building energy performance modelling and simulation...
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1
2011/2012
© ČVUT v Praze, FSv K125 prof.Kabele
BUILDING ENERGY PERFORMANCE MODELLING AND SIMULATION 5
125BEPM,MEB,MEC prof.Karel Kabele 56
Climate data
Project site
Building geometry
Materials & Constructions
Lighting & equipment
internal gains
Occupancy
Lighting
Ventilation
Plant & Systems
IEA, 1994 Study the detail of input data
Input data categories
125BEPM,MEB,MEC 57 prof.Karel Kabele
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© ČVUT v Praze, FSv K125 prof.Kabele
Input data
•Hourly weather data (in most cases for an entire year). Main climate parameters:
– Dry-bulb temperature
– RH
– Wind speed and wind direction
– Solar radiation (direct and diffuse)
Reference year (RY)
Should represent mean values of main climate parameters that are as close as possible to long-time mean values.
Main requirements for RY
• True frequencies, i.e., as near as possible to true mean values over a longer period,
e.g., a month, and a natural distribution of higher and lower values for single days.
• True sequences, i.e., the weather conditions must have a duration and follow each
other in a similar manner to often-recorded conditions for the location.
• True correlation between different parameters, i.e. temperature, solar radiation,
cloud cover and wind.
prof.Karel Kabele 58
Climate data
125BEPM,MEB,MEC
Dry and wet bulb temperature
125BEPM,MEB,MEC prof.Karel Kabele 59
Fan
Wet bulb
temperature Dry bulb
temperature
Wet
„sock“
Air
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© ČVUT v Praze, FSv K125 prof.Kabele
Psychrometric chart
125BEPM,MEB,MEC prof.Karel Kabele 60
Dry bulb
temperature
Relative
humidity %
Absolute
humidity
Dew
point
Wet bulb
temperature
Enthalpy
TEMPERATURE
HUMIDITY
RATIO
g/kg
Fan Wet bulb
temperat
ure
Dry bulb
temperatu
re
Wet
„sock“
Air
Weather data formats
• *.epw – EnergyPlus weather files
• *.wea - Weather Data File
• *.dat - plain text file
• WYEC and WYEC2 data files
• Test Reference Year (TRY)
• Typical Meteorological Year (TMY)
• Design Summer Year (DSY)
Data Sources
• Simulation programs file libraries
• Energy plus website
• Meteonorm
• ESP-r embedded files
• ASHRAE
Conversion of data formats possible
• Weather Tool (Square One)
• Esp-r
• Weather manager
Climate data
125BEPM,MEB,MEC 61 prof.Karel Kabele
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Climate data for energy calculations:
Multi-year datasets: they are fundamental and include a substantial amount of information for a number of years.
Typical years: a typical or reference year is a single year of hourly data selected to represent the range of weather patterns that would typically be found in a multi-year dataset. The definition of a typical year depends on how it satisfies a set of statistical tests relating it to the parent multi-year dataset.
Representative days: they are hourly data for some average days selected to represent typical climatic conditions. Representative days are economical for small-scale analysis and are often found in simplified simulation and design tools.
Selection of weather data format driven by the modelling objective. E.g.:
Sizing of cooling/heating plant => design weather year
Estimation of overheating risk for naturally ventilated spaces (percentage of hours over a
certain temperature) => near extreme summer and mid-season
Annual energy use prediction=> typical weather year
Climate data
125BEPM,MEB,MEC prof.Karel Kabele
prof.Karel Kabele 63
PRG iwec
Climate data
125BEPM,MEB,MEC
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© ČVUT v Praze, FSv K125 prof.Kabele
125BEPM,MEB,MEC 64 prof.Karel Kabele
Solar processes
• Solar constant 1360 W/m2
• Difusse and direct radiation • Real radiation max 1000 W/m2
• Solar altitude (Alt) (β) sometimes referred to as elevation, that is the angle between the object and the observer's local horizon.
• Solar azimuth (Az) (φ), that is the angle of the object around the horizon
125BEPM,MEB,MEC 65 prof.Karel Kabele
sinsincoscoscossin LATHLAT
cos
sincossin
H
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© ČVUT v Praze, FSv K125 prof.Kabele
Solar processes
Incident angle of solar rays to sloped surface
125BEPM,MEB,MEC prof.Karel Kabele 66
cossinsincoscoscos
θ the angle of incidence
between the direct
solar beam and the
normal to the surface
β Solar altitude
γ Surface-solar azimuth - the
angular difference between
the solar azimuth φ and
the surface azimuth ψ.
Σ surface tilt angle, measured from the horizontal
Project Site
Input data
• Location (e.g. latitude, longitude, altitude)
• Solar and wind exposure
• Ground reflectance and temperature
Data Sources
• Client
• Architect
• Photographic material
• Weather file
• Google Earth
• Topographic maps
• Site visit
• Inherent program database
125BEPM,MEB,MEC 67 prof.Karel Kabele
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© ČVUT v Praze, FSv K125 prof.Kabele
Building Geometry
Input data
• Single- or multi-zone simulation programs orientation, space volumes, opaque and
transparent surface areas
• Whole-building simulation programs orientation, full 3D geometry
Data Sources
• Drawings and specifications
• CAD geometry import
Zoning
• Increased complexity has a significant negative impact on calculation time (for program)
and on modeling time (for user) especially for large projects with the benefits in the
simulation output from this more “realistic” representation of the building being only
minimal.
• Spaces should be grouped into one zone when similarities exist in:
Free-running environmental performance
Conditioning (HVAC) characteristics
Internal and solar gains.
Zoning
125BEPM,MEB,MEC 68 prof.Karel Kabele
http://www.doe2.com/download/equest/eQUESTv3-Overview.pdf
125BEPM,MEB,MEC 69 prof.Karel Kabele
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© ČVUT v Praze, FSv K125 prof.Kabele
Input data
• Material properties (conductivity, density, specific heat, short-wave absorptivity, long-wave emissivity, moisture diffusion resistance )
• Thickness of individual element layers
Data Sources
• Opaque building elements:
o Architect
o Inherent program library
o User personal database
o Published databases from recognized institutions and associations (e.g. ASHRAE, CIBSE)
• Transparent building elements:
• Facade specialist
o Manufacturer data
o Output from specific programs (e.g. WIS and WINDOW)
Materials & Constructions
125BEPM,MEB,MEC 70 prof.Karel Kabele
Optical properties
prof.Karel Kabele 71
Documentation Visible transmittance Solar absorptivity and
reflectivity U-value
Calculation Incident angle (0-80°) related values Direct transmittance Reflectivity Heat gain Absorptivity
125BEPM,MEB,MEC
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© ČVUT v Praze, FSv K125 prof.Kabele
Optical properties
The Solar Heat Gain Coefficient (SHGC) or g-factor consists of two components: – Solar radiation passed through the window (solar optical transmittance) – Solar radiation absorbed within the glazing system and redirected to the
indoor space by heat transfer (inward flowing fraction)
• The solar optical transmittance is a wavelength-dependent spatial distribution function. It is associated with the incident direction of the sun (bi-directional function) and depends on the type (material, coating, thickness) and geometry of the fenestration system. The considered solar spectrum is mainly visible and near infrared.
• The inward flowing fraction depends in addition on the inside/outside air temperatures and film coefficients and on the room characteristics, and relies on the combination of convection, conduction and radiation effects. It is mainly based on the far infrared spectrum.
125BEPM,MEB,MEC prof.Karel Kabele 72
Glazing
prof.Karel Kabele 73
Clear float 76/71, 6mm, internal blind id: DCF7671_06i
Clear float 76/71, 6mm, no blind id: DCF7671_06nb
125BEPM,MEB,MEC
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© ČVUT v Praze, FSv K125 prof.Kabele
WINDOW 5.2
prof.Karel Kabele 74
http://windows.lbl.gov/
125BEPM,MEB,MEC
Sensible heat from lights
Heat transferred to the room from the lights can be calculated as
Hl = Pinst K1 K2
where
Hl = heat transferred from the lights (W)
Pinst = installed effect (W)
K1 = simultaneous coefficient
K2 = correction coefficient if lights are ventilated. (= 1 for no ventilation, = 0.3-0.6 if ventilated)
76 prof.Karel Kabele
Installed effect W/m2
125BEPM,MEB,MEC
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Sensible heat from electric equipment
Heat transferred from electrical equipment can be calculated as
• Heq = Peq K1 K2
where
– Heq = heat transferred from electrical equipment (W)
– Peq = electrical power consumption (W)
– K1 = load coefficient
– K2 = running time coefficient
prof.Karel Kabele 125BEPM,MEB,MEC 77
Sensible heat from machines
When machines runs heat can be transferred to the room from the motor and/or the machine.
If the motor is in the room and the machine is outside Hm = Pm / hm - Pm
If the motor is belt driven and the motor and belt is in the
room and the machine is outside Hm = Pm / hm - Pm hb
If the motor and the machine is in the room Hm = Pm / hm • In this situation the total power is transferred as heat to
the room. • Note! If the machine is a pump or a fan, most of the
power is transferred as energy to the medium and may be transported out of the room.
If the motor is outside and the machine is in the room Hm = Pm
If the motor is belt driven and the motor and belt is outside
and the machine is in the room Hm = Pm hb
prof.Karel Kabele 78
where Hm = heat transferred from the machine to the room (W) Pm = electrical motor power consumption (W) hm = motor efficiency hb = belt efficiency
125BEPM,MEB,MEC
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Sensible and latent heat from persons
• Number
– Design values
– Models – static, stochastic
• Heat
• CO2 production
125BEPM,MEB,MEC prof.Karel Kabele 80
REF: J. Page, D. Robinson, N. Morel, J.-L. Scartezzini, A generalised stochastic model for the simulation of occupant presence Energy and Buildings (2007)
Metabolic rate
• The metabolic rate, or human heat production, is often measured in the unit "Met". The metabolic rate of a relaxed seated person is one Met, where
• 1 Met = 58 W/m2 • The mean surface area, the Du-Bois area, of the
human body is approximately 1.8 m2. The total metabolic heat for a mean body can be calculated by multiplying with the area. The total heat from a relaxed seated person with mean surface area would be
• 58 W/m2 x 1.8 m2 = 104 W
125BEPM,MEB,MEC prof.Karel Kabele 81
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prof.Karel Kabele 82
Activity W/m2
Reclining 46
Seated relaxed 58
Standing relaxed 70
Sedentary activity (office, dwelling, school, laboratory) 70
Graphic profession - Book Binder 85
Standing, light activity (shopping, laboratory, light industry) 93
Teacher 95
Domestic work - shaving, washing and dressing 100
Standing, medium activity (shop assistant, domestic work) 116
Washing dishes standing 145
Domestic work - washing by hand and ironing (120-220 W) 170
Volleyball 232
Gymnastics 319
Aerobic Dancing, Basketball, Swimming 348
Sports - Ice skating, 18 km/h 360
Skiing on level, good snow, 9 km/h, Backpacking, Skating ice or roller, Tennis 405 1 Met = 58 W/m2 , 58 W/m2 x 1.8 m2 = 104 W
125BEPM,MEB,MEC
Occupants - sensible x latent heat
Specific Enthalpy of Dry Air = Sensible Heat
ha = cpa . t Specific Enthalpy of Water Vapor = Latent Heat
hw = cpw . t + hwe
Specific Enthalpy of Moist Air h = ha + x hw
125BEPM,MEB,MEC 83 prof.Karel Kabele
where
h = specific enthalpy of moist air (kJ/kg)
ha = specific enthalpy of dry air (kJ/kg)
x = humidity ratio (kg/kg)
hw = specific enthalpy of water vapor (kJ/kg)
t = air temperature = water vapor temperature (oC)
cpa = specific heat capacity of air at constant pressure (kJ/kg.oC,
kWs/kg.K) =1.006 (kJ/kgoC)
cpw = specific heat capacity of water vapor at constant pressure
(kJ/kg.oC, kWs/kg.K) =1.84 (kJ/kg.oC)
hwe = evaporation heat of water at 0oC (kJ/kg) = 2,502 (kJ/kg)
Radiation Convection
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Heat load by persons
0
50
100
150
200
250
300
350W
Latent heat
Radiation
Convection
125BEPM,MEB,MEC prof.Karel Kabele 84
Operation profile
Cold water use in residential building
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
hod
Co
ld w
ate
r (
w/o
DH
W)
l/p
ers
/hr
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
Mean
125BEPM,MEB,MEC prof.Karel Kabele 85
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CO2 production
• Carbon dioxide (CO2) concentration in "clean" air is 575 mg/m3.
• Huge concentrations can cause headaches and the concentration should be below 9000 mg/m3.
prof.Karel Kabele 125BEPM,MEB,MEC 86
Ventilation
Input data
• Mechanical ventilation rates
• Infiltration rates
• Mechanical ventilation schedules (hourly, daily, weekly, seasonal etc)
• Controls
• Characteristics of fans and ducts
• External pressure coefficients and characteristics of natural ventilation openings (size, operation schedule etc) and
• In case of CFD also define geometry, grid, boundary conditions and turbulence model.
Data Sources
o Building services engineer
o Published databases and guidelines from recognized institutions and associations (e.g. ASHRAE, SMACNA, AIVC)
125BEPM,MEB,MEC 87 prof.Karel Kabele
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© ČVUT v Praze, FSv K125 prof.Kabele
Plant & Systems
Input data
• System types (e.g. VAV, CAV) and specifications (e.g. efficiency, capacity)
• Plant specification for each system component (e.g part load performance curves, full load efficiency, stand-by losses etc )
• System and plant components control characteristics (e.g. thermostat set points, sensor types and locations, operational characteristics such as: On/Off, proportional only, etc)
Resources
o Building services engineer
o Inherent program library
o Published databases from recognized institutions and associations (e.g. ASHRAE, CIBSE)
125BEPM,MEB,MEC 88 prof.Karel Kabele
http://www.designbuilder.co.uk/content/view/115/182/
Graphical definition of HVAC plant and components
125BEPM,MEB,MEC 89 prof.Karel Kabele
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ESP-r background
• ESP-r (Environmental Systems Performance; r for „research“) • Dynamic, whole building simulation finite volume,
finite difference sw based on heat balance method. • Academic, research / non commercial • Developed at ESRU, Dept.of Mech. Eng. University of
Strathclyde, Glasgow, UK by prof. Joseph Clarke and his team since 1974
• ESP-r is released under the terms of the GNU General Public License. It can be used for commercial or non-commercial work subject to the terms of this open source licence agreement.
• UNIX, Cygwin, Windows
prof.Karel Kabele 90
http://www.esru.strath.ac.uk/
125BEPM,MEB,MEC
ESP-r architecture
prof.Karel Kabele 91
Project manager
Climate
Material
Construction
Plant components
Event profiles
Optical properties
Databases maintenace
Model editor
Zones
Networks •Plant •Vent/Hydro •Electrical •Contaminants
Controls
Simulation controler
Results analysis
•Timestep •Save level •From -To •Results file dir •Monitor •…
•Graphs •Timestep rep. •Enquire about •Plant results •IEQ •Electrical •CFD •Sensitivity •IPV
125BEPM,MEB,MEC
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LOW-ENERGY BUILDING ENERGY SYSTEM MODELLING
Case study
125BEPM,MEB,MEC prof.Karel Kabele 92
Introduction
• Low Energy Buildings ? < 50 kWh/m2/a
– perfect thermal insulation of the building envelope
– design and control of heating systems
– warm-air heating systems.
– solar energy utilisation
– long-term energy accumulation
• How to design?
125BEPM,MEB,MEC prof.Karel Kabele 93
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Low Energy Building
125BEPM,MEB,MEC prof.Karel Kabele 94
Architectural concept Zoning
Greenhouse
Thermal insulation
Air tightness of the envelope
Energy system concept – Controlled ventilation
– Warm-air heating
– Solar energy utilisation
125BEPM,MEB,MEC prof.Karel Kabele 95
Principles of solar energy utilisation
Active solar water collectors
Passive solar gains via glazed balconies
Gains from greenhouse – midterm accumulation into the gravel accumulator below the building.
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125BEPM,MEB,MEC prof.Karel Kabele 96
Midterm solar energy accumulation
Greenhouse air warming up
Loading of the accumulator
Unloading of the accumulator
Additional heat source
125BEPM,MEB,MEC prof.Karel Kabele 97
Problem description
Boundary conditions
Geometry
Climate
Fresh air volume
Required output of the system
Optimisation criterions
Annual energy consumption
Output of the additional heat source
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© ČVUT v Praze, FSv K125 prof.Kabele
125BEPM,MEB,MEC prof.Karel Kabele 98
Modelling of energy performance
Modelling tool selection criterions
Dynamic modelling
Heat transfer coefficients
ESP-r, TRNSYS
Model in ESP-r
Zonal model describing building and energy system … why 2 models?
Energy system
Building
+
Building model
125BEPM,MEB,MEC prof.Karel Kabele 99
Input:
10 zones, construction, shading elements, operational schedule
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© ČVUT v Praze, FSv K125 prof.Kabele
Model of active solar system with mid-term heat
accumulation
125BEPM,MEB,MEC prof.Karel Kabele 100
ESP-r model •HVAC system divided into 5 thermal zones
•roof air solar collector •greenhouse air solar collector •gravel heat accumulator •heat exchanger •air heater
125BEPM,MEB,MEC prof.Karel Kabele 101
Simulation Climate database:
Test reference year Time period 1 year Time step of the output 1 hour Time step of the calculation 1 minute
Building: What?
Energy demand for heating How? 1x simulation loop Output: Heating output
Energy system What? Annual energy consumption How? Virtual experiments •Loading air variation •Accumulation mass of the gravel Output? Design of the elements
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Simulation results
125BEPM,MEB,MEC prof.Karel Kabele 102
Annual energy consumption
Heating energy consumptionimpact of accumulator
100%
56%
52%
53%
47%
47%
44%
0%
20%
40%
60%
80%
100%
120%
0 1 2 3 4 5 6
Virtual experiment Nr.
Virtual experiment
0 without accumulator
1-3 change of the loading air volume 100 to 2000 m3/h
4-6 change of gravel mass 50 to180 t
Energetický systém
Temperature in the accumulator
100% = 11,4MWh =410EUR/year
Conclusions
• Virtual experiments confirmed that use of preheating of fresh air supply in gravel accumulator, located below the building contributes positively into the energy balance.
• Use of simple preheating of fresh air supply in gravel accumulator decreases annual energy consumption for ventilation air to approx. 50%.
• Virtual experiments did not confirm significant influence of design parameters to the collecting and accumulating of solar energy in simulated configuration of collectors and accumulator size. The solar energy contribution is in this case very small and most of the accumulator energy gain is given by relative constant earth temperature below the building. In all of simulated virtual experiments was the accumulator mass temperature during the year in the range 12°C to 16°C
125BEPM,MEB,MEC prof.Karel Kabele 103
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