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Diurnal Water Use & Implications for Master Planning
Diurnal Water Use & Implications for Master Planning
Michigan Section AWWA Annual Conference
August 13, 2010
Janice Skadsen
Michigan Section AWWA Annual Conference
August 13, 2010
Janice Skadsen
Co-AuthorsCo-Authors
Molly Wade, City of Ann Arbor
Pete Perala, City of Ann Arbor (retired)
Stan Plante, CDM
Henry Fan, CDM
Mark TenBroek, CDM
Goals of the Master PlanGoals of the Master Plan
IMPROVE city’s capacity to predict flow and pressure in existing distribution system.
DETERMINE system improvements needed to meet current and projected water demands
PRIORITIZE capital improvement projects that will sustain reliable water distribution into the future
Water Master Plan ProjectWater Master Plan Project
Data Collected
– Collect detailed diurnal and seasonal water use patterns for different types of customers
Data Purpose
– Use patterns in hydraulic model (InfoWater) to provide more realistic water demands
City of Ann Arbor StatisticsCity of Ann Arbor Statistics
Service area about 50 square miles
Population about 115,000
5 pressure districts
About 27,000 meters
All pipe InfoWater hydraulic model
Automatic Data Readers (AMR)Automatic Data Readers (AMR)
Installed in 2004 to:
– Reduce FTEs for manual meter reading
– Reduce workman’s comp claims
– Improve data information and timeliness
– Improve customer service
Provides real-time detailed data
– Collect data twice per day
Cost $6.9M for approximately 27,000 meters
AMR Pattern ApproachAMR Pattern Approach
Reprogrammed 100 meters:
– Used 30 minute data collection intervals
– Meters selected to represent a range of user types
– Data collection between February, 2009 and April, 2009
– Data collection completed September, 2009
Processed data:
– Develop weekly patterns
0
5
10
15
20
25
30
Flo
w (m
gd
)
Ann Arbor WTP Production
Average Maximum Minimum
AMR Data PatternsAMR Data Patterns
Residential patterns:
– Consistent
– Outdoor Waterers
– Irrigation only meter
– Snowbird (not sampled)
Small commercial patterns
Large user patterns
Irrigation & outdoor waterer patterns
0 24 48 72 96 120 144 1680.0
0.5
1.0
1.5
2.0
2.5
3.0Residential Consistent Pattern
Hours
No
rma
lize
d W
ate
r U
se
Sample size = 22Sunday Saturday
Sample size = 6
More peaks than consistent userPattern is average over May-AugustApply this pattern to summer months
Use constant pattern for winter months
0 24 48 72 96 120 144 1680.0
0.5
1.0
1.5
2.0
2.5
3.0
Residential Outdoor Waterer Pattern
Hour
No
rma
lize
d W
ate
r U
se
Sunday Saturday
0 24 48 72 96 120 144 1680.0
5.0
10.0
15.0
20.0
25.0
30.0
Dedicated Irrigation Summer/Max Pattern
Hours
No
rma
lize
d U
sa
ge
Additional Residential PatternsAdditional Residential Patterns
Snowbird:
– No samples showing reduced winter use
– Recommend consistent residential
AMR Data PatternsAMR Data Patterns
Residential patterns
Small commercial patterns
Large user patterns
Irrigation & outdoor waterer pattern
Pattern comparisons
0 24 48 72 96 120 144 1680.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Commercial Restaurant Average Pattern
Hours
No
rma
lize
d W
ate
r U
se
Sample size = 4
0 24 48 72 96 120 144 1680.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Commercial Multi-Family Housing Pattern
Hours
No
rma
lize
d W
ate
r U
se
Sample size = 8
0 24 48 72 96 120 144 1680.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Commercial Office Pattern
Hours
No
rma
lize
d W
ate
r U
se
Sample size = 5
0 24 48 72 96 120 144 1680.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Commercial Retail Pattern
Hours
No
rma
lize
d W
ate
r U
se
Sample size = 4
AMR Data PatternsAMR Data Patterns
Residential patterns
Small commercial patterns
Large user patterns
Irrigation & outdoor waterer pattern
Pattern comparisons
Demand Distribution - Largest 200 UsersDemand Distribution - Largest 200 Users
1 10 19 28 37 46 55 64 73 82 91 1001091181271361451541631721811901990
100
200
300
400
500
600
700
800
Usage Ranking
Av
era
ge
Us
ag
e (
GP
M)
39% of total system demand
27% from Top 50 users
Large UsersLarge Users User types:
– 12 Campus (Univ. of Michigan, community college)
– 12 Medical (2 major hospitals)
– 7 Student Housing
– 3 Hotels
– 2 U of M Power Plant connections
– 2 Wholesale Customers
– 1 Retirement Home
– 1 Office
– 1 Unique (mixed commercial /residential)
0 24 48 72 96 120 144 1680.0
1.0
2.0
3.0
4.0
5.0
6.0
Campus
U/M SCHOOL OF PUBLIC HEALTH
U/M MICHIGAN UNION
U/ M PALMER DRIVE COMMONS
U/M LAUNDRY NC
U/M ELECTRICAL ENG & COMPUTER SCEINCE
U/M MASON HALL
U/M EAST HALL
U/M LITERATURE SCIENCE AND THE ART
U/M INTRAMURAL SPORTS BLDG
U/M LURIE ENGINEERING
U/M (Chemistry Building)
(WCC)
Hours
No
rma
lize
d w
ate
r u
se
0 24 48 72 96 120 144 1680.0
1.0
2.0
3.0
4.0
5.0
6.0
Campus - representative
Hours
No
rma
lize
d w
ate
r u
se
Sample size = 11
0 24 48 72 96 120 144 1680.0
1.0
2.0
3.0
4.0
5.0
6.0
Medical
U/M CANCER & GERIATRIC
U/M UNIVERSITY HOSPITAL
U/M UNIVERSITY HOSPITAL
U/M MEDICAL SCIENCE II
U/ M BIOMEDICAL SCIENCE RESEARCH BLDG
VETERANS ADMIN HOSP
U OF M/ CARDIO VASCULAR BLDG
U/M MEDICAL SCIENCE III
U OF M CANCER & GERIATRIC
U/M DENTAL/KELLOGG
U/M MEDICAL SCIENCE III
U/M DENTAL/KELLOGG
Hours
No
rma
lize
d w
ate
r u
se
0 24 48 72 96 120 144 1680.0
1.0
2.0
3.0
4.0
5.0
6.0
Medical - representative
Hours
No
rma
lize
d w
ate
r u
se
Sample size = 12
0 24 48 72 96 120 144 1680.0
1.0
2.0
3.0
4.0
5.0
6.0
Student U/M BURSLEY HALL
HURON TOWERS APTS
U/M MARY MARKLEY
U/M NORTHWOOD V APTS 2701
U/M WEST QUADRANGLE
U/M VERA BAITS II COMAN HSE
U/M SOUTH QUAD
Hours
No
rma
lize
d w
ate
r u
se
0 24 48 72 96 120 144 1680.0
1.0
2.0
3.0
4.0
5.0
6.0
Student - representative
Commercial Multi-Family Average pattern
Student - straight average pattern
Hours
No
rma
lize
d w
ate
r u
se
Sample size = 7
0 24 48 72 96 120 144 1680.0
1.0
2.0
3.0
4.0
5.0
6.0
Hotel
WEBERS INN
DALHMAN APTS LTD (Campus Inn)
WINSTON HOSPITALTY (Marriott)
Hours
No
rma
lize
d w
ate
r u
se
0 24 48 72 96 120 144 1680.0
1.0
2.0
3.0
4.0
5.0
6.0
Hotel - representative
Hours
No
rma
lize
d w
ate
r u
se
Sample size = 3
0 24 48 72 96 120 144 1680.00
1.00
2.00
3.00
4.00
5.00
6.00
Retirement Homes
Hours
No
rma
lize
d w
ate
r u
se
Sample size = 1
Retirement Homes ApproachRetirement Homes Approach Assume two types of use:
– Assisted living:Recommend using monitored pattern
– Retirement community:Recommend using multi-family pattern
0 24 48 72 96 120 144 1680.0
1.0
2.0
3.0
4.0
5.0
6.0
Scio Township
SCIO TOWNSHIP
SCIO TOWNSHIP
Hours
No
rma
lize
d w
ate
r u
se
Scio ApproachScio Approach
Monitored pattern reflects tank operation
Composite demands unknown
Recommend using Ann Arbor’s composite pattern
0 24 48 72 96 120 144 1680.0
1.0
2.0
3.0
4.0
5.0
6.0
Large User Average Pattern
Hour
No
rma
lize
d W
ate
r U
se
AMR Data PatternsAMR Data Patterns
Residential patterns
Small commercial patterns
Large user patterns
Irrigation & outdoor waterer pattern
Pattern comparisons
Seasonal PatternsSeasonal Patterns
Criteria:
– Consider all AMR data for 2 years
–Standard deviation > 40% of monthly
averages
–Summer use (May – August) > rest of year
(Sept, Oct, Mar & Apr)
Seasonal Water wUsersSeasonal Water wUsers
20% of residential
22% of small commercial
0% of large users
Irrigation only meters (746 accounts, 4 large user) – develop generated pattern due to lack of data
0 24 48 72 96 120 144 1680
1
2
3
4
5
6
7
Hours
No
rmal
ized
Wat
er U
se
AMR Data PatternsAMR Data Patterns
Residential patterns
Small commercial patterns
Large user patterns
Irrigation & outdoor waterer pattern
Pattern comparisons
– Average day
– Non-summer day
– Summer max day
– Max day
Existing diurnal pattern
Year Round Non-Summer Summer Max Max Day0
5
10
15
20
25
30
35
Demands by User Type
TOP 200Small Commercial - AverageCommercial RetailCommercial RestaurantCommercial OfficeCommercial Multi-FamilyCommercial IrrigationResidential Outdoor WaterersResidential ConsistentUFW
Sy
ste
m D
em
an
d (
MG
D)
1.5~2.0
0 11 22 33 44 55 66 77 88 99 110 121 132 143 154 1650
10
20
30
40
50
60
Annual Average Patterns
TOP 200Small Commercial - AverageCommercial RetailCommercial RestaurantCommercial OfficeCommercial Multi-FamilyCommercial IrrigationResidential Outdoor WaterersResidential ConsistentUFW
Sy
ste
m D
em
an
d (
MG
D)
14.8
0 11 22 33 44 55 66 77 88 99 110 121 132 143 154 1650
10
20
30
40
50
60
Non-Summer Patterns
TOP 200Small Commercial - AverageCommercial RetailCommercial RestaurantCommercial OfficeCommercial Multi-FamilyCommercial IrrigationResidential Outdoor WaterersResidential ConsistentUFW
Sy
ste
m D
em
an
d (
MG
D)
13.3
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 1600
10
20
30
40
50
60
Summer Patterns
TOP 200Small Commercial - AverageCommercial RetailCommercial RestaurantCommercial OfficeCommercial Multi-FamilyCommercial IrrigationResidential Outdoor WaterersResidential ConsistentUFW
Sy
ste
m D
em
an
d (
MG
D)
20.8
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 1600
10
20
30
40
50
60
Max Day Patterns
TOP 200Small Commercial - AverageCommercial RetailCommercial RestaurantCommercial OfficeCommercial Multi-FamilyCommercial IrrigationResidential Outdoor WaterersResidential ConsistentUFW
Sy
ste
m D
em
an
d (
MG
D)
30.3
0 24 48 72 96 120 144 1680
10
20
30
40
50
60
Diurnal Pattern Comparison – Average Day
Year RoundYear Round - Original Diurnal
Sy
ste
m D
em
an
d (
MG
D)
0 24 48 72 96 120 144 1680
10
20
30
40
50
60
Diurnal Pattern Comparison – Max Day
Max DayMax Day - Original Diurnal
Sy
ste
m D
em
an
d (
MG
D)
0 24 48 72 96 120 144 1680
0.5
1
1.5
2
2.5
3Comparison of Diurnal
Demands in ModelUFWResidential ConsistentResidential Outdoor WaterersSmall CommercialTOP 200Original Diurnal
No
rma
lize
d W
ate
r U
se
BenefitsBenefits
Higher peaks and lower minimums observed versus typical assumptions
Improved understanding of water use, particularly local conveyance
Effort minimal to reprogram and collect data, but some effort to analyze
Data collection limited by volunteer participation & battery life
RecommendationsRecommendations
Consider developing residential user classes
– Consistent year-round use
– Summer waterer with increased summer peaks
Use large user flows and patterns directly where available
Consider a variety of commercial and small industrial patterns where possible
Recommended