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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. Co-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 Plan.

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Diurnal Water Use & Implications for Master Planning

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  1. Diurnal Water Use & Implications for Master Planning Michigan Section AWWA Annual Conference August 13, 2010 Janice Skadsen

  2. Co-Authors • Molly Wade, City of Ann Arbor • Pete Perala, City of Ann Arbor (retired) • Stan Plante, CDM • Henry Fan, CDM • Mark TenBroek, CDM

  3. Goals 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

  4. Water 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

  5. City of Ann Arbor Statistics • Service area about 50square miles • Population about 115,000 • 5 pressure districts • About 27,000 meters • All pipe InfoWater hydraulic model

  6. Automatic Data Readers (AMR) • Installed in 2004to: • 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

  7. AMR 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

  8. AMR Data Patterns • Residential patterns: • Consistent • Outdoor Waterers • Irrigation only meter • Snowbird (not sampled) • Small commercial patterns • Large user patterns • Irrigation & outdoor waterer patterns

  9. Sunday Saturday Sample size = 22

  10. Sunday Saturday Sample size = 6 More peaks than consistent user Pattern is average over May-August Apply this pattern to summer months Use constant pattern for winter months

  11. Additional Residential Patterns • Snowbird: • No samples showing reduced winter use • Recommend consistent residential

  12. AMR Data Patterns • Residential patterns • Small commercial patterns • Large user patterns • Irrigation & outdoor waterer pattern • Pattern comparisons

  13. Sample size = 4

  14. Sample size = 8

  15. Sample size = 5

  16. Sample size = 4

  17. AMR Data Patterns • Residential patterns • Small commercial patterns • Large user patterns • Irrigation & outdoor waterer pattern • Pattern comparisons

  18. Demand Distribution - Largest 200 Users • 39% of total system demand • 27% from Top 50 users

  19. Large 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)

  20. Sample size = 11

  21. Sample size = 12

  22. Sample size = 7

  23. Sample size = 3

  24. Sample size = 1

  25. Retirement Homes Approach • Assume two types of use: • Assisted living: • Recommend using monitored pattern • Retirement community: • Recommend using multi-family pattern

  26. Scio Approach • Monitored pattern reflects tank operation • Composite demands unknown • Recommend using Ann Arbor’s composite pattern

  27. AMR Data Patterns • Residential patterns • Small commercial patterns • Large user patterns • Irrigation & outdoor waterer pattern • Pattern comparisons

  28. Seasonal 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)

  29. Seasonal 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

  30. AMR 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 Existingdiurnal pattern

  31. 1.5 ~2.0

  32. 14.8

  33. 13.3

  34. 20.8

  35. 30.3

  36. Benefits • 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

  37. Recommendations • 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

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