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Peak Water Demand Study: Development of Metrics and

Method for Estimating Design Flows in Buildings

ACEEE 2016 HOT WATER FORUM: PORTLAND, OR

TORITSEJU OMAGHOMI, STEVEN BUCHBERGER, TIMOTHY WOLFE,

JASON HEWITT, AND DANIEL COLEFEBRUARY 22 ND, 2016

1

Outline

Background

Task Group

Water Use Database

Key Parameters and Considerations

Application

Conclusion

2

BACKGROUND

3

Hunter’s Curve

Today, Hunter’s curve is often faulted for giving overly conservative design….Why?

4Fixture Units

GPM

High efficiency fixtures = lower q

Uncongested use = lower p

1940

2016

Changing Times

5

Modified Hunter’s Curve(s)

6http://www.armstronginternational.com/files/products/wheaters/pdf/sizing%20chart.pdf

Different curve for different end users

TASK GROUP

7

IAPMO Task Group Orders

“….will work singularly to develop the probability model to predict peak demands based on the number of plumbing fixtures of different kinds installed in one system.”

8

(Bring Hunter into 21st Century)

Project Activities

Acquire Data

Analyze Data

Develop Model

Peer Review & Publication of

Report

9

WATER USE DATABASE

10

Database: Location of Homes Over 1000

households

Surveyed between 1996 -2011

2,800 residents

11,350 monitoring days

11

Database: Summary of Measured Data

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Water Use Event Duration Volume

Pe

rce

nta

ge

Bathtub

Clothes washer

Dishwasher

Faucet

Shower

Toilet

Others

Leak

Six unique fixtures

862,900 water use events (excluding leaks)

1,591,700 gallons

889,700 minutes

12

-Evaporative Cooler

Database: Details of Water Use at FixtureFREQUENCY OF FIXTURE USE PER CAPITA PER DAY VOLUME OF WATER USE PER CAPITA

Bathtub5.2%

Clothes washer25.0%

Dishwasher1.9%

Faucet20.2%

Shower21.9%

Toilet25.8%

13

Bathtub0.5%

Clothes washer3.2%

Dishwasher1.5%

Faucet73.5%

Shower2.5%

Toilet18.8%

Database: Residential Fixture Classification

Fixture* Ultra-Efficient Efficient Inefficient

Clothes washer (gallons /load)

< 20 20 – 30 > 30

Dishwasher(gallons/cycle)

< 1.8 1.8 – 3.0 > 3.0

Shower(gallon/minute)

< 2.2 2.2 – 2.5 > 2.5

Toilet(gallon/flush)

< 1.8 1.8 – 2.2 > 2.2

14

*Bathtubs and faucets were excluded

KEY PARAMETERS & CONSIDERATIONS

15

Key Fixture Characteristics

16

Fixture flow rateq:

Fixture Countn:

1 2 3 . . . x . . . . n

1 -

2 -

3 - Fixture Probability of usep:it

pT

Computed Fixture Flow Rate; q

17

( )

( )

Volumeof each pulse gallonsq

Duration of each pulse minutes

Clothes Washer Flow Rate

18

Fixture classification (Percentage of sampled homes)

0

1

2

3

4

5

6

7

Ultra C. (16.9%) Efficient C. (17.2%) Inefficient C. (65.9%)

Flo

w r

ate

(gp

m)

Mean

Outliers

Recommended

Clothes washer efficiency measured in gallons/load

Dishwasher Flow Rate

19

Fixture classification (Percentage of sampled homes)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Ultra D. (24.6%) Efficient D. (48.5%) Inefficient D. (26.9%)

Flo

w r

ate

(gp

m)

MeanOutliersRecommended

Dishwasher efficiency measured in gallons/cycle

Shower Flow Rate

20

Fixture classification (Percentage of sampled homes)

0.0

1.0

2.0

3.0

4.0

5.0

Ultra S. (67.5%) Efficient S. (15.0%) Inefficient S. (17.5%)

Flo

w r

ate

(gp

m)

MeanOutliersRecommended

Shower efficiency measured in gallons/minute

Toilet (Water Closet) Flow Rate

21

Fixture classification (Percentage of sampled homes)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Ultra T. (21.5%) Efficient T. (24.9%) Inefficient T. (53.6%)

Flo

w r

ate

(gp

m)

MeanOutliersRecommended

Toilet efficiency measured in gallons/flush

Bathtub Flow Rate

22

Fixture (Percentage of sampled homes)

0

1

2

3

4

5

6

7

8

9

10

Bathtub (100%)

Flo

w r

ate

(gp

m)

Mean

Outliers

Recommended

Faucet Flow Rate

23

Fixture (Percentage of sampled homes)

Bathroom sink

Kitchen and Laundry sink

0.0

0.5

1.0

1.5

2.0

2.5

Faucet (100%)

Flo

w r

ate

(gp

m)

MeanOutliersRecommended

Summary of “Efficient” Fixture Flow Rate

24“Efficient” = Ultra-efficient and Efficient Fixtures

0

1

2

3

4

5

6

7

8

9

10

Bathtub Clothes Washer Toilet Shower Faucet Dishwasher

Flo

w r

ate

(gp

m)

Mean

Outliers

Recommended

Peak Hour of Fixture Use (by Volume)

25

Clothes washer10

Dishwasher20

Faucet18

Shower07

Toilet07

Bathtub19

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Dai

ly V

olu

me

(ga

llon

s)

Hour of Day

Average hourly volume of water use at a fixture per home per day

Single Home 12 Day Water Use Profile

26

0

50

100

150

200

250

300

350

400

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Vo

lum

e (

gallo

ns)

Hour of Day

Clothes washer

Dishwasher

Faucet

Shower

Toilet

Total Volume

Peak Hour

(10S761)

Fixture peak hour of water use (by Volume)

Bathtub; 19

Clothes washer; 10

Dishwasher; 20

Faucet; 18

Shower; 07

Toilet; 07

0

5

10

15

20

25

30

35

40

45

50

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Vo

lum

e (

gallo

ns)

Hour of Day

Average hourly volume of water consumed at each fixture

Multiple Homes Water Use Profile (by Volume)

27

Home #1;10S761

Home #2; 10S764

Home #3; Cal_Fed-15151

0

5

10

15

20

25

30

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Ave

rage

dai

ly v

olu

me

(ga

llon

s)

Hour of Day

28

2 homes

151 homes

57 homes

0

5

10

15

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Pe

rce

nta

ge (

%)

Hour of Day

Distribution of Peak Hour of Water Use(N = 1,038 Homes)

Peak Hour Probability of Fixture Use

Focused on only efficient fixtures

29

( )

i

i

th

i

t

pn D T

t Duration of i water use event

n Number of fixtures

D Number of observation days

T 60 minutes 1hour observation window

Probability of Fixture Use at Individual Homes

Considered only efficient fixtures30

100.17

1 1 60

minutesp

fixture day minutes

i

i

t

pn D T

Probability of Fixture Use – Group of Homes

31

Number of residents

Number of bathrooms

Number of bedrooms

Positive Correlation

Probability of Shower Use

32

I – Group weighted average

Recommended Value = 0.025

N = 100

N = 365

N = 142

N = 58

N = 141

N = 18

N = 13

147

7669

35

144 5 7 2 1 1 3 1

0

20

40

60

80

100

120

140

160

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12

Fre

qu

en

cy

Probability

Distribution of Shower p values for Homes with 2 residents

33

(N = 365)Recommended value

Summary Probability of Fixture of Use

Each fixture has a unique water use profile.

Probability of fixture use does not dependent on how frequent a fixture used.

Probability of fixture use increased as the number of residents increased.

34

Design Fixture Probability Values

35

p = 0.010

p = 0.005p = 0.025

p = 0.050

APPLICATION

36

Binomial Model

37Hunter did this

exactly busyPr (1 )

out of fixtures

x n xx n

p pn x

*n in a big buildings

“Frequency Factor” Approach

38

0.99x Mean z Standard Deviation

99th Percentile

Normal Approximation of Peak Flow

39

2

0.99 0.99

1 1

1K K

k k k k k k k

k k

Q n p q z n p p q

0.99 0.99q qQ z

- Wistort (1995)

Binomial Distribution

40

*n in a small buildings

Zero Truncated Binomial Distribution (ZTBD)

41

Modified Wistort’s Model

Note:

is probability of stagnation in a home (i.e. no water use)

Addresses water demand in single family homes with high P0

Transitions back to Wistort’s model as P0 approaches 0

42

2

2

0.99 0.99 0 0

1 1 10

11 1

1

K K K

k k k k k k k k k k

k k k

Q n p q z P n p p q P n p qP

0

1

1 k

kn

kP p

2

0.99 0.99

1 1

1K K

k k k k k k k

k k

Q n p q z n p p q

Hypothetical

Residential

Building

Pipe Layout

43

Peak Flow Calculations

SectionUPC FU

UPC Method(gpm)

MW Method(gpm)

1 25.5 18.0 11.8

2 10.5 8.5 6.9

44

Example: 2.5 bath single family home

gpm - gallons per minute

Sizing Pipes

SectionUPC

MethodPipe Size*

MW Method

Pipe Size*

1 18.0 gpm 1” 11.8 gpm 3/4"

2 8.5 gpm 3/4" 6.9 gpm 3/4"

45* Maximum flow rate at 8 f/s

Example: 2.5 bath single family home

Demand Sensitivity to p and q values

46

CONCLUSION

47

Conclusion Introduced a computational model to estimate peak water demand in single and multi-family dwellings.

Recommended fixture p and q values.

Model applicable to a wide spectrum of buildings.

48

Single Family Homes

Questions?

omaghotu@mail.uc.edu

University of Cincinnati

49

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