3. Pre-design applications

Transcription

1 3. Pre-design applications 7S9X0 Environmental Performance of Buildings TU/e Dept. of the Built Environment Building Performance group Environmental Performance of Buildings 7S9X0 TU/e TU/e Building Building Performance Group Group

2 Building simulation typical application Simulation >> When? Usually after the design phase, for code compliance Eventually during the design phase, for decision making Rarely before the design of any particular building or after the building is constructed. PAGE 2

3 Pre-design simulation - examples Product development Strategy for building concept development Development of simplified (or surrogate or meta) models Government policy target setting PAGE 3

4 Product development Start from lab-scale prototype Support decision-making and identify potential areas for improvement Which properties/characteristics should be changed? Focus on promising markets Quantify potential performance improvement for typical buildings Identify future development directions PAGE 4

5 Product development example 1 Smart Energy Glass (PeerPlus) Relative performance gains [%] Total energy savings Useful daylight illuminance Glare discomfort Overheating hours PAGE 5

6 Product development example 1 PAGE 6

7 Product development example 2 Low infrared absorptivity coatings Primary energy consumption (MJ/m2.y) Normal (n 8) Improved IR reflectivity (n 10) IR reflectivity of light grey HPL panels Heating Cooling PAGE 7

8 Product development example 2 Low infrared absorptivity coatings PAGE 8

9 Product development challenges Lack of information about the product How do we simulate something that does not exist yet? How to adapt current programs to simulate future products? Source code modifications are often necessary PAGE 9

11 Strategy development Assess the potential of innovative building concepts Put in perspective current practices in the building industry What-if analysis Little concern regarding immediate feasibility Innovation steering, providing input for new ideas/developments in the industry PAGE 11

12 Strategy development example 1 Virtual Natural Lighting Solutions R.A.. Mangkuto (2013) PhD project, TU/e PAGE 12

13 View complexity Strategy development example 1 Complex view, diffuse Simplified view, diffuse Complex view, directional Simplified view, directional Light directionality R.A.. Mangkuto (2013) PhD project, TU/e PAGE 13

14 Strategy development example 2 Earthscrapers Dronkelaar, C. van 2013, Underground buildings, MSc thesis, Technische Universiteit Eindhoven PAGE 14

15 Strategy development challenges Lack of information Focus on the potential Use of current CBPS programs Source code modifications are often necessary Decision making techniques are usually required Conflicting targets, multiple stakeholders PAGE 15

17 Development of simplified models State of the art models Detailed complex models Require high expertise, computationally expensive In some situations, simpler models are more appropriate Use state-of-the-art models to produce/calibrate/validate a user-friendly and/or computationally inexpensive model Applications Regulatory purposes Building control Early design, commercial purposes, PAGE 17

18 Simplified model example 1 Simplified model for Brazilian energy regulation Melo, A. P. 2012, Development Of A Method To Predict Building Energy Consumption Through An Artificial Neural Network Approach, PhD thesis, UFSC, Brazil. PAGE 18

19 Simplified model example 2 Solar Chimney sizing tool Q harvest = glazed area (MWh/year) Hensen, J. L. M., Costola, D., & Trcka, M. 2012, Earth, Wind and Fire project. Final report - activities carried out by the computational building performance simulation group, PAGE 19

20 Simplified models challenges Define scope and goals of the model Assumptions regarding the input, uncertainty Define the necessary accuracy for the model Simple enough, but not too simple Managing large dataset, automate simulations Statistical analysis PAGE 20

22 Government policy setting Evaluation of certain measures on the whole building stock In-depth knowledge about the current situation Assessment of different scenarios / policies Carrot or the stick? PAGE 22

23 Government policy example Climate change adaptation for the Dutch housing stock PAGE 23

24 Government policy challenges Proper information about the building stock Role of occupant behavior Managing large dataset, automate simulations, statistical analysis Robustness of solutions/conclusions Synthesis of results, recommendations PAGE 24

25 7S9X0 Environmental Performance of Buildings TU/e Dept. of the Built Environment Building Performance group Environmental Performance of Buildings 7S9X0 TU/e TU/e Building Building Performance Group Group

26 Monitoring passive house renovation PAGE 26 Environmental Performance of Buildings 7S9X0 TU/e TU/e Building Building Performance Group Group

27 PAGE 27

28 Why? - Design versus use Monitoring? - Energy (warm water, heating) - Indoor climate (temperature, air quality) - Operation building systems - Effect different design solution façade - Effect user behavior How? - Measurement in 10 dwellings for a period of 2 year - Two types (2x5 dwellings) PAGE 28

29 Example At least 30 sensors per dwelling [3 minute values] Monitor set-up (Type I) PAGE 29

30 Monitor set-up (Type I) Raam contact PAGE 30

31 Weather 2013 (month average 3 locations) Grouping of information: 3 weather stations, one year data, four parameters PAGE 31

32 Example result weather Example carpot plot for quick overview of data PAGE 32

33 Energy use 10 dwellings (gas [m 3 ]) PAGE 33

34 Energy use 10 dwellings (electricity [kwh]) PAGE 34

35 Energy use Variation all dwellings district (2008/2012) Type 505 Type 506 Type 505 Type Gas[m 3 ] Electricity [kwh] Boxplots for analysis spread of results PAGE 35

36 T + RH in psychrometric graph; example criteria (PHPP). (adapted from Climate evaluation chart [Martens, M climate risk assessment in museums. PhD thesis, Eindhoven University of Technology]) Indoor environment (one dwelling living room) PAGE 36

37 Indoor environment (one dwelling sleeping rooms) PAGE 37

38 Percentage Exceedance 25.5 o C (red) Undershoot 16 o C (blue) Woning-ruimte Totaal Winter Lente Zomer Herfst H0100-lr 2% 0% 0% 9% 0% H0100-br1 7% 0% 3% 25% 0% Binnenmilieu H0100-br2 8% 1% 4% 30% 0% H0200-lr 8% 0% 2% 30% 0% H0200-br1 8% 0% 3% 28% 0% H0200-br2 6% 0% 2% 19% 0% H0300-lr 8% 0% 0% 31% 0% H0300-br1 12% 0% 1% 45% 0% H0300-br2 11% 1% 1% 41% 0% H0400-lr 2% 1% 0% 7% 0% H0400-br1 5%/-1% 2% 2% 18% 0% H0400-br2 6% 0% 0% 24% 0% H0500-lr 5% 0% 0% 21% 0% H0500-br1 8% 0% 3% 30% 0% H0500-br2 0% 0% 0% 0% 0% H0600-lr 4% 0% 0% 17% 0% H0600-br1 7% 0% 1% 26% 0% H0600-br2 10% 0% 2% 37% 0% H0700-lr 2% 0% 0% 9% 0% H0700-br1 5% 0% 0% 18% 0% H0700-br2 12% 0% 3% 45% 0% H0800-lr 2% 0% 0% 8% 0% H0800-br1 7% 0% 1% 27% 0% H0800-br2 8% 0% 1% 32% 0% H0900-lr 6% 0% 1% 22% 0% H0900-br1 9% 0% 2% 33% 0% H0900-br2 7% 0% 1% 26% 0% H1000-lr 4% 0% 0% 15% 0% H1000-br1 13% 0% 4% 48% 0% H1000-br2 10% 0% 3% 36% PAGE 38 0%

39 Example result indoor climate Living room (left; temperature, relative humidity, CO 2 concentration, window use) Sleeping room (right; temperature, relative humidity, CO 2 concentration, window use) PAGE 39

40 Indoor environment Combining information in one graph to assess potential correlations PAGE 40

41 Indoor environment PAGE 41

42 Indoor environment CO 2 concentration (averaged number of hours per day concentration within indicated band width (left: Jan-Feb 2013; right: Jul-Aug 2013) NOTE: these values are not corrected, therefore only comparison winter summer is of interest (absolute # of hours generally are too high) PAGE 42

43 closed open Ventilation Jan-Dec PAGE 43

44 Histogram to group information Ventilation system PAGE 44

45 Combine information Room heating PAGE 45

46 Domestic hot tapwater DHW PAGE 46

47 Gas use as a function of the monthly mean outdoor temperature Same type PAGE 47

48 Electricity use as a function of the monthly mean outdoor temperature PAGE 48

49 Specific gas use as a function of the monthly mean living room temperature (Jan-Mar) PAGE 49

50 Degree days weighted specific gas use as a function of the monthly mean living room temperature (Jan-Mar) PAGE 50

51 Topic Summing up Different ways of visualizing data Generally interesting to compare datasets (i.e. not only time series plots) PAGE 51

52 Remarks for results shown Data obtained for 2013 Specific: H0900 (506) gas use monitored since end of March (Jan-Mar estimated based on average daily use; April not complete) H0500 (506) hourly indoor environment data not available first three weeks January H0200 (506) indoor temperature bed room 2 apparent offset of ~10K. Local weather station since March (KNMI data for Jan and Feb; in part analysis KNMI data) Hourly data is presented from 1 st day of month at 0:00: average 0:00-0:30 Remaining hours: average between (x-1):30-x:30 Last day of month at 24:00: average 23:30-24:00 Airing habits rooms: window opening [0: open 1:closed] Airing habits remainder: window opening kitchen, attic + fan position PAGE 52

53 Explanation shown results Assumptions: Gas conversion: 1 m 3 = 9.77 kwh; Efficiency boiler DHW = 0.7; Efficiency boiler space heating = 1.0; Gas use cooking = 40m 3 per year; Minimum volume DHW tapping to determine average temperature difference: 1 liter Energy use DHW based on actually measured T PAGE 53

54 Accuracy: Explanation shown results Indoor temperature: ±0.35K Relative humidity: ±2.5% CO 2 -concentration: ± (60 + 3% reading) ppm; (CO 2 sensors were compared to reference sensor and calibration gas (500 ppm/2000 ppm ±5%)) DHW flow meter: 1 pulse = m 3 (±5% <3 l/h; ±2% >5 l/h) kwh meter: 1 pulse = kwh (±1.6%) Temperature sensors: ±0.25K Energy use DHW: ±6% (reference values: T=45 K; flow=2.5 l/h) Energy use heating: ±10% [Bril, C Samenvatting Foutenanalyse ] Function f form: Error in f: PAGE 54