7S9X0 - Week 5 Uncertainties in building performance simulation

Transcription

1 7S9X0 - Week 5 Uncertainties in building performance simulation

2 Uncertainties Does any of you observe large variations in output due to unknown inputs in BESTEST case assignment? 7000 BESTEST CASE CASE-A BESTEST CASE Energy demand, kwh % 1000 CASE-A 0 Cooling energy demand Heating energy demand / Department of the Built Environment / Unit Building Physics and Services PAGE 1

3 Uncertainties BESTEST CASE Energy demand, kwh CASE-B BESTEST CASE 25% CASE-B 0 Cooling energy demand Heating energy demand / Department of the Built Environment / Unit Building Physics and Services PAGE 2

4 Uncertainties in BPS occupants weather How to deal with uncertainties? Boundary conditions of building performance simulation Chosen boundary conditions influence simulation results Carefully consider your assumptions! / Department of the Built Environment / Unit Building Physics and Services PAGE 3

5 Uncertainties in Occupant behavior occupants / Department of the Built Environment / Unit Building Physics and Services PAGE 4

6 Uncertainties in Occupant behavior 1. Why consider occupant behavior in BPS? 2. Occupant behavior models 3. Occupant scenarios 4. Overview / Department of the Built Environment / Unit Building Physics and Services PAGE 5

7 Occupant behavior Measurements in identical houses during two years: People prefer different temperatures Same temperature, but 40% difference in heat consumption Max difference: -65% [Maier et al., 2009] identical identical Occupants can have strong houses influence on building houses performance Wrong assumptions regarding occupant behavior lead to large differences between measurements and simulation results / Department of the Built Environment / Unit Building Physics and Services PAGE 6

8 Occupant behavior models How are occupants modeled in building simulation programs? Three types of occupant behavior models: 1. Deterministic models 2. Stochastic models 3. Multi-agent models Occupant behavior consists of: Presence of people in the building Interactions of people with the building / Department of the Built Environment / Unit Building Physics and Services PAGE 7

9 Deterministic models (1) Building simulation models are deterministic (each run of a model will give the same output) Occupant behavior is also modeled deterministically in most building simulation tools using diversity profiles : occupancy use of lighting Predefined fixed behavior / Department of the Built Environment / Unit Building Physics and Services PAGE 8

10 Stochastic models (2) Will people always turn on the light as predefined? In reality user behavior is more complex: Probability of switching on lighting: beliefs perception [Annex 53, 2012] Stochastic models are developed probability curves / Department of the Built Environment / Unit Building Physics and Services PAGE 9

11 Stochastic models (2) Behavior model framework: Occupant in room? illuminance Energy demand heating - office Frequency [-] Use lighting? Use blinds? Use appliances? Occupant s actions input simulation Use windows? Energy demand heating [Wh] Gives more information about the building performance than a single value! / Department of the Built Environment / Unit Building Physics and Services PAGE 10

12 Multi-agent models (3) The goal is not to simplify real behavior [ ] but to model real life behavior in all its complexity as closely as possible [Tabak & De Vries, 2010] Autonomous agents interact with each other and their environment Multi-agent model: Population of agents with specific state and location in building Definition of the agents needs, beliefs and rules for interaction [Tabak & De Vries, 2010] / Department of the Built Environment / Unit Building Physics and Services PAGE 11

13 Occupant behavior models Three types of occupant behavior models: 1. Deterministic models 2. Stochastic models 3. Multi-agent models Low complexity, low resolution, few inputs High complexity, high resolution, many inputs, time consuming Higher complexity and resolution require more input data, which might lead to higher uncertainties If occupants cannot influence the performance indicator, use the lowest model resolution! / Department of the Built Environment / Unit Building Physics and Services PAGE 12

14 Occupant scenarios Making assumptions regarding occupant behavior is difficult Handle uncertainties in assumptions by using occupant scenarios: low high internal gains, low high heating heating setpoint, setpoint, Aspects of occupant behavior: Low or high internal gains Low or high heating setpoint Low or high ventilation use Intermittent or constant presence Many possible occupant scenarios / Department of the Built Environment / Unit Building Physics and Services PAGE 13

15 Occupant scenarios Making assumptions regarding occupant behavior is difficult Handle uncertainties in assumptions by using occupant scenarios: Energy demand design 1 indicator of robustness to occupant behavior Energy demand design 2 / Department of the Built Environment / Unit Building Physics and Services PAGE 14

16 Occupant behavior - overview Occupants can have strong influence on building performance, so important to take occupants into account in building simulations Various occupant behavior models are developed: deterministic, stochastic, multi-agent During the building design phase, occupant scenarios can be used to investigate the performance robustness of buildings / Department of the Built Environment / Unit Building Physics and Services PAGE 15

17 Uncertainties in weather weather / Department of the Built Environment / Unit Building Physics and Services PAGE 16

18 Contents 1. Definitions 2. Weather Measurements Data Test reference years 3. Climate change scenarios PAGE 17

19 Definitions weather and climate Weather is the state of the atmosphere, to the degree that it is hot or cold, wet or dry, calm or stormy, clear or cloudy. The climate of a certain area is the average weather, the average of a longer time, of meteorological parameters as temperature, precipitation, moisture, solar irradiation and wind. Also the extreme values of such parameters belong to the climate. (translated from Dutch to English) PAGE 18

20 Weather measurements The Dutch weather is monitored by: 34 full metrological stations; Wind direction, wind speed, temperature, relative humidity, global horizontal irradiation, sight, precipitation type. 325 additional precipitation stations KNMI (2000) Handboek Waarnemingen; 1. Meetstation algemeen PAGE 19

21 Weather measurements De Bilt Eindhoven Wilhelminadorp Rotterdam Lelystad Stavoren KNMI (2000) Handboek Waarnemingen; 1. Meetstation algemeen PAGE 20

22 Weather measurements Used until 1992 PAGE 21

23 Weather data PAGE 22

24 Weather data Weather data parameters used in building simulation: Diffuse solar on the horizontal External dry bulb temperature Direct normal solar intensity Prevailing wind speed Wind direction Relative humidity PAGE 23

25 Typical weather year Most commonly a full-year hourly simulation is performed An extensive effort is therefor paid in finding representative or typical weather years by which long-term (multi-year) performance can be estimated by a single year Two types of typical weather years are used: 1. Test Reference Years (TRY) 2. Typical Meteorological Year (TMY) PAGE 24

26 Test reference year (TRY) Test Reference Year (TRY) A representative year selected from a multi-year set Weather years with extremely high or low mean temperatures are progressively eliminated Results in a mild year that excludes extreme conditions In the 70s the decision was made that the 1964/65 was representative for the Dutch outdoor climate when it was used for: Calculation of average energy use for heating and cooling* Calculation of indoor temperature in the summer* * ISSO Eisen voor de binnentemperatuur in gebouwen. ISSO 74. Rotterdam PAGE 25

27 Test reference year (TRY) ISSO Eisen voor de binnentemperatuur in gebouwen. ISSO 74. Rotterdam PAGE 26

28 Typical Meteorological Year (TMY) Typical Meteorological Year (TMY) The lack of extreme conditions in TRYs led to the development of TMYs TMY is a composite of typical months, not necessarily of the same year In 2008 the Dutch TMY was published in the NEN 5060 NEN Hygrothermal performance of buildings - Climatic reference data. NEN 5060 PAGE 27

29 Typical Meteorological Year (TMY) TRY (historical data) TMY (NEN 5060) PAGE 28

30 Climate change scenarios Future expectations for the Netherlands: Increase in extreme precipitation Number of rainy days will decrease The sea level will rise Temperature will rise Klein Tank & Lenderink, Climate change in the Netherlands. 21/05/2015 PAGE 29

31 Future indoor climate projection Use measured weather data to assess indoor climate of (Dutch) buildings Use climate scenarios to assess how vulnerable (robust) buildings are for climate change Apartment < Portiekflat, representatie van woningen in Nederland; - Vloeroppervlak: 59.0 m 2 ; - Gesloten geveloppervlak: 79.5 m 2 ; - Glas oppervlak: 16.4 m 2 ; - Rc-waarde dak: 0.22 m 2 K/W; - Rc-waarde gevel: 0.19 mknmi, 2 K/W; U-waarde glas Enkel: 5.2 W/m 2 K Dubbel: 2.9 W/m 2 K.

32 Future indoor climate projection Use measured weather data to assess indoor climate of (Dutch) buildings Use climate scenarios to assess how vulnerable (robust) buildings are for climate change

33 Summary / Department of the Built Environment / Unit Building Physics and Services PAGE 32

34 7S9X0-Week5 Building Performance Robustness

35 Contents What is robustness? Case study: occupant behavior o Robustness indicator to occupant behavior Case study: climate change o Robustness of building systems to climate change o Vulnerability to heat waves / Department of the Built Environment / Unit Building Physics and Services PAGE 34

36 Robustness (1) How to evaluate the robustness of a building? What is robustness? Is this a robust building? 30 o C delayed peak temperature reduced peak temperature 15 o C day night day Indoor air temperature; low thermal mass Indoor air temperature; high thermal mass Ambient temperature Indoor air temperature is not sensitive to ambient air temperature fluctuations / Department of the Built Environment / Unit Building Physics and Services PAGE 35

37 Robustness (2) Is this a robust building? [Maier et al., 2009] / Department of the Built Environment / Unit Building Physics and Services PAGE 36

38 Robustness (2) Is this a robust building? Measurements of identical houses during two years: People prefer different temperatures Same temperature, but 40% difference in heat consumption: - occupant behavior? - system fault? Max difference: -65% Energy demand is sensitive identical to occupant influences identical houses houses / Department of the Built Environment / Unit Building Physics and Services PAGE 37

39 Robustness (3) Indoor temperature Heating energy demand not sensitive to amb. temp. fluctuations sensitive to occupant behavior Sensitivity can be investigated for all sorts of performance indicators The sensitivity is a measure of a performance indicator s robustness to a certain stimulus: Highly sensitive PI is not robust Not sensitive PI is robust / Department of the Built Environment / Unit Building Physics and Services PAGE 38

40 Robustness (4) General definition of robustness: ability of a building to handle changes or disturbances in the building s environment and maintain the required performance Possible disturbances/uncertainties during operation of building: Weather conditions/climate Occupant behavior (e.g. use of windows, ventilation and appliances) Degradation of materials (e.g. aging of insulation materials) Faults in system (e.g. malfunction/defects) Robustness guarantees building performance (e.g. important for performance contracting) Take disturbances/uncertainties into account during design phase / Department of the Built Environment / Unit Building Physics and Services PAGE 39

41 Robustness to uncertainties-an example = conductivity material 1 density material 1 conductivity material 2 density material 2 x y [Houben et al., 2010] Range or standard deviation is an indicator of the robustness Probability distribution can be used to calculate risks (e.g. set target value and accept % higher than target) / Department of the Built Environment / Unit Building Physics and Services PAGE 40

42 Case study :Robustness to occupant behavior Case study: robustness to occupant behavior / Department of the Built Environment / Unit Building Physics and Services PAGE 41

43 Occupant behavior (2) Use occupant scenarios to assess the performance robustness of a building design to occupant behavior: low high internal gains, low high heating heating setpoint, setpoint, Aspects of occupant behavior: Low or high internal gains Low or high heating setpoint Low or high ventilation use Intermittent or constant presence Many possible occupant scenarios / Department of the Built Environment / Unit Building Physics and Services PAGE 42

44 Occupant behavior (3) Use occupant scenarios to assess the performance robustness of a building design to occupant behavior: Energy demand design 1 Energy demand design 2 Standard deviation (SD) is a measure of the robustness to occupant behavior / Department of the Built Environment / Unit Building Physics and Services PAGE 43

45 Occupant behavior (4) Use occupant scenarios to assess the performance robustness of a building design to occupant behavior RSD per performance indicator 120% Relative standarddeviation per performance indicator 100% Compare robustness of multiple design variants using the relative 80% standard deviation (RSD) as robustness indicator: 60% 40% 20% 0% 1 Average RSD = 2 Low mass and closed 3 Low mass and open SD mean Case studies 4 Heavy mass and closed 5 Heavy mass and open Heating energy demand Cooling energy demand Primary energy use Maximum room temperature [Hoes et al., 2009] Variant 3 shows to be most robust to occupant behavior, however regarding the absolute values this building performs bad, e.g. T max = 48 o C! Robustness indicator is useful as selection criterion for building 1: Average 2: Lightweight and closed 3: Lightweight and open 4: Heavyweight and closed 5: Heavyweight and open variants with comparable mean values / Department of the Built Environment / Unit Building Physics and Services PAGE 44

46 Case study-robustness to Climate change Future can not be foreseen, but exploration of the future can inform decision makers There is not one possible future, uncertainty calls for variety of futures, mapping a possibility space Use scenarios to assess performance of building designs For example, climate change scenarios [Klein Tank & Lenderink, 2009] / Department of the Built Environment / Unit Building Physics and Services PAGE 45

47 Climate change (1) Investigate robustness of three system concepts: Top-cooling Fancoil Floor cooling [Evers et al., 2008] / Department of the Built Environment / Unit Building Physics and Services 46

48 Climate change (2) Thermal comfort Energy use Top-cooling Fancoil Floor cooling Top-cooling Fancoil Floor cooling Thermal comfort: floor cooling most robust [Evers et al., 2008] Energy use: fancoil and floor cooling more robust than top-cooling / Department of the Built Environment / Unit Building Physics and Services 47

49 Climate change- vulnaberility to heat waves (3) 1 The results show that terraced houses are most robust to heat waves; detached houses are least robust, followed by corner and semi-detached houses Terraced house Corner house Detached house Semi-detached house / Department of the Built Environment / Unit Building Physics and Services 48

50 In conclusion Robustness is the ability of a building to handle changes or disturbances in the building s environment and maintain the required performance Robustness of building designs and systems can be investigated based on uncertainties and scenarios using appropriate robustness indicators / Department of the Built Environment / Unit Building Physics and Services PAGE 49

51 Thank you! / Department of the Built Environment / Unit Building Physics and Services PAGE 50