Estimating Commercial, Industrial, and Institutional Water Use Based on Heated Building Area

Transcription

1 Estimating Commercial, Industrial, and Institutional Water Use Based on Heated Building Area M. Morales, J. Heaney, K. Friedman, and J. Martin Presenter: Miguel A. Morales Conserve Florida Water Clearinghouse University of Florida AWWA Water Conservation Symposium March 16, 2011

2 Conserve Florida Water Clearinghouse Developing model to serve as water conservation planning tool (EZ Guide 2) Estimates water use within a water budget Evaluates conservation best management practices

3 Macro to Nano-Scale Evaluation of Urban Water Use is Feasible in Florida Total Parcels 8,807,768 Parcels GRU 55,551 Parcels Alachua 99,305

4 Significance of CII Water Use The USGS estimates that CII water use in Florida is at least 23% of total public supplied 29% nationwide Dziegielewski et al. (2000) estimate CII to be 15 to 25% of public water supplied Conclusion: CII water use is significant!

5 Estimating Water Use Water use is the product of rate of water use multiplied by a measure of its size k k Q Total x n k 1 Where: Q Total = water use for n sectors α k = water use coefficient of sector k x k = size of sector k n = number of sectors For projecting water use of future customers, utilities often rely on: similar customers within their service area water use coefficients developed through studies in other locales

6 Popular CII Water Use Coefficients Number of employees is a popular measure of size given its historical availability: used by IWR-MAIN and Maddaus DSS Model These coefficients are outdated, and IWR-MAIN is no longer supported Other measures of size depend on the type of facility Databases for these measures of size are lacking

7 Employment Data Available from: U.S. Census Pros: available throughout the United States Cons: Limited by spatial and customer classification aggregation required to ensure anonymity (Traffic Analysis Zone (TAZ), 2-digit NAICS employment size classes) Private Surveys Pros: Produce customer-level data Cons: Expensive to conduct and only provide a snapshot in time

8 Why Employment Data? Other measures of size, such as building area, have been shown to be better predictors of water use across the CII subsectors (Dziegielewski et al. 2000) Reasoning: Employee data has historically been more available compared to other parameters such as acreage (Mercer and Morgan 1974) Source Available U.S. Economic Census Every 5 years City Smallest geographical unit County Business Patterns Annually Zip code Longitudinal Employer- Household Dynamics Quarterly TAZ Commercial surveys Varies Customer 8

9 Improved Water Use Coefficients Measures of size need to be: Good predictor of water use Consistently defined across CII subsectors and available at a finer spatial resolution Same measure of size for all CII subsectors allows for: Comparisons of water use across subsectors More readily available databases Physical and economic property data available for every parcel in the state of Florida through: Florida Department of Revenue (FDOR) and Florida County Property Appraisers (FCPA) 9

10 FDOR and FCPA Databases FCPA Parcel and building fields vary between counties Obtained individually from county Partitions parcels into 100 FDOR land use categories Provides building heated area FDOR Standard data fields Free and easily accessible Partitions parcels into 100 land use categories Spatial location and dimensions of every parcel in the state Provides building effective area

11 Levels of Spatial Aggregation in Florida Unit Value Population/Unit Population 18,800,000 1 Parcels 8,800, Census Blocks 362, Traffic Analysis Zones (TAZs) 12,747 1,475 Utilities 2,623 7,167 Counties ,597 11

12 Parcel-level Data: Finer Spatial Resolution than TAZ or Census Blocks TAZ Parcel-level Data

13 Standardized State Classification of Customers into 64 Subsectors Urban Water Use (n = 64) Residential (n = 9) CII (n = 55) Single Family (n = 4) Multi-family (n = 5) Commercial (n = 28) Industrial (n = 11) Institutional (n = 16) FDOR 01-02, FDOR 03, 06-08, 28 FDOR 11-27, FDOR 41-49, FDOR 71-79, 81-85, 90, 97

14 Jan-03 May-03 Sep-03 Jan-04 May-04 Sep-04 Jan-05 May-05 Sep-05 Jan-06 May-06 Sep-06 Jan-07 May-07 Sep-07 Jan-08 May-08 Sep-08 Jan-09 May-09 Sep-09 Water Use (MGD) Utility Benchmark CII Water Billing and Property Attribute Data Utility benchmark water use and heated area data supplied by Hillsborough County Water Resources Services (HCWRS ), and Gainesville Regional Utilities (GRU ) Corresponding FCPA CII data obtained Total benchmark of 3,172 CII parcels out of a total of 432,000 CII parcels in state of Florida Years of billing HCWRS GRU CII parcels 1,770 1,402 Commercial 67% 72% Industrial 9% 10% Institutional 24% 18% Commercial Industrial Institutional 14

15 Heated Area (sf) Relationship of Effective Area to Heated Area for 3,172 Benchmark CII Parcels FDOR database more readily available than FCPA, but only presents effective area (EA) 1,200,000 1,000, , , , ,000 0 Effective Area (sf) y = x R² = Strong relationship between EA and heated area (HA) for all 3,172 CII parcels allows for reliable conversion between these two measures of size 15

16 Relationship of Heated Area to Water Use Strong correlation between heated area and water use for 3,172 CII parcels in HCWRS and GRU: Correlation Coefficient, r Heated area (sf) Effective area (sf) Parcel area (acres) Step information of stepwise regression: Effective year built Heated area (sf) Effective area (sf) Parcel area (acres) Effective year built Average monthly water use (gal) Average monthly water use (gal) Multiple R R Adjusted Std. error of R 2 estimate Heated area (sf) Effective year built Parcel area (acres) Indicates little predictive power is gained by addition of other variables 16

17 Developing Water Use Coefficients Based on Heated Area The FDOR and FCPA databases linked with customer billing data for 3,172 CII establishments to develop water use coefficients Coefficient: water use/heated sq. feet Two coefficients presented: average and May peak May is the peak month use for most water utilities in Florida Coefficients based on: Utility monthly water use data Customer classification via FDOR land use code Heated area from FCPA

18 Utility Can Compare Their CII Water Use to Statewide Average for FL Utility Benchmarks Statewide Application FDOR Code Description Sample Size HA EA q j ( average gallons/ heated ft 2 /day) q p (peak gallons/ heated ft 2 /day) Peak Avg. Ratio State Parcel Count State Total Heated Area (acres) State Total Water Use (MGD) % CII Heated Area in State % of CII Water Use in State 11 Stores, One-Story ,049 6, % 5.85% 16 Community Shopping Centers ,164 6, % 6.30% 17 Office, One-Story ,400 4, % 5.01% 18 Office, Multi-Story ,311 7, % 4.86% 19 Medical Office ,976 2, % 4.10% 21 Restaurant , % 5.57% 22 Fast-Food Restaurants , % 1.99% 23 Financial Institutions , % 2.73% 27 Auto Sales / Repair ,807 2, % 2.80% 39 Hotels / Motels ,633 5, % 12.56% Other Commercial ,935 10, % 11.90% Total Commercial 2, ,881 48, % 63.67% 41 Light Manufacturing ,109 6, % 3.21% 48 Warehousing / Distribution ,419 18, % 5.96% 49 Open Storage ,589 2, % 4.06% Other Industrial ,147 3, % 3.71% Total Industrial ,264 30, % 16.93% 71 Churches ,275 4, % 2.09% 74 Homes for the Aged ,898 3, % 3.06% 83 Public County Schools ,685 7, % 5.10% Other Institutional ,995 8, % 9.14% Total Institutional ,853 23, % 19.39% Total CII 3, , , % %

19 Weighted Average Coefficient Aggregation of CII Sectors Sector HA/EA Weighted Average Water Use Coef. (gal/hsf /mo) Number of Parcels Total Effective Area (sf) Avg. Heated Area (sf) Total Heated Area (sf) % of Total Heated Area in Sector Avg. Monthly Water Use (gal) Total Monthly Water Use (MG) % of Total Water Use in Sector Commercial ,552,417 9,210 8,141, % 40, % Industrial ,867,627 16,909 6,729, % 15, % Institutional ,288,555 14,940 4,003, % 87, % TOTAL CII ,550 20,708,599 12,177 18,875, % 42, % Commercial Subsectors HA/EA Weighted Average Water Use Coef. (gal/hsf /mo) Number of Parcels Total Effective Area (sf) Avg. Heated Area (sf) Total Heated Area (sf) Stores, One-Story ,047,601 4, ,933 Mixed Use ,378 1,447 66,559 Department Stores ,808, ,791 1,747,080 Supermarkets / Convenience Stores ,650 21,655 43,311 Fast-Food Restaurants ,711 2,496 57,405 Financial Institutions ,445 3,759 67,664 Auto Sales / Repair ,638 8, ,670 TOTAL COMMERCIAL ,552,417 9,210 8,141,215 A weighted average of the coefficients is carried out based on the total area of the two-digit FDOR subsectors Coefficients are directly dependent on the land use mix within a given service area boundary

20 Projecting Water Use FDOR includes year built for each parcel in the state Allows for time series projections based on: Number of accounts Measures of size (average building heated area) Water use Great improvement over past projecting methods Offers subsector projections Based on all constructed parcels Able to link with population projections

21 Frequency of Parcel Construction Projection Example (FDOR 16) Age Group FDOR Community Shopping Centers Average Average Weighted Average Sample Heated Effective Water Use Coef. Size Area Year Built (gal/heated ft (ft 2 ) 2 /d) Pre , , Post , Total , Time series trends for community shopping centers (FDOR 16): Average heated area is increasing Water use per ft 2 of heated area is increasing At the State level, approximately 250 parcels are built each year

22 Estimating End Uses A true benefit-cost analysis of water conservation best management practices (BMPs) requires: End use inventory of water using devices Based on FL plumbing and building codes Their water use efficiency Based on fixture service life and FL plumbing code Divided into three efficiency age groups (Pre-1983, , and 1995-present ) Their frequency of use Based on functional population and the literature

23 Estimating Restroom Fixture Water Use Functional population Frequency of fixture use Fixture efficiency Fixture count functional ft population 2 person uses * day gallons use ft 2 fixtures gallons fixture * day From FL-specific impact fee studies From national studies on residential frequency of use From FL plumbing and building codes

24 Total water saved (MGD) Marginal cost of water saved ($/kgal) Optimization and the Evaluation of CII BMPs By knowing cost and water use efficiency of retrofits, BMP performance and marginal cost functions can be produced Example: Utility in South Florida doing CII toilet retrofits (0.8 gpf at $300/retrofit) Save an average of 43 gal/toilet/d Save an average of 266 gal/toilet/d Save an average of 5 gal/toilet/d ,000 1,500 2,000 Number of toilets retrofitted Total water saved (MGD)

25 Conclusions The availability of the FDOR database and customer water billing data provides a major improvement in our ability to estimate and project CII water use The Conserve Florida Water Clearinghouse has developed these water use coefficients and heated area statistics available at Methodology allows for: Water budget Projection of water use End-use analysis and BMP optimization Heated area as a measure of size allows for application to other property appraiser databases outside the state of Florida

26 More Information Morales, M., Heaney, J., Friedman, K., Martin, J Estimating Commercial, Industrial, and Institutional Water Use Based on Heated Building Area. Journal of American Water Works Association, accepted for publication. Friedman, K., Heaney, J., Morales, M., Palenchar, J Water Demand Management Optimization Methodology. Journal of American Water Works Association, accepted for publication.

27 Optimization and Evaluation of CII BMPs Parcel-level estimates on the number of end use devices, their water use efficiency, and frequency of use: Allows for the calculation of water saved through retrofits Only changing variable is water use efficiency Number of devices and frequency of use remains the same Knowing water saved in retrofit, along with cost of retrofit allows for the calculation of the marginal cost of water saved A measure of the cost-effectiveness of water conservation BMPs For any given device, the least efficient fixtures with higher usage rates are the most cost-effective to retrofit BMPs can be ranked in order of cost-effectiveness to optimize targeting of customers for water conservation