peak water demand study - aceee...peak water demand study: development of metrics and method for...
TRANSCRIPT
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
50