3. Pre-design applications

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TU /e – B ui ldi ng P er fo rm anc e G Env iro nm ent al P er fo rm anc e o f B ui ldi ng s 7S 9X 0 TU /e – B ui ldi ng P er fo rm anc Env iro nm ent al P er fo rm anc e o f B ui ldi ng s

7S9X0

Environmental Performance of Buildings

TU/e Dept. of the Built Environment

Building Performance group

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TU /e – B ui ldi ng P er fo rm Env iro nm ent al P er fo rm anc e o f B ui ldi ng s TU /e – B ui ldi ng P er fo Env iro nm ent al P er fo rm anc e o f B ui ldi ng s 7S 9X 0

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.

(3)

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TU /e – B ui ldi ng P er fo rm anc e G Env iro nm ent al P er fo rm anc e o f B ui ldi ng s TU /e – B ui ldi ng P er fo rm anc Env iro nm ent al P er fo rm anc e o f B ui ldi ng s 7S 9X 0

Pre-design simulation - examples

• Product development

• Strategy for building concept development

• Development of simplified (or surrogate or meta)

models

• Government policy target setting

(4)

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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

(5)

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Product development example 1

• Smart Energy Glass

(PeerPlus)

-50 -40 -30 -20 -10 0 10 20

Total energy savings Useful daylight illuminance Glare discomfort Overheating hours

1

2

3

4

5 Rel

at

iv

e p

er

fo

rm

an

ce g

ai

ns

[%

]

PAGE 5

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Product development example 1

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Product development example 2

• Low infrared absorptivity coatings

0 50 100 150 200 250 300 350

Normal (n°8) Improved IR reflectivity (n°10)

Pr im a ry e n e rg y c o n s u m p ti o n (M J /m 2 .y )

IR reflectivity of light grey HPL panels

Heating Cooling

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Product development example 2

• Low infrared absorptivity coatings

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815 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

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Pre-design simulation - examples

• Product development

• Strategy for building concept development

• Development of simplified (or surrogate or

meta) models

• Government policy target setting

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815 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

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815 Strategy development example 1

• Virtual Natural Lighting Solutions

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815 Strategy development example 1

Vi

ew

c

om

pl

exi

ty

Light directionality

Complex view,

diffuse

Simplified view,

diffuse

Simplified view,

directional

Complex view,

directional

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815 Strategy development example 2

• Earthscrapers

(15)

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815 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

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Pre-design simulation - examples

• Product development

• Strategy for building concept development

• Development of simplified (or surrogate or

meta) models

• Government policy target setting

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815 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, …

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815 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

(19)

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815 Simplified model example 2

• Solar Chimney sizing tool

Q

_harvest

_{= + 0.345 ∙ glazed area (MWh/year)}

Hensen, J. L. M., Costola, D., & Trcka, M. 2012, Earth, Wind and Fire project. Final report - activities

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815 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

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Pre-design simulation - examples

• Product development

• Strategy for building concept development

• Development of simplified (or surrogate or

meta) models

• Government policy target setting

(22)

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815 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?

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815 Government policy example

• Climate change adaptation for the Dutch housing stock

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815 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

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TU /e – B ui ldi ng P er fo rm anc e G Env iro nm ent al P er fo rm anc e o f B ui ldi ng s 7S 9X 0 TU /e – B ui ldi ng P er fo rm anc Env iro nm ent al P er fo rm anc e o f B ui ldi ng s

7S9X0

Environmental Performance of Buildings

TU/e Dept. of the Built Environment

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TU /e – B ui ldi ng P er fo rm Env iro nm ent al P er fo rm anc e o f B ui ldi ng s 7S 9X 0 TU /e – B ui ldi ng P er fo Env iro nm ent al P er fo rm anc e o f B ui ldi ng s

Monitoring passive house

renovation

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TU /e – B ui ldi ng P er fo rm anc e G Env iro nm ent al P er fo rm anc e o f B ui ldi ng s TU /e – B ui ldi ng P er fo rm anc Env iro nm ent al P er fo rm anc e o f B ui ldi ng s 7S 9X 0 PAGE 27

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Monitoring ?

Why?

- Design versus use

- 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)

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Monitor set-up (Type I)

Example

At least 30 sensors per dwelling

[3 minute values]

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Monitor set-up (Type I)

Raam contact

(31)

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Weather 2013

(month average – 3 locations)

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815 Energy use

Variation all dwellings district (2008/2012)

Gas[m

3 _]

_{Electricity [kWh]}

Type 505

Type 506

Type 505

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TU /e – B ui ldi ng P er fo rm anc Env iro nm ent al P er fo rm anc e o f B ui ldi ng s TU /e – B ui ldi ng P er fo rm Env iro nm ent al P er fo rm anc e o f B ui ldi ng s 7S 9X 0

Binnenmilieu

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% 0%

Percentage

Exceedance 25.5

o

_{C (red)}

Undershoot 16

o

_{C (blue)}

PAGE 38

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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)

2012-03-06 PAGE 43

closed

open

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Ventilation system

PAGE 44

(45)

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Room heating

PAGE 45

Combine information

(46)

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815 Specific gas use

as a function of the monthly mean living room temperature (Jan-Mar)

(50)

7S

815 Degree days weighted specific gas use

as a function of the monthly mean living room temperature (Jan-Mar)

(51)

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Summing up

Topic

–

Different ways of visualizing data

–

Generally interesting to compare datasets

(i.e. not only time series plots)

(52)

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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

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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

(54)

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TU /e – B ui ldi ng P er fo rm Env iro nm ent al P er fo rm anc e o f B ui ldi ng s TU /e – B ui ldi ng P er fo Env iro nm ent al P er fo rm anc e o f B ui ldi ng s 7S 9X 0 PAGE 54