Parametric Analysis to Support the Integrated Design and Performance Modeling of Net-Zero Energy Houses

William O’Brien1, Student Member ASHRAE; Andreas Athienitis1, PhD, P.Eng., Member ASHRAE; Ted Kesik2, PhD, P.Eng., Member ASHRAE

1Dept of Building, Civil, and Env. Eng., Concordia University, Montréal, Québec, Canada; 2John H. Daniels Faculty of Architecture, Landscape, and Design, University of Toronto; Corresponding author: w_obrie@encs.concordia.ca

INTRODUCTIONDetached houses represent 56% of the current housing stock in Canada and 12% ofall Canadian energy use (NRCan 2008). To address this, a tool is being developed tosupport the early stage design of low and net-zero energy solar houses. Thesehouses can have both strong passive solar performance and active solar systems.For example, see the EcoTerra (below) near net-zero energy house, located nearMontreal, Quebec.This work illustrates some of the analytical research that has been performed todevelop a performance model that balances: simplicity, flexibility, accuracy, andperformance.

ACKNOWLEDGEMENTSThis work was funded by the Solar Buildings Research Network - a strategic NSERC (Natural Sciences and Engineering Research Council of Canada) research network. Additional support through a grant-in-aid for William O’Brien from the American Society of Heating, Refrigeration, and Air-Conditioning Engineers is also acknowledged.

REFERENCESNatural Resources Canada (NRCan) (2008). Energy use data handbook tables

(Canada). Office of Energy Efficiency (OEE).

THE MODEL• A generic EnergyPlus model was created and is defined by 30 parameters (all

inputs to the design tool). They are summarized in the table below.

2D PARAMETER SENSITIVITY (INTERACTIONS))

OBJECTIVES

SUBSYSTEM INTERACTIONS

APPROPRIATE MODEL RESOLUTION

EA,B(-1) =RA(-1)B(-1) - RA(+1)B(-1) (1)EA,B(+1) =RA(-1)B(+1) - RA(+1)B(+1) (2)IA,B = 0.5(EA,B(+1) – EA,B(-1)) (3)

• Identify the most critical parameters of the house energy model• Identify and visualize the most significant two-way parameter interactions• Select the most appropriate level of model resolution for select subsystems

No. Abr. Name Definition Min Max Nominal Unit Design Discrete1 IN Infiltration rate Air infiltration rate (constant) 0.025 0.075 0.1 ach 2 IG Internal Gains Internal (sensible) heat gains scheme 1 3 2 Class number1 3 HS Heating Setpoint Minimum temperature to which zones

are controlled during the day (7AM to 10PM)

16 (63) 22 (72) 22 (72) °C (°F)

4 HSN Nighttime Heating Setpoint

Mininimum temperature to which zones are controlled at night (10PM to 7AM)

17 (63)

22 (72) 18 (64) °C (°F)

5 CS Cooling Setpoint Maximum temperature to which zones are controlled during the cooling season

22.5 (73)

27 (81) 26 (79) °C (°F)

6 FA Floor Area Total conditioned floor area (including basement)

100 (1076)

300 (3228)

200 (2152) m2 (sq. ft)

7 ST Stories Number of stories excluding basement 1 2 2 1 8 AR Aspect Ratio Width (oriented nearest to E-W) to

Length (oriented nearest to N-S) ratio 0.5 2 1 1

9 OR Orientation House orientation; Angle between Wall 1 normal and South (+ = CCW)

-45 45 0 degrees

10 WR Wall Resistance Thermal resistance of all above-grade (opaque) walls from surface to surface

4.4 (25)

12 (68.1)

6 (34.1) m2K/W (h·ft²·°F/Btu)

11 CR Ceiling Resistance Thermal resistance of the ceiling from surface to surface

8.8 (50)

15 (85.2)

10 (56.8) m2K/W (h·ft²·°F/Btu)

12 BS Basement Slab Resistance

Thermal resistance of all basement slab (or slab on grade) from surface to surface

1.6 (9.1) 3 (17) 1.6 (9.1) m2K/W

(h·ft²·°F/Btu)

13 BW Basement Wall Resistance

Thermal resistance of all basement wall from surface to surface

3.1 (17.6)

6 (34.1)

3.1 (17.6) m2K/W (h·ft²·°F/Btu)

14 WT1 Window Type 1 Type of window for South-most window(s) 1 5 3 Class number2

15 WT2 Window Type 2 Type of window for East-most window(s) 1 5 3 Class number2

16 WT3 Window Type 3 Type of window for North-most window(s) 1 5 3 Class number2

17 WT4 Window Type 4 Type of window for West-most window(s) 1 5 3 Class number2

18 FT Frame Type Frame type for all windows on house 1 3 2 Class number3 19 WWR1 Window-to-Wall Ratio

1Window-to-wall ratio for South-most window(s)

0.05 0.8 0.4 1

20 WWR2 Window-to-Wall Ratio 2

Window-to-wall ratio for East-most window(s)

0.05 0.5 0.1 1

21 WWR3 Window-to-Wall Ratio 3

Window-to-wall ratio for North-most window(s)

0.05 0.5 0.1 1

22 WWR4 Window-to-Wall Ratio 4

Window-to-wall ratio for West-most window(s)

0.05 0.5 0.1 1

23 CI Air circulation rate Air circulation rate between zones (assumed constant while on); turned on if ∆T>3°C

0400

(847) 200 (423)L/s (CFM)

24 OH Overhang Depth Overhang depth to window height ratio 0.001 0.5 0.3 1 25 BLS Shades close solar

thresholdBlinds/shades are closed if both of these conditions are exceeded

0 1000 (317)

150 (47.5)

W/m2 (Btu/h·ft2)

26 BLT Shades close temperature threshold

15 (59)

40 (104) 20 (68)

°C (°F)

27 TMS Thermal Mass on South zone floor

Thickness of concrete on on South zone floor

0.001 (0.003

0)

0.2 (0.61) 0.1 (0.31)

m (ft)

28 TMV Thermal mass on vertical wall

Thickness of concrete on interior vertical surface

0.001 (0.003

0)

0.2 (0.61) 0.1 (0.31)

m (ft)

29 RT Roof Type Roof typology 1 2 1 Class number4 30 SL Roof Slope Slope of South-most facing roof (0° = flat) 10 60 35 degrees

1 Low, medium, or high internal gains scheme, averaging 550, 850, and 1250 Watts2 Double-glazed, clear, air-filled; double-glazed, clear, argon-filled; double-glazed, low-e, argon-filled; triple-glazed, clear, argon-filled; triple-glazed, low-e, argon-filled3 Vinyl, wood, or Aluminum with a thermal break4 Gable or hip roof

HT

W

L

WWR3

North Zone

South Zone

Basement Zone

South OR

SL

WWR2

WWR1

Hb

Roof Zone

WWR4

Solar

Roof Zone

South Zone

North Zone

Basement Zone

TMS

TMV

Grade

1D PARAMETER SENSITIVITY )

2000

3000

4000

5000

6000

7000

8000

Com

bine

d he

atin

g an

d co

olin

g en

ergy

(kW

h/ye

ar)

• Interactions are not limited tooccurring between parameters, butcan also occur between entiresubsystems.• Subsystems that do not interact at

all can be designed independently.However, subsystems withsubstantial interactions should bedesigned in an integrated mannerbecause the change of a onesubsystem is likely to have asignificant effect on the other(s).The importance of assessing thelevel of interaction is evident inreducing design efforts.

• It is valuable to identify strongly interacting parameters, for these are the onesthat should be manipulated in subsets, rather than individually. For instance,depending on the glazing type, a large window is either advantageous or not,thus indicating the importance of simultaneously designing them.

• In contrast, parameters that show only weak interactions can be manipulatedand optimized independently. For example, the wall and ceiling insulation levelsonly weakly interact.

• To indentify the strength of all two-way interactions, simulations were run toidentify the most significant interactions between all pairs of parameters. For30 parameters, there are 435 two-way interactions (30 choose 2).

• Performance-based interactions are those for which the performance of twosubsystems depend on the design of the other and should be modeledsimultaneously.• Practical interactions are those in which geometrical or other practical constrains

must be considered. If only a practical interaction exits between two subsystemsthey may be modeled independently as long as the designer ensurescompatibility between them.• Thermal interactions indicate that there is significant thermal coupling between

subsystems and should be modeled simultaneously and thermally coupled.

• The results show that controls, operations, and windows are the most significantparameters.• East and west-facing windows have the greatest influence of the windows

because if they are large, they result in higher heating loads and potentialsummertime overheating.• The significance of a parameter indicates the following:o The amount of effort designers should put into selecting its value (for design

parameters) and,o The amount of effort the model developer should put into accurately modeling

it.• However, even if a parameter appears to be insignificant, it could still have strong

interactions (see next section).

Careful consideration should always be given to selecting the appropriate modelresolution at every stage of design, such that accuracy and complexity arebalanced. Two aspects of the model were closely examined: windows, infiltration,

Window Modeling•Window geometry was modeled explicitly, as grouped

windows, and with a multiplier (all with a WWR of 40% andcoincident centroids; see diagrams at right).• All other parameters were at their nominal values.• The grouped windows have a much lower frame to glazing

area ratio, resulting in lower conductance and less shadingfrom frames. However, explicitly defining windows requiressome specific knowledge about window size and more effortto input data. Using the multiplier provides a hybrid of thetwo but eliminates the possibility of studying daylighting,sicne daylight performance is sensitive to windowplacement.• The results (below) show that all three metrics that were

measured are within 5% of each other, indicating thatgrouping windows is suitable for early stage design on thecondition that the windows and frames are highperformance.

Infiltration Modeling• Infiltration plays a significant role in energy performance of houses, yet it is

difficult to predict before they are built is built and measurements are taken.• Two different modeling options were explored, given that the mean air change

rate is known and equal for both.1. Constant rate: air is exchanged with the outside at a fixed rate, or,2. Effective leakage area: infiltration is a function of windspeed and the

temperature difference between indoors and outdoors.• The results (above) indicate only a small difference (5%) in energy use, indicating

that there is little benefit to a higher resolution model, unless the effectiveleakage area is actually known.

• The ranges areintended to beflexible whileensuring that thedesigns meet code.• Non-design

parameters are thosethat would normallybe fixed at thebeginning of design,such as floor area andthe number of stories.• Design parameters

are those that can bemanipulatedthroughout design toenhance performance(energy, indoorenvironmentalquality).• Discrete parameters

can only take on afixed number ofoptions, whereascontinuousparameters can be setto any value withinthe allowable range.

EcoTerra House (left), generic EnergyPlus model isometric view showing key geometric parameters(center), east elevation of model showing thermal mass locations (right)

•Matlab was used to generate EnergyPlus input (idf) files, run simulations, andobtain results for the entire range of allowable parameter values.• The simulated range of combined heating and cooling energy (ideal loads) is

shown in the graph below.

A-1 +1

B(+1)

B(-1)RA(-1)B(+1)

RA(+1)B(-1)

RA(-1)B(-1)

RA(+1)B(+1)

Strength of interactions

0.0

5.0

10.0

15.0

20.0

0

1,000

2,000

3,000

4,000

5,000

6,000

8.8 m^2K/W (R-50) 15 m^2K/W (R-85)

MB

tu

Com

bine

d A

nnua

l Hea

ting

and

Cool

ing

Ener

gy (k

Wh)

A(CR)

B(WR)=4.4 m^2K/W (R-25)

B(WR)=12 m^2K/W (R-68)

Example of weak interaction

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0.05 0.80

MB

tu

Com

bine

d A

nnua

l Hea

ting

and

Cool

ing

Ener

gy (k

Wh)

A(WWR1)

B(GT1)=Triple-glazed, low-e, argon-filled

B(GT1)=Double-glazed, clear, air-filled

Example of a stronginteraction

“Wheel of interactions” showing the 100 strongest two-wayinteractions. The thickness of the lines corresponds to the strength.

• Equations 1-3 were used toquantify interactions. In visualterms, a strong interaction occursif the difference is slopes of thetwo lines is large.

Grouped windows

Explicit windows

Envelope and Base Loads

BIPV

PrPe

Energy EfficiencyMeasures

Passive SolarHeating and

Cooling

Pr

Pe

PrPr

Pe Pr T

Pe Pr T

Pe – Performance InteractionPr – Practical InteractionT – Thermal Interaction

BIPV/T

SolarDHW

Solar Thermal for Space Heating

Venn diagram showing varying degrees ofinteraction between the house’s major subsystems;greater overlap means stronger interaction

Window multiplier(16X)

0

2000

4000

6000

8000

10000

Transmitted Solar

Heating Cooling

Ener

gy (k

Wh/

year

)

Explicit

Multiplier

Grouped

Effect of different window modelingmethods

-

500

1,000

1,500

2,000

2,500

3,000

3,500

Heating Cooling

Ener

gy (k

Wh/

year

)

Fixed rate

Effective Leakage Area

Effect of different infiltrationmodeling methods

Sensitivity plot for the 30 parameters over their entire range