Input data categories

Transcription

1 BUILDING ENERGY PERFORMANCE MODELLING AND SIMULATION 5 prof.karel Kabele 56 Input data categories Plant & Systems Climate data Project site Ventilation Lighting Occupancy Building geometry Materials & Constructions IEA, 1994 Study the detail of input data Lighting & equipment internal gains prof.karel Kabele 57 1

2 Climate data 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 Dry and wet bulb temperature Fan Dry bulb temperature Wet bulb temperature Wet sock Air prof.karel Kabele 59 2

3 Psychrometric chart Enthalpy Dry bulb temperatu re Fan Wet bulb temperat ure Relative humidity % HUMIDITY RATIO g/kg Wet sock Absolute humidity Air TEMPERATURE Dew point Wet bulb temperature Dry bulb temperature prof.karel Kabele 60 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 prof.karel Kabele 61 3

4 Climate data 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 prof.karel Kabele 62 PRG iwec Climate data prof.karel Kabele 63 4

5 prof.karel Kabele 64 Solar processes Solar constant 1360 W/m 2 Difusse and direct radiation Real radiation max 1000 W/m 2 Solar altitude (Alt) (β) sometimes referred to as elevation, that is the angle between the object and the observer's local horizon. sin cos LAT cos cos H sin LAT sin Solar azimuth (Az) (φ), that is the angle of the object around the horizon cos sin H sin cos prof.karel Kabele 65 5

6 Solar processes Incident angle of solar rays to sloped surface cos cos cos sin sin cos θ β γ the angle of incidence between the direct solar beam and the normal to the surface Solar altitude Surface-solar azimuth - the angular difference between the solar azimuth φ and the surface azimuth ψ. Σ surface tilt angle, measured from the horizontal prof.karel Kabele 66 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 prof.karel Kabele 67 6

7 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 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. prof.karel Kabele 68 m/download/equest/ equestv3- Overview.pdf prof.karel Kabele 69 7

8 Materials & Constructions Input data Material properties (conductivity, density, specific heat, short-wave absorptivity, longwave emissivity, moisture diffusion resistance ) Thickness of individual element layers Data Sources Opaque building elements: o o o o Architect Inherent program library User personal database Published databases from recognized institutions and associations (e.g. ASHRAE, CIBSE) Transparent building elements: Facade specialist o o Manufacturer data Output from specific programs (e.g. WIS and WINDOW) prof.karel Kabele 70 Optical properties Documentation Visible transmittance Solar absorptivity and reflectivity U-value Calculation Incident angle (0-80 ) related values Direct transmittance Reflectivity Heat gain Absorptivity prof.karel Kabele 71 8

9 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. prof.karel Kabele 72 Glazing Clear float 76/71, 6mm, no blind id: DCF7671_06nb Clear float 76/71, 6mm, internal blind id: DCF7671_06i prof.karel Kabele 73 9

10 WINDOW prof.karel Kabele 74 Sensible heat from lights Heat transferred to the room from the lights can be calculated as H l = P inst K 1 K 2 where H l = heat transferred from the lights (W) P inst = installed effect (W) K 1 = simultaneous coefficient K 2 = correction coefficient if lights are ventilated. (= 1 for no ventilation, = if ventilated) Installed effect W/m 2 prof.karel Kabele 76 10

11 Sensible heat from electric equipment Heat transferred from electrical equipment can be calculated as H eq = P eq K 1 K 2 where H eq = heat transferred from electrical equipment (W) P eq = electrical power consumption (W) K 1 = load coefficient K 2 = running time coefficient prof.karel Kabele 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 H m = P m / h m - P m If the motor is belt driven and the motor and belt is in the room and the machine is outside H m = P m / h m - P m h b If the motor and the machine is in the room H m = P m / h m 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 H m = P m If the motor is belt driven and the motor and belt is outside and the machine is in the room H m = P m h b 78 where H m = heat transferred from the machine to the room (W) P m = electrical motor power consumption (W) h m = motor efficiency prof.karel Kabele h b = belt efficiency 11

12 Sensible and latent heat from persons Number Design values Models static, stochastic Heat CO 2 production REF: J. Page, D. Robinson, N. Morel, J.-L. Scartezzini, A generalised stochastic model for the simulation of occupant presence Energy and Buildings (2007) 80 prof.karel Kabele 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/m 2 The mean surface area, the Du-Bois area, of the human body is approximately 1.8 m 2. 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/m 2 x 1.8 m 2 = 104 W 81 prof.karel Kabele 12

13 Activity W/m 2 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 ( 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 Met = 58 W/m 2, 58 W/m 2 x 1.8 m 2 = 104 W 82 prof.karel Kabele Occupants - sensible x latent heat Specific Enthalpy of Moist Air h = ha + x hw Specific Enthalpy of Dry Air = Sensible Heat ha = cpa. t Radiation Convection Specific Enthalpy of Water Vapor = Latent Heat hw = cpw. t + hwe 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 ( o C) cpa = specific heat capacity of air at constant pressure (kj/kg. o C, kws/kg.k) =1.006 (kj/kg o C) cpw = specific heat capacity of water vapor at constant pressure (kj/kg. o C, kws/kg.k) =1.84 (kj/kg. o C) hwe = evaporation heat of water at 0 o C (kj/kg) = 2,502 (kj/kg) 83 prof.karel Kabele 13

14 W Cold water (w/o DHW) l/pers/hr Heat load by persons Latent heat Radiation Convection 84 prof.karel Kabele Operation profile Cold water use in residential building Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mean hod 85 prof.karel Kabele 14

15 CO 2 production Carbon dioxide (CO 2 ) concentration in "clean" air is 575 mg/m 3. Huge concentrations can cause headaches and the concentration should be below 9000 mg/m 3. prof.karel Kabele 86 Input data Mechanical ventilation rates Infiltration rates Ventilation 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 o Building services engineer Published databases and guidelines from recognized institutions and associations (e.g. ASHRAE, SMACNA, AIVC) prof.karel Kabele 87 15

16 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 o o Building services engineer Inherent program library Published databases from recognized institutions and associations (e.g. ASHRAE, CIBSE) prof.karel Kabele 88 Graphical definition of HVAC plant and components prof.karel Kabele 89 16

17 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 noncommercial work subject to the terms of this open source licence agreement. UNIX, Cygwin, Windows prof.karel Kabele 90 ESP-r architecture Databases maintenace Climate Material Construction Plant components Event profiles Optical properties Model editor Zones Networks Plant Vent/Hydro Electrical Contamina nts Controls Project manager Simulation controler Timestep Save level From -To Results file dir Monitor Results analysis Graphs Timestep rep. Enquire about Plant results IEQ Electrical CFD Sensitivity IPV prof.karel Kabele 91 17

18 Case study LOW-ENERGY BUILDING ENERGY SYSTEM MODELLING prof.karel Kabele 92 Introduction Low Energy Buildings? < 50 kwh/m 2 /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? prof.karel Kabele 93 18

19 Low Energy Building Architectural concept Zoning Greenhouse Thermal insulation Air tightness of the envelope Energy system concept Controlled ventilation Warm-air heating Solar energy utilisation prof.karel Kabele 94 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. prof.karel Kabele 95 19

20 Midterm solar energy accumulation Greenhouse air warming up Loading of the accumulator Unloading of the accumulator Additional heat source prof.karel Kabele 96 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 prof.karel Kabele 97 20

21 Modelling of energy performance Energy system 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? Building + prof.karel Kabele 98 Building model Input: 10 zones, construction, shading elements, operational schedule prof.karel Kabele 99 21

22 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 Model of active solar system with mid-term heat accumulation prof.karel Kabele 100 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 prof.karel Kabele

23 Simulation results Annual energy consumption Heating energy consumption impact of accumulator 100% = 11,4MWh =410EUR/year 120% 100% 80% 60% 40% 20% 0% 100% 56% 52% 53% 47% 47% Virtual experiment Nr. 44% Virtual experiment 0 without accumulator 1-3 change of the loading air volume 100 to 2000 m 3 /h 4-6 change of gravel mass 50 to180 t Energetický systém Temperature in the accumulator prof.karel Kabele 102 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 prof.karel Kabele