
performances of this system (pressure drops and HVAC components efficiencies) are estimated
according to the standards prEN 13053 and 13779. The constant hygienic ventilation flow rate is
fixed to 45 m³/h of fresh air per hour and per occupant; the AHU is supposed to be equipped with a
classical cross-flow heat recovery system.
It appears that monthly computed and measured consumptions are very different. Mostly the fuel, but
also the electricity consumptions are largely underestimated by the software. This suggests the
existence of important energy savings potentials.
Figure 38: Measured and computed fuel consumptions (first run)
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Figure 39: Measured and computed electricity consumptions (first run)
4.3.2 Calibration and analysis
In this second phase of the study, the second tool (SIMAUDIT) has to be used. This second allows to
simulate the behaviour of the actual installation with more accuracy.
With more realistic hypotheses (much higher ventilation flow rate) and a description of the actual
HVAC system (four-pipes induction units, no heat recovery system,...) and its performances and
operating conditions, the software gives the results shown in Figure 40 and Figure 41.
Figure 40: Recorded and computed monthly fuel consumptions (calibrated model)
Figure 41: Recorded and computed monthly electricity consumptions (calibrated model)
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To simplify the parametrization and the calibration works, the mono-zone model, previously used in
“BENCHMARK” is also used in the second part of the study. The results obtained with this
simplifed global model should surely be improved by using the multi-zone capability of the tool.
The calibration method used here is totally heuristic and based on practical guidelines. The computed
and recorded datawill be compared in terms of Mean Bias Error (MBE) and coefficient of variation
of Root Mean Squared Error (CV(RMSE))
During the first step, the tuning of the parameters is based on as-built data and on the analysis of
recorded data. In constrast with the benchmarking made before, this consists in implementing:
The actual primary and secondary HVAC components;
The actual setpoints and occupancy and operating schedules;
The design air renewal rate;
Standard values coming from prEN standards and rule of thumb values are used for the undetermined
HVAC components characteristics (as pump, fans and plant performances) and for the plug and
lighting loads. At the end of this first step, it appears that the electricity consumption is largely
underestimated and has to be re-evaluated. Even if the fuel consumption seems to be well estimated,
the calibration of the whole system (building and HVAC system) has to be continued. Indeed, both
electricity and fuel consumptions are intrinsincally linked and cannot be studied separatly.
During the second step, plug and lighting loads are adjusted basing on observations made during site
visits and interviews. As expected, the electricity consumption increases and the fuel consumption
decreases because of the increasing of density of lighting and plug loads (Erreur ! Source du renvoi
introuvable.). At this step of the calibration work, the shapes of the consumption profiles are similar
to the recorded ones but a “scale” difference remains.
Table 4: Calibration - simulation runs
RUN # ERROR FUEL ELEC.
0 (benchmark) MBE
CV(RMSE)
-70.9 %
22.2 %
-47.8 %
13.8 %
1 (base case) MBE
CV(RMSE)
-4.4 %
4.2 %
-25.6 %
7.4 %
2 MBE
CV(RMSE)
-11.9 %
4.5 %
- 9.4 %
2.8 %
3 (baseline) MBE
CV(RMSE)
-6.6 %
3.4 %
-3.2 %
1.6 %
The third step consisted in changing the performance of primary HVAC heating and cooling
equipment (for which data ar not available) in more realistic values, depending on the type and age
of the equipment. At the end of this third step, the results are satisfying and both MBE and
CV(RMSE) are near the range defined by ASHRAE guideline 14-2002, respectively, +/- 5 % and +/-
15 %.
As shown in Figure 40 and Figure 41, both consumption profiles are quite well reproduced by the
calibrated model.
Figure 42 shows a comparison between the thermal signature based on 5 years of recorded data and
the one generated by the calibrated model (dotted line). Once more, the results are satisfying and the
thermal behavior of the building and its system are well represented. The slope of both recorded and
computed signatures, respectively 50 and 55 kW/K, are in good agreement with the building global
heat transfer coefficient mentioned here above (52 kW/K).
Page 49
Figure 42: Computed and Recorded Thermal Signatures
The computed thermal signature (dotted line) is characterized by a better correlation coefficient
(R²=98%). This indicates that, with strictly well defined occupancy, corresponding internal gains and
simplified HVAC control, the fuel consumption is mainly correlated to the outdoor temperature. So,
as supposed above, the discrepancies observed with the recorded data (R²=66%) should be mainly
due to the variations in occupancy rate, internal gains and control strategy.
Even if the calibration has not been performed for a given year because of the reasons previously
discussed, the simplified model used here seems to be able to represent the “average” behavior of the
building and its system.
After calibration, the simulation tool is used to disaggregate the electricity consumption and to
identify the main energy consumers.
As it appears in Figure 43, an important part of the monthly electricity consumption is due to lighting
and plug loads (appliances). An important part is also due to ventilation fans, pumps and basement
utilities (extraction fans to basement parking zone and parking lighting). Only 10% of the annual
electricity consumption are due to the chillers.
Figure 43:Monthly disaggregation of computed electricity consumption
Page 50
4.3.3 Retrofit options
Some retrofits were already made on the plant and on the AHU’s. The replacement of existing
induction units by more efficient devices (new induction units or fan coils, if fitting in the small
space available) should make possible to run the system with higher chilled water temperature and
therefore better chiller COP.
Many other retrofit opportunities can already be identified:
1. A better fresh air management is certainly achievable. The air renovation time period could
be reduced from 75 h to 50h per week, in order to fit with the full occupancy. In heating
period, the night set back of the thermostat is here uneconomical: it’s much better to shut
down the primary air supply and to use the induction units in static heating mode. From other
part, the primary air flowrate migth be reduced in mid season, according to the actual cooling
demand of the offices.
2. A direct energy recovery system might be installed between supply and exhaust air circuits.
3. Some air re-circulation would be welcome, whenever the induction units require more
primary air than what is needed for indoor air quality.
4. Variable speed might help in reducing fans and pumps consumptions.
5. Variable chilled water temperature might also be introduced.
6. The possibility of using such installation in free chilling mode (i.e. with production of chilled
water by the cooling towers only) should be always considered. Actually, an attempt of free
chilling was even done sometime ago, by adding a water-to-water heat exchanger between
the condenser and the evaporator circuits (in parallel to the chillers). For reasons which
couldn’t be found back, this experience failed and the system was dismanteled!
7. An optimal control of cooling towers should allow to reduce the electrical consumption of
their fans.
8. A more careful analysis of the space and time distributions of heating and cooling demands
would help in identifying the opportunities of heat pumping and reversible air conditioning:
- During almost all the heating season, the extracted air represents a “free” heat source of
more than 600 kW for a heat pump, whose evaporation temperature would be fixed
around 5°C.
- The air-cooled condensers of the auxiliary chillers, used all the year for the data
processing offices, are other free heat sources.
This last retrofit opportunity is studied in details in the frame of the IEA-ECBCS Annex 48 (Lebrun
and André, 2008) project (“Heat Pumping and Reversible Air Conditioning”).
Once again, adapted simulation models are badly needed to evaluate the selected ECO’s. Among the
ECOs identified here above, only a few ones are evaluated here.
The simplest retrofit opportunity is certainly the implementation of a better fresh air management. It
is proposed to reduce the air renovation time period from 75h to 55h per week, in order to fit better
with the occupancy period.
The second retrofit option is to to increase air recirculation, whenever the induction units require
more primary air than what is needed for IAQ. Currently, the fresh air flow rate per occupant is about
170 m³/h. It is proposed to reduce it to about 72 m³/h (corresponding to “high indoor air quality” as
defined by prEN standard 13779).
The third retrofit option envisaged here is to install a classical air-to-air recovery system on the fresh
air intake.
Page 51
As shown in Erreur ! Source du renvoi introuvable. a better management of the ventilation
schedule and the air recirculation allows a reduction of about 60% of the fuel consumption and
causes only a slight increase of the electricity consumption (less than 2%, due to additional pressure
drops in the economizer). The installation of an air-to-air recovery system leads to a smaller
reduction of fuel consumption (15%) but causes a bigger increase of electricity consumption (6%).
Of course, other retrofit options, such as replacement of lighting and appliances by less-consuming
devices or replacement of the induction units by units requiring a smaller primary air flow rate
should also be studied.
Figure 44: ECO's evaluation
4.3.4 Conclusion
In this case study One of the main difficulties comes from the too many lacks of information. Filling
these lacks would require, more detailed energy records on site and more detailed information about
the actual installation.
However, monthly energy bills and quick inspections allow a first and rough analysis of the
behaviour of the building and of its HVAC system.
Simulation models are used to allow a better interpretation of the available data. Differente models of
HVAC equipment are used at different stages of the energy audit. The most simplified models with
very few parameters are of great help for benchmarking purposes. They allow the auditor to get a
first impression about energy saving potentials.
A fully heuristic calibration method has been used to calibrate the tool to averaged fuel and
electricity consumptions. The use of averaged data has allowed us to avoid the effect of the variable
use of the building and its system.
Calibrated but still simplified simulation models are then used to disaggregate the electricity
consumption and to allow a better interpretation of on site records.
Finally, more detailed and specific simulation tools should be used to allow a safe identification and
an assessment of the most promising retrofit opportunities.
Page 52
Conclusion
To be continued…
Page 53
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Masy, G. (2006). Dynamic Simulation on Simplified Building Models and Interaction with Heating
Systems. Proceedings of the 7th
International Conference on System Simulation in Buildings, Liège,
Belgium. Liège: Les éditions de l’Université de Liège.
Masy, G. (2008). Modeling and validation of a simplified multizone dynamic building model
connected to heating system and HVAC unit. PhD Dissertation. Université de Liège, Belgium.
Visier, J.C., Jandon, M. (2004). International Energy Agency – Energy Conservation in Buildings
and Community Systems - Annex 40: Commissioning of Building HVAC Systems for Improved
Energy Performances. Synthesis Report.
Woloszyn, M. (1999). Moisture-Energy-Airflow modeling of multizone buildings. PhD Dissertation.
CETHIL Laboratory, INSA Lyon, France.
Page 56
Appendix
Appendix A: Walls Parameters Default Values
External Walls
External walls
Description
from indoor to outdoor
Insulation
thickness
cm
U
weighted
W/m²K
C
J/m²K
External vertical walls
hollow concrete block + insulation + air layer strongly
ventilated
5 0.55
150000
7 0.42
9 0.34
clay block + insulation + air layer strongly ventilated
5 0.50
7 0.39
9 0.32
massive wood + insulation + air layer strongly ventilated 7.5 0.32 100000
cellular concrete block + insulation + air layer strongly
ventilated 5 0.34 80000
Hollow concrete block + insulation + air layer weakly
ventilated + bricks
5 0.51
290000
7 0.39
9 0.32
clay block + insulation + air layer weakly ventilated +
bricks
5 0.46
7 0.37
9 0.30
cellular concrete block - 0.47 110000
fermacell wood + insulation + panel OSB + insulation +
celit + air between battens + wood 18.6 0.16 70000
massive wood + insulation + air layer weakly ventilated +
bricks 7.5 0.30 240000
plywood + insulation between rafters + plywood + air
layer strongly ventilated 6 0.59 15000
insulation + multiplex + air layer strongly ventilated 7.5 0.40 25000
panel OSB + insulation + celit + air layer strongly
ventilated 20 0.15
70000
panel OSB + insulation + celit 20 0.15
plywood + insulation between rafters + plywood + air
layer weakly ventilated + bricks 6 0.53 150000
insulation + multiplex + air layer weakly ventilated +
bricks 7.5 0.37 160000
air between battens + insulation + stone 9 0.32 1035000
insulation + panel OSB + insulation + concrete block 14 0.17 230000
Roofs
heavy : hollow concrete floor + concrete + insulation
6 0.47
460000 8 0.37
12 0.26
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light : insulation between purlins + air layer strongly
ventilated 18 0.28 20000
light : air layer weakly ventilated + insulation
6 0.49
15000
12 0.27
18 0.18
light : insulation between purlins + air layer strongly
ventilated
6 0.56
12 0.35
light : air layer weakly ventilated + agglomerates panel +
insulation
6 0.45
40000 8 0.36
12 0.25
light : air between battens + insulation + air layer strongly
ventilated 10 0.31 20000
heavy : insulation + hollow concrete floor + concrete 6 0.44
460000 8 0.35
light : air between battens + insulation + celit + air layer
strongly ventilated 17.5 0.17
50000
light : air between battens + insulation + tightness 10 0.28
Floors on outdoor environment
heavy : tiled floor + mortar + insulation + concrete +
hollow concrete floor
4 0.64
365000 6 0.47
8 0.37
light : wood + insulation + panel OSB + insulation + air
between battens + wood 15.7 0.20 70000
heavy : tiled floor + mortar + concrete + hollow concrete
floor + insulation
6 0.47 370000
8 0.37
Floors on crawled space heavy : tiled floor + mortar + insulation + concrete +
hollow concrete floor 4 0.59 360000
heavy : tiled floor + mortar + concrete + hollow concrete
floor + insulation 6 0.44 370000
heavy : tiled floor + mortar + insulation + reinforced
concrete slab 4 0.61
390000 heavy : tiled floor + mortar + reinforced concrete slab +
insulation 6 0.45
Floors on cellar heavy : tiled floor + mortar + insulation + concrete +
hollow concrete floor 4 0.59 360000
heavy : tiled floor + mortar + concrete + hollow concrete
floor + insulation 6 0.44 370000
heavy : tiled floor + mortar + insulation + reinforced
concrete slab 4 0.61
390000 heavy : tiled floor + mortar + reinforced concrete slab +
insulation 6 0.45
Ground contact floors
heavy : tiled floor + mortar + insulation + reinforced
concrete slab
4 0.68 390000
6 0.49
heavy : tiled floor + mortar + insulation + concrete ( +
sand + ballast) 7 0.43
430000 heavy : tiled floor + mortar + reinforced concrete slab +
insulation 4 0.68
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Ground contact vertical walls
hollow concrete block + mortar + insulation
3 0.83
320000 5 0.56
7 0.43
Party or indoor vertical walls in contact with unheated rooms hollow concrete block + insulation + concrete block 2 0.93
300000 clay block + insulation + clay block 2 0.69
cellular concrete block + insulation + cellular concrete
block 2 0.29
150000 hollow concrete block + insulation 6 0.48
clay block + insulation 6 0.44
cellular concrete block + insulation 6 0.31 80000
Party or indoor vertical walls in contact with heated rooms hollow concrete block + insulation + concrete block 2 0.93
300000 clay block + insulation + clay block 2 0.69
cellular concrete block + insulation + cellular concrete
block 2 0.30 150000
External walls Description
from indoor to outdoor φ θ
External vertical walls concrete block + insulation + air layer strongly ventilated
1
0.10
clay block + insulation + air layer strongly ventilated
massive wood + insulation + air layer strongly ventilated
cellular concrete block + insulation + air layer strongly ventilated
concrete block + insulation + air layer weakly ventilated + bricks
0.70
clay block + insulation + air layer weakly ventilated + bricks
cellular concrete block
fermacell wood + insulation + panel OSB + insulation + celit + air between
battens + wood
massive wood + insulation + air layer weakly ventilated + bricks
0.50
plywood + insulation between rafters + plywood + air layer strongly ventilated
insulation + multiplex + air layer strongly ventilated
panel OSB + insulation + celit + air layer strongly ventilated
panel OSB + insulation + celit
plywood + insulation between rafters + plywood + air layer weakly ventilated +
bricks 0.15 0.49
insulation + multiplex + air layer weakly ventilated + bricks 0.13 0.28
air between battens + insulation + stone 0.49 0.26
insulation + panel OSB + insulation + concrete block 0.25 0.13
Roofs heavy mass : hollow concrete floor floor + concrete + insulation 1
0.10 light mass : insulation 0.85
heavy mass : insulation + hollow concrete floor floor + concrete 0.20 0.40
light mass: air between battens + insulation + celit + air layer strongly ventilated 0.45 0.08
light mass : air between battens + insulation + tightness 0.59 0.06
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Floors on outside heavy mass : tiled floor + mortar + insulation + concrete + hollow concrete floor
0.65 0.10
light mass : wood + insulation + panel OSB + insulation + air between battens +
wood
heavy mass : tiled floor + mortar + concrete + hollow concrete floor + insulation 1
Floors on ventilated void heavy mass 1 0.10
Floors on cellar heavy mass 1 0.10
Ground contact floors heavy mass 1 0.05
Ground contact vertical walls concrete block + mortar + insulation 1 0.10
Party or internal vertical walls in contact with not heated rooms Insulated symmetrical or asymmetrical 1 0.15
Party or internal vertical walls in contact with heated rooms Insulated symmetrical 1 0.10
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Internal Walls
Internal walls
Description
from inside to outside of the zone
Insulation
thickness
cm
U
W/m²K
C
J/m²K
ζ
Internal vertical walls
gypsum board + air + gypsum board - 1.85 20000 Left part
Right part
0.50
0.50
concrete block - 2.51 160000 Left part
Right part
0.50
0.50
gypsum board + insulation + gypsum
board 7 0.42 20000
Left part
Right part
0.50
0.50
Internal floors
heavy : carpet + mortar + concrete +
hollow concrete floor - 2.00 450000
higher part
lower part
0.60
0.40
heavy : tiled floor + mortar + concrete +
hollow concrete floor - 2.22 450000
higher part
lower part
0.60
0.40
heavy : carpet + mortar + concrete +
hollow concrete floor + cavity/plenum +
suspended ceiling
- 0.85 460000 higher part
lower part
0.25
0.75
heavy : tiled floor + screed + insulation +
hollow concrete floor + cavity/plenum +
suspended ceiling
- 0.89 460000 higher part
lower part
0.25
0.75
Internal walls Description
from inside to outside of the zone ζ φ θ
Internal vertical walls
light mass
not insulated left part
0.5 1
0.50
right part 0.50
insulated left part
right part
0.10
0.10
heavy mass not insulated left part 0.80
right part 0.80
Internal floors
heavy mass
not insulated without
suspended ceiling
higher part
lower part
0.60 0.85 0.80
0.40 0.95 0.75
not insulated with
suspended ceiling
higher part
lower part
0.25 0.80 0.80
0.75 0.65 0.95
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Appendix B: List of inputs (BENCHMARK and SIMAUDIT)
Variable Units Name
Climate & Weather Data
Iglob W/m² Global radiation reaching an horizontal surface
Idiff W/m² Diffused radiation reaching an horizontal surface
Iirh W/m² Pre-computed infra-red losses (interpolation between 45 and 100 W/m²)
tout °C Outdoor dry-bulb temperature
wout kg/kg Outdoor humidity ratio
Occupancy Schedules
focc,od - hourly occupancy factor from Monday to Friday
focc,we - hourly occupancy factor during week end
fset,od - hourly control factor from Monday to Friday
fset,we - hourly control factor during week end
fAHU,od - hourly ventilation factor from monday to friday
fAHU,we - hourly ventilation factor during week end
flight,od - hourly lighting factor from Monday to Friday
flight,we - hourly lighting factor during week end
fappl,od - hourly appliances functioning factor from Monday to Friday
fappl,we - hourly appliances functioning factor during week end
fweek - Weekly occupancy factor
Page 62
Appendix C: List of parameters (BENCHMARK)
Variable Units Name
0. Simulation Parameters
τsummer,1 h Initial value of time variable
τsummer,2 h Final value of time variable
Δτsummer h Time step
Location - Belgium – Uccle or User (related to tables weather_data and
sun_data)
1. Building Description
1.1. General Description
Afloor m² Total floor surface (whole zone)
n_storey - Number of storeys
n_zone m Storey height
Afrontages\A,floor - Frontages area per unit of floor area
Fopaquefrontages,South - Fraction of opaque surface South oriented
Fopaquefrontages,East - Fraction of opaque surface East oriented
Fopaquefrontages,North - Fraction of opaque surface North oriented
Fopaquefrontages,West - Fraction of opaque surface West oriented
Fopaquefrontages,Shadow - Fraction of opaque surface shadowed
Awindows\A,frontages - Windows area per unit of frontage area
Fwindows,South - Fraction of glazed surface South oriented
Fwindows,East - Fraction of glazed surface East oriented
Fwindows,North - Fraction of glazed surface North oriented
Fwindows,West - Fraction of glazed surface West oriented
Fwindows,Shadow - Fraction of glazed surface shadowed
Ainternalwalls\storey m²/storey Internal walls area per storey
1.2. Building envelope
hin W/m².K Indoor combined radiative-convective heat transfer coef.
hout W/m².K Outdoor combined radiative-convective heat transfer coef.
Uopaque,frontages W/m²-K Opaque wall heat transfer coefficient
Copaque,frontages J/m².K Opaque wall thermal capacity per unit of wall area
ζ, θ, φ - Wall R-C model parameters
αopaque,frontages - Opaque wall absorbance
εir,opaque,frontages - Opaque wall emissivity
(idem for floor, ceiling and roof slabs)
Uwindows W/m²-K Windows heat transfer coefficient
SFwindows - Windows solar factor
Cint,walls \A,int,walls J/m²-K Internal walls thermal capacity per unit of wall area
1.3. Infiltration / Exfiltration
ninfiltr,max 1/h Maximal infiltration rate (related to the zone volume)
Xexfiltr 1/h Maximal exfiltration rate (related to the blown volume)
1.4. Internal gains
W appl,max\A,floor W/m² Maximal heat gain due to appliances per unit of floor area
W light,max\A,floor W/m² Maximal heat gain due to lighting per unit of floor area
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Afloor\occupant m²/occ Floor area/occupant
Occupants activity W/occ Occupants activity (related to metabolic rate)
2. HVAC system description
2.0. Indoor/Outdoor nominal conditions
tout,winter,sizing,n C Sizing winter outdoor temperature
RHout,winter,sizing,n - Sizing winter outdoor relative humidity
tout,summer,sizing,n C Sizing summer outdoor temperature
RHout,summer,sizing,n - Sizing summer outdoor relative humidity
Imax,n W/m² Sizing summer global solar radiation
ta,in,winter,n C Sizing winter indoor temperature
RHin,winter,n - Sizing winter indoor relative humidity
ta,in,summer,n C Sizing summer indoor temperature
RHin,summer,n - Sizing summer indoor relative humidity
ta,ex,AHU,winter,sizing,n C Sizing winter AHU exhaust temperature
ta,ex,AHU,summer,sizing,n C Sizing summer AHU exhaust temperature
2.1. Heating TU
FheatingTU,oversizing - Heating TU sizing factor
tw,su,heatingFCU,n C Nominal heating TU supply water temperature
Δtw,heatingFCU,n K Nominal heating TU water temperature variation
FcoolingTU,oversizing - Cooling TU sizing factor
tw,su,coolingFCU,n C Nominal cooling TU supply water temperature
Δtw,coolingFCU,n K Nominal cooling TU water temperature variation
2.2. Air Handling Unit
nCAV,vol\h,n 1/h Nominal ventilation rate per unit of zone volume
tw,suAHU,heating,n C Nominal AHU heating coils supply water temperature
Δtw,AHU,heating,n K Nominal AHU heating coils water temperature variation
tw,suAHU,cooling,n C Nominal AHU cooling coil supply water temperature
Δtw,AHU,cooling,n K Nominal AHU cooling coil water temperature variation
recovery,n - Sensible passive heat recovery nominal effectiveness
adiabhum,n - Adiabatic humidifier nominal effectiveness
s,mainfan,n - Main fan nominal effectiveness
s,returnfan,n - Return fan nominal effectiveness
ηsteam,generator,n - Electrical steam humidifier efficiency
preturnduct,n Pa Return duct nominal pressure drop
preturn,recovery,n Pa Recovery coil (return duct side) pressure drop
preturn,economiser,n Pa Economiser (return duct side) pressure drop
psupplyduct,n Pa Supply duct nominal pressure drop
pfresh,recovery,n Pa Recovery coil (supply duct side) pressure drop
pfresh,economiser,n Pa Economiser (supply duct side) pressure drop
pAHUfilter,n Pa AHU filter nominal pressure drop
ppreheatingcoil,n Pa Preheating coil nominal pressure drop
padiabhum,n Pa Adiabatic humidifier nominal pressure drop
psteamhum,n Pa Steam humidifier nominal pressure drop
pcoolingcoil,n Pa Cooling coil nominal pressure drop
ppostheatingcoil,n Pa Post-heating coil nominal pressure drop
2.3. Heat Production
Page 64
tw,ex,boiler,set,max °C Maximal exhaust boiler water temperature set point
tw,ex,boiler,set,min °C Minimal exhaust boiler water temperature set point
tout,low °C Lower limit of water temperature control law
tout,high °C Upper limit of water temperature control law
hotwaterdistr,n - Hot water distribution network nominal thermal efficiency
pw,hotwaterdistr,n Pa Hot water distribution network nominal pressure drop
s,sh,hotwaterpump,n - Hot water pump nominal effectiveness
motor,hotwaterpump,n - Hot water pump nominal electrical efficiency
boiler,n - Gas boiler nominal efficiency
tw,heating,n K Hot water nominal temperature variation
Fheatingplant,oversizing - Heating plant sizing factor
2.4. Cool Production
tw,cooling,n K Chilled water nominal temperature variation
fsizing,chiller - Chiller sizing factor
coldwaterdistr,n - Chilled water distribution network nominal thermal
efficiency
pw,coldwaterdistr,n Pa Chilled water distribution network nominal pressure drop
s,sh,coldwaterpump,n - Chilled water pump nominal effectiveness
motor,coldwaterpump,n - Chilled water pump nominal electrical efficiency
tw,ex,chiller,set °C Exhaust chiller water temperature set point
tw,cooling,n K Chilled water nominal temperature variation
Fcoolingplant,oversizing - Cooling plant sizing factor
coldwaterdistr,n - Chilled water distribution network nominal thermal
efficiency
pw,coldwaterdistr,n Pa Chilled water distribution network nominal pressure drop
2.5. HVAC System Control
ta,in,heating,set,min °C Heating indoor setpoint during unoccupation period
ta,in,heating,set,occ °C Heating indoor setpoint during occupation period
ta,in,cooling,set,occ °C Cooling indoor setpoint during occupation period
ta,in,cooling,set,max °C Cooling indoor setpoint during unoccupation period
tout,max,recovery °C Maximal outdoor temperature for heat recovery
tout,max,adiabhum °C Maximal outdoor temperature for adiabatic humidification
wex,adiabhum,set kg/kg Adiabatic humidifier exhaust humidity ratio set point
Cadiabhum,fbwex - Proportional regulation gain for adiabatic humidification
with feedback on exhaust humidity ratio
RHin,adiabhum,set - Indoor relative humidity set point for adiabatic
humidification
Cadiabhum,fbRHin - Proportional regulation gain for adiabatic humidification
with feedback on indoor relative humidity
Cdehumidif,fbwex - Proportional regulation gain for dehumidification with
feedback on exhaust humidity ratio
RHin,dehumidify,set - Indoor relative humidity set point for dehumidification
Cdehumidif,fbRHin - Proportional regulation gain for dehumidification with
feedback on indoor relative humidity
ta,ex,postheatingcoil,set °C Exhaust post-heating coil air temperature set point
Cpostheating 1/K Proportional regulation gain for postheating
tout,max,postheating °C Maximal outdoor temperature for postheating
Page 65
RHin,steamhum,set - Indoor relative humidity set point for humidification
tout,max,steamhum °C Maximal outdoor temperature for steam humidification
Csteamhum 1/K Proportional regulation gain for steam humidification
tout,max,heatingTU °C Maximal outdoor temperature for heating with terminal units
tout,min,coolingTU °C Minimal outdoor temperature for cooling with terminal units
CcoolingTU 1/K Proportional regulation gain for cooling with terminal units
CheatingTU 1/K Proportional regulation gain for heating with terminal units
Page 66
Appendix D: List of outputs (BENCHMARK)
Variable Units Name
Heating and Cooling Demands
Qheatingplant,kWh kWh Total heating demand to heat production system (including heat losses)
Qsysheatingload,kWh kWh Total useful heating demand from HVAC components
QheatingTU,kWh kWh Terminal Units heating demand
QheatingAHU,kWh kWh AHU components heating demand
Qpreheatingcoil,kWh kWh Preheating coil heating demand
Qpostheatingcoil,kWh kWh Postheating coil heating demand
Qhotwaterdistr,loss,kWh kWh Hot water distribution network heat losses
Qcoolingplant,kWh kWh Total cooling demand to cold production system (including heat gains)
Qsyscoolingload,kWh kWh Total useful cooling demand from HVAC components
QcoolingTU,kWh kWh Terminal Units cooling demand
QcoolingAHU,kWh kWh AHU components cooling demand
Qcoolingcoil,kWh kWh Preheating coil cooling demand
Qcoldwaterdistr,loss,kWh kWh Cold water distribution network heat gains
Fuel Consumption
Qfuel,kWh\m² kWh/m² Fuel consumption per square meter of floor area
Qboilerfuel,kWh kWh Total fuel consumption
Vboilerfuel,liter l Consumed fuel volume
Vboilerfuel,m³ m³ Consumed fuel volume
Electricity Consumption
Wlight,kWh kWh Lighting electricity consumption
Wappl,kWh kWh Appliances electricity consumption
WAHU,fans,kWh kWh AHU main and return fans electricity consumption
Wfans,kWh kWh Fans (AHU and TU) electricity consumption
Wfans,% % Percentage of electricity consumption due to fans
Wsteamgenerator,kWh kWh Electrical steam humidifier electricity consumption
Wpumps,kWh kWh Pumps electricity consumption
Wpumps,% % Percentage of electricity consumption due to pumps
Wchiller,kWh kWh Chiller electricity consumption
Wchiller,% % Percentage of electricity consumption due to chiller
Waux,boiler,kWh kWh Boiler auxiliaries electricity consumption
Wtotal,kWh kWh Boiler auxiliaries electricity consumption
Wtotal,peak,kWh kWh Peak electricity consumption
Wtotal,offpeak,kWh kWh Offpeak electricity consumption
Wtotal,kWh\m² kWh Offpeak electricity consumption
Primary Energy and CO2 Emissions
ECO2,elec,offpeak kg CO2 CO2 emissions due to offpeak electricity consumption
ECO2,elec,peak kg CO2 CO2 emissions due to peak electricity consumption
ECO2,elec kg CO2 Total CO2 emissions due to electricity consumption
ECO2,fuel kg CO2 Total CO2 emissions due to fuel consumption
ECO2,total kg CO2 Total CO2 emissions
Eprim,total kWh Total primary energy consumption
Page 67
