Sustainable Water Management Conference

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Sustainable Water Management ConferenceDenver, Colorado

March 31, 2014

Presentation by Jack C. Kiefer, PhD and Lisa R. Krentz

Methodology for Determining Baseline Commercial,

Institutional, and Industrial End Uses of Water

WRF Project #4375Jack C. Kiefer, PhD

Lisa R. Krentz

Overview• Project goals and objectives

• Background and examples about the research problem

• Elements of emerging methodology

WRF 4375: Goals

• Provide water utilities with better and consistent means of understanding the amount of water used by their CII customers by category and by end use or purpose

• This is methodological development—not direct measurement or estimation of usage rates or end-use metrics or benchmarks!

Methodology for Determining Baseline Commercial, Institutional, and Industrial

(CII) End Uses of Water

WRF 4375: Objectives• Establish data requirements and recommended

practices for:

1. Identifying practical or useful classifications of CII users

2. Evaluating the range of end uses that are present

3. Calculating alternative metrics of water usage rates

Why are methodological improvements needed?

• CII consumption typically 30-50% of utility demands

• Improve effectiveness of water use forecasts

• Easier targeting for water efficiency programs

• Better usage rate metrics for benchmarking

• Information for designing rate structures

Basics of the “CII Problem”

• Heterogeneous sector• Lack of consistent classification• Significant variability in water use between and within

classes of customers• Water use is a “derived demand” that is bundled into

production of goods and services• “Explanatory” data generally lacking

Common and Less Common Classifications Customary CII groupings

Single sector—Nonresidential “catch-all”

Commercial and Industrial—sometimes Institutional/Govt/Public

Classification criteria not always clear

Tied to wastewater charges Phoenix AZ example —

previous system of 34 classes (19 CII)

Linked to external classifications Land use/Building type

► City of Austin example — 49 premise codes (majority nonresidential)

► Florida Department of Revenue — 57 CII categories

NAICS or “NAICS-like”

Heterogeneity and Variability

Within-Class Variability (Lodging example)

Luxury Hotels

Motels

Within-Class Variability (Office Building example)

Tower Type

Example Drivers of End Use Consumption and Related Proxies

End Use Category Demand Drivers Proxies of Scale or Capacity

Cooling (water based) Preferences/Policies Tonnage Cubic Footage Square Footage

Irrigation Irrigated Landscape Area Irrigable Area ET Air Temperature

Sanitary People (Visitors + Employment)

Number of Fixtures

Square Footage

Number of Employees

Process water Production Level Number of Employees

Square Footage

Other/Misc

Pools Preferences/Policies Area ET Air Temperature

Food service Volume of service (meals served) Area Number of

EmployeesSeating capacity

Multiple drivers or proxies of scale

Single water use record not broken into end uses

Differentiating Scale from Rate (or Intensity) of Use

For example:

• Water use in a school = avg. water use per student x students

• Water use in hotel = avg. water use per occupied room x occupied rooms

• Water use for irrigation = avg. water use per irrigated sq. ft. x irrigated area

NqNNQQ

Rate of use Driver

Scale, Intensity, and Sementti

Scatterplot of water use and some measure of scale for a set of customers in a given CII class

Differentiating Scale from Rate (or Intensity) of Use

Q = a + qN

General effects of scale

(e.g., water use increases with employment, production, size of facility)

Scale, Intensity, and SegmentationDifferentiating Scale from Rate

(or Intensity) of Use

q(no irrigation, no cooling)

q(irrigation, no cooling)

q(irrigation, cooling)

Basics of Emerging CII Methodology

• Classify customers along some common definitions and criteria—Vast majority of CII water use and accounts reside in

20-25 categories—End use dominant classes relatively easy to define

Example CII Classification Scheme

1. Lodging2. Office Building3. School or College4. Health Care Facility5. Eating or Drinking Place6. Retail Store7. Warehouse8. Auto/Auto Service9. Religious Building10. Retirement or Nursing

Home11. Manufacturing

12. Other Commercial13. Other Institutional14. Other Industrial15. Landscape only16. Laundromat17. Commercial or Industrial

Laundry18. Car Wash19. Park or Recreational Area20. Golf Course21. Power Plant/Utility22. Server Facility/Data Center

• Unless classification capabilities already exist, linkage to external data is helpful for—Defining NAICS-like categories—Defining customer as a “water-using location”—Assigning customers into classes

• What do tax appraiser or parcel-level data provide?—A useful measure of scale: square-footage—Potential indicator for passive efficiency: year

built and year improved—Lot size, footprint, stories, other—Common spatial identifier to link to other data

sources

• Primary data collection needed for further market segmentation of CII customers (surveys or audits)

• Data collection instrument developed to—Establish fundamental “global” differentiators

▪ Irrigation?▪ Cooling?▪ Alternative supply source(s)?

—Dig deeper into operations▪ Business/property sub-classification▪ Presence of other end uses▪ Employment, visitation, production▪ Hours of operation, etc.

Info Management Methodology

Water Use Billing Data Parcel DataUnique “Location”• Tax Classification•Square footage

Unique “Location”• Tax Classification

•NAICS Classification•Square footage•Employment

External Business Data

Customer Surveys Other External Sources

Unique “Location”• Classification

•Square footage•Employment•Other drivers

•End Uses•Operations

Aggregation by class to support class characterization, forecasting, rate design, etc.

Samples by class to support benchmarking, program design, water budget analysis, etc.

Mean = 0.244

5% 25% 50% 75% 95%

Percentile

Distribution of Hotel Water Use Per Square Foot (n=77)

0 100 200 300 400 500 600 700 800

Apartment Hotel/Motel (n=3)

Hotel (n=22)

Motel (n=12)

Condo or Time Share (n=1)

Other (n=3)

Hotel and Resort (n=4)

Water Use per Room per Day (Annual Average 2006)

Sample Mean = 215 gpd (n=45)

No irrigation, no cooling, no pools

Irrigation, pools, similar amenities

All/enhanced amenities, high occupancy

Cautions• Significant information management

work required—Account geocoding—Multiple meters serving a “location”—Matching of accounts/meters to

parcels▪1 to 1 (good!)▪1 to many/many to 1▪Many to many

Cautions• Significant information management work

required (continued)—Matching of business data

▪ Can cost $$▪ Can have a mismatch between location and corporate data

—Mixed use/multiple business locations

• Primary data collection involves sampling and logistics

Next Steps• Finish “beta-testing” of data collection instrument

• Refine and recommend final methodology

• Develop some illustrative examples

• Project report

Thanks!• Utilities

— City of Austin— East Bay Municipal

Utility District— New York City

Department of Environmental Protection

— City of Boulder— City of Fort Collins— Colorado Springs Utilities— Tampa Bay Water— City of Phoenix

• Other Team Members— Ben Dziegielewski— University of Florida— William (Bill)

Hoffmann• Maureen Hodgins,

Water Research Foundation

Jack C. Kiefer, PhDjkiefer@hazenandsawyer.com

618.889.0498

Sustainable Water Management Conference

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