Tampa Bay Water uses the results of the long-term forecast models for two primary purposes:

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1 AGENDA ITEM H3 DATE: December 2, 2013 TO: FROM: SUBJECT: Matt Jordan, General Manager Charles H. Carden, Chief Operating Officer Annual Demand Forecast Evaluation and Long-term Demand Forecast Update - Presentation SUMMARY: Tampa Bay Water has developed the Long-term Demand Forecasting System for the purpose of providing annual and long-term water demand projections for the seven water demand planning areas of our six member governments. Each year the model forecast is evaluated and verified, and new water demand projections are developed based on updated socioeconomic projections. RECOMMENDATION: Receive presentation. Continue annual updates of agency longterm demand forecasts. DISCUSSION: The annual evaluation of the Agency s long-term demand forecast models and the updated long-term demand forecast are complete. A detailed discussion of the process used to update the water demand projections is contained in the attached technical memorandum, with additional explanation provided in the references cited therein. A summary of results will be presented to the Board at its December 16 meeting. Tampa Bay Water uses the results of the long-term forecast models for two primary purposes: 1. Annual budgeting and source allocation near term projections up to five years. 2. Long range water supply planning forecasting demands for at least a 10 to 20-year time horizon. Demand Forecast Update The total regional demand projected for Water Year 2015, based on the updated set of models, is mgd. The updated projection is consistent with the Water Year 2015 projections made last year, which was 238 mgd. As detailed in the attached technical memorandum, increases in multi-family housing units and employment are outweighing the effects of slow income growth and increasing water pricing resulting in positive growth in regional water demands. Model results for Hillsborough County and City of Tampa water demand planning areas show slight (less than 3%) increases in projected water demands for the 2020 to 2035 time horizon. Updated projections for demographic and socioeconomic variables show that the projected growth rate for the total number of housing units in the Tampa Bay region increased from

2 Matt Jordan December 2, 2013 Page 2 of % per year in the 2011 forecast to 1.16% in the 2012 forecast. The highest growth rates are projected to occur in the City of Tampa and Hillsborough County water service areas. Housing unit growth rates for Pinellas County and the City of St. Petersburg are projected to remain flat. However, current socio-economic projections do not predict a return to the robust local housing market experienced in the timeframe. Updated employment projections suggest lower long-term employment than previous projections but slightly more optimistic projections in the near and mid terms. Employments projections are highest for the City of Tampa. Mean household income levels are projected to continue growing at slower rates than were projected in In addition, the updated water demand projections include reductions in wholesale water delivery through Pinellas County s system. Results are shown in Figure 1 and Table 1. The Water Year 2013 regional demand prediction, based on the updated model, is mgd. Actual demand for this water year was mgd. Projected water demands are based on 30-year normal weather conditions. It is anticipated that next year s evaluation will identify that the cooler, wet spring for 2013 contributed to actual demands being lower than projected. The 2035 regional demand projection is approximately 4 mgd higher than last year s 2035 regional demand projection. This is a slight change (about 1.5%) in the long term demand projection resulting from the updated models. Figure 1. Updated Long-term Demand Forecast for Tampa Bay Water Service Area

3 Matt Jordan December 2, 2013 Page 3 of 5 Table 1 provides actual water use for Water Year 2012 and projected water use for Water Years 2013 through 2035 by member government, in million gallons per day (mgd). The decrease in demand for Pinellas County reflects the reduction in wholesale water delivery from the County to Tarpon Springs, Clearwater and Oldsmar. Table 1. Updated Long-term Demand Forecast by Member Government, mgd Budgeted Water Delivery The updated regional demand forecast is used for estimating how much water Tampa Bay Water should expect to deliver to the Member Governments for the next five years. Tampa Bay Water deducts from the total regional demand forecast the amount of water the City of Tampa, and City of New Port Richey and Pasco County utilities self-supply, given normal hydrologic conditions. Tampa Bay Water is currently allocating an annual planned delivery of six mgd to the City of Tampa to account for uncertainty in Hillsborough River flow. The five year water delivery projections for each member government are shown in Table 2. Table 2. Five Year Budgeted Water Delivery Projection, in mgd Member Governments FY 2015 FY 2016 FY 2017 FY 2018 FY 2019 FY 2020 Pinellas County City of St. Petersburg Hillsborough County City of Tampa Pasco County City of New Port Richey Total Budgeted Delivery Tampa Bay Water s 2015 budget will be based on a budgeted delivery of mgd, assuming normal hydrologic conditions prevail. This is a slight decrease from the 2014

4 Matt Jordan December 2, 2013 Page 4 of 5 budgeted delivery of mgd; which was based on drier than normal hydrologic conditions. Demand Forecast Evaluation Staff and consultants have completed the annual review and evaluation of forecast performance for Water Year The attached technical memorandum also summarizes the evaluation of model forecasts. This is the fourth year the demand forecast models have been evaluated using a consistent process. Evaluation results for the past three years are shown on Table 3. Some highlights of the annual review and evaluation include: Total retail projections for Water Year 2012 were over predicted by 0.8 percent (1.5 mgd), which is well within standard performance bounds on model prediction error. Total regional water demand projection for Water Year 2012, using 2011 base year models, was mgd and actual total regional demand for Water Year 2012 was , a difference of 0.4%. Model forecasts by sector improved (i.e., had less prediction error) for Water Year 2012 as compared to previous forecasts (2009, 2010, 2011). Single-family forecast models perform best of the three sector models (single family, multi-family, non-residential). The primary source of model error is contained within the driver variables (e.g., single family units, multi-family units, employment). Conditions were drier than normal in winter of 2011 and spring of 2012 (December through March) and wetter than normal during the summer of 2012, except for the month of September which was drier than normal. Conditions were cooler than normal in October 2011, but much warmer than normal during winter 2011 and spring 2012, summer months were cooler than normal. Model performance is judged to be adequate; calibration to 2012 actual demands was performed. Re-development of model parameters is scheduled to occur with the re-development of the long term demand models currently underway. Table 3. Predicted versus Actual Retail Water Use for WYs 2010, 2011 and 2012

5 Matt Jordan December 2, 2013 Page 5 of 5 BACKGROUND: The Long-term Demand Forecast Models were re-developed with billing account data through water year 2008, property appraiser data, socio-economic data, and weather data (e.g., rainfall and temperature). An updated long-term demand forecast for the years was developed. In December 2010, the project consultant, Hazen and Sawyer, P.C., completed documentation of the re-developed models and forecast results. Each year, forecasts are obtained for demographic and socio-economic variables used in the models, and an updated long-term demand forecast over the planning horizon to 2035 is prepared. The variables include income, single family and multi-family housing, employment, development densities, persons per household, fraction of water accounts with reclaimed water and water price. The long-term demand forecasts also are based on long-term normal weather parameters for rainfall and temperature. Attachment

6 Demand Forecast Annual Evaluation and Update November 2013 Tampa Bay Water has completed the annual demand forecast evaluation for Water Year 2012 and has updated the long-term demand forecast for the agency s seven water demand planning areas to the year Alison Adams, Ph.D., P.E., Tampa Bay Water David Bracciano, Tampa Bay Water Jack Kiefer, Ph.D., Hazen and Sawyer, P.C. John Clayton, Ph.D., P.E., Hazen and Sawyer, P.C. Lisa Krentz, Hazen and Sawyer, P.C. Damann Anderson, P.E., Hazen and Sawyer, P.C.

7 TABLE OF CONTENTS LIST OF FIGURES LIST OF TABLES Page CHAPTER 1 INTRODUCTION... 1 CHAPTER 2 MODEL PREDICTION VERSUS ACTUAL FOR WY SECTION 2.1 Water Year 2012 Model Drivers... 3 SECTION 2.2 Explanatory Variables... 5 CHAPTER 3 WATER YEAR 2012 PREDICTED VERSUS ACTUAL RESULTS CHAPTER 4 LONG-TERM DEMAND FORECAST UPDATE SECTION 4.1 Update of Model Variable Projection Data SECTION 4.2 Updated Long-term Demand Results REFERENCES FIGURES Figure 1 Water Demand Planning Areas... 2 Figure 2 Total County Population Projections Figure 3 Projections of Housing Units in Tampa Bay Water Service Area Figure 4 Projections of Single Family Units Figure 5 Projections of Multi-Family Units Figure 6 Single Family Unit Projections by WDPA Figure 7 Multi-Family Unit Projections by WDPA P a g e ii

8 Figure 8 Tampa Bay Region-wide Employment Projections Comparison Figure Employment Projections by WDPA Figure Projections of Real Marginal Price by WDPA Figure Single Family Persons Per Household Projections by WDPA Figure Multi-Family Persons Per Household Projections by WDPA Figure 13 Comparison of Retail Water Use Projections for Tampa Bay Water area Figure 14 Total Retail, Unbilled and Wholesale Water Use Projections Figure 15 Historic Demand and Base Year 2011 and 2012 Regional Demand Forecasts Figure 16 Base Year 2012 Tampa Bay Water Updated Regional Demand Forecast TABLES Table 1 Comparison of WY 2012 Actual Units and WY 2011 Projections... 4 Table 2 Sector Driver Trend by WDPA from WY 2011 to WY Table 3 Change in Persons per Household from WY 2011 to WY Table 4 Persons Per Household Projection Errors... 6 Table 5 Real Mean Household Income Projection Errors WY 2011 to WY Table 6 Price Projection Errors WY 2011 to WY Table 7a WY 2012 Projection Errors for Single Family Reclaimed Fraction... 9 Table 7b WY 2012 Projection Errors for Multi-Family Reclaimed Fraction... 9 Table 8 WY 2012 Actual Rainfall Minus Long-term Average Rainfall Table 9 WY 2012 Average Max. Daily Temperature minus Long-term Average Max. Daily Temperature Table 10 Water Year 2012 Retail Predicted vs. Actual Water Use P a g e iii

9 Table 11 Comparison of Predicted Regional Water Demands Base on 2010 Base Year Socioeconomic Projections and 2011 Base Year Socioeconomic Projections? Table 12 Predicted vs. Actual Water Use by Sector and WDPA Table 13 Predicted vs. Actual Retail Use for Water Years 2010, 2011 and Table 14 Table 15 Percent Difference between 2011 and 2012 Housing Unit Projections for Percent Difference in 2035 Employment Projections 2012 Compared With Table 16 Base Year 2012? Income Projections by WDPA Table 17 Updated Housing Densities Table 18 Fraction of Reclaimed Accounts for Single Family Sector Table 19 Fraction of Reclaimed Accounts for Multi-Family Sector Table 20 Changes in Wholesale Customer Projected Water Demand Table 21 Updated Regional Long-term Demand Forecast, 201 Base Year P a g e iv

10 1. INTRODUCTION Tampa Bay Water provides water demand forecasts for its six Member Governments specifically to project the amount of water supply needed within Tampa Bay Water s service area. The Long-term Demand Forecasting models are designed primarily for the purpose of longer-term planning and forecasting, over year time horizons. The models follow a monthly and yearly time step, which provides the capability of predicting water use over shorter intervals. Tampa Bay Water updated its Long-term Demand Forecasting models in 2008 and subsequently developed two long-term water demand forecasts to 2035, using Water Year 2008 as the base year which was update using Water Year 2009 as the base year (Hazen and Sawyer, 2010a). Since 2009 the agency updates the long-term demand forecast annually to capture changes in socioeconomic trends. The primary purposes of providing these forecasts for the seven water demand planning areas (WDPAs) of the six member governments are: 1. Annual budgeting and source allocation near term forecasting up to five years into the future. 2. Long-range water supply planning forecasting water demands for at least 20 years into the future. Development of annual forecasts and comparison with actual water use can assist Tampa Bay Water in learning how to adapt to changes in water use and related weather and socioeconomic conditions. A set of procedures have been developed to conduct an annual evaluation of the predictive capability of the demand forecast models based on the most recent model predictions and actual water use, and updated model predictions based on revised socioeconomic conditions. Tampa Bay Water uses services provided by Hazen and Sawyer, P.C., to conduct the annual evaluation and update model predictions for the longterm demand forecast. As described in the Long-term Demand Forecasting Model documentation (Hazen and Saywer, 2010a), retail demand is modeled using three sector specific econometric models. Each model generates demand forecasts based on WDPA specific weather and socioeconomic projections. Sector specific models therefore satisfy the need for modeling retail demand on a member by member basis. From these results, sector specific results can be aggregated as needed into various time periods and geographic delineations. P a g e 1

11 Tampa Bay Water s annual demand forecasting evaluation procedure (Hazen and Sawyer, 2010b) is used to perform a comparison between the forecasted and actual retail water use for each water demand planning area (Figure 1). The analysis compares observed water use for the most recent water year having a complete data set against the predicted water use for the same year. This analysis verifies the predictive capability of the demand forecast models and provides information regarding the uncertainty of the socioeconomic projections. Figure 1. Water Demand Planning Areas P a g e 2

12 2. Model Predicted versus Actual Water Use for Water Year 2012 The forecasting models relate water consumption to weather and socioeconomic factors which influence the use of water (e.g., price, income, housing density, persons per household, employment, growth in housing). The evaluation of the models requires a complete year of data for all the model parameters. For this evaluation, Water Year 2012 is the most recent water year in which all member water use and weather data are available. Water Year 2012 data needed to complete the annual evaluation include: Member water use data by account, including wholesale water delivered by members to their wholesale customers Tampa Bay Water delivery data by WDPA Actual daily temperature and rainfall data for the rainfall and temperature stations used to develop the long-term demand forecast models (for a list of rainfall and temperature stations used see Hazen and Sawyer, 2010a) Updated water rate schedules for each Member 2012 Socioeconomic data - this creates the 2012 base year o Single-family, multi-family and non-residential units (sector drivers) o Persons per household o Income o Housing Density o Reclaimed Fraction o Fraction Employment in service and commercial sectors 2.1 Water Year 2012 Model Drivers (Units) The forecasting methodology employs a rate of use times driver approach for calculating sectoral demands. Each sector specific model calculates average monthly demand, or rate of use, per water consuming entity, or driver unit. A different driver unit is defined for each sector (e.g., single-family, multi-family and non-residential). The SF sectoral model calculates retail demand per single family household, with single family households serving as a driver unit. Likewise, the MF sectoral model calculates retail demand per multifamily dwelling unit, with the number of multifamily dwelling units serving as a driver unit. The NR sectoral model calculates retail demand per employee, with number of employees serving as a driver unit. A forecast of demand for any given sector is a simple product of predicted rate of use and the predicted number of driver units. P a g e 3

13 2.1.1 Forecasting Model Units Single family units are determined based on the number of single family active accounts in Tampa Bay Water s service area. The total number of single family units in Water Year 2012 was 506,099. This is less than the total number of single family units projected for 2012 in 2011, which were 508,184 units. The number of single family units increased by almost 5000 accounts from the Water Year 2011 total of 501,592. The City of Tampa WDPA has the largest number of single family units. Multi-family units are determined based on a unit to account ratio developed in 2007 (Hazen and Sawyer, 2010(a)). The total number of multi-family units in Water Year 2012 was 313,176. This is slight increase from the total number of units of 309,481 in Water Year The total number of multi-family units projected for 2012 in 2011 was 310,984. The City of Tampa WDPA has the largest number of multi-family units. Non-residential units reflect the estimated number of employees and are determined based on an employee to account ratio developed in 2007 (Hazen and Sawyer 2010) using Florida Department of Transportation TAZ-level employee data. The total number of employees for non-residential water accounts in Tampa Bay Water s service area in Water Year 2012 was 1,212,912. The total number of employees for Water Year 2011 was 1,212,605. The total number of employees projected for 2012 in 2011 was 1,208,309. The City of Tampa WDPA has the largest number of employees for non-residential water accounts. The evaluation includes quantifying the uncertainty of socioeconomic projections. The retail demand forecast is influenced significantly by the number of units projected for each sector for each water demand planning area. In Water Year 2011 a projection of the unit drivers for Water Year 2012 was obtained. Table 1 shows the results of comparing the observed units for Water 2012 against the projection of 2012 made in Table 1. Comparison of WY 2012 Actual Units and WY 2011 Projection of WY 2012 P a g e 4

14 The evaluations show that the projections of WY 2012 made in WY 2011 over projected single-family units by 0.4%, under projected multi-family units by 0.7% and under projected number of employees by 0.1% across all the WDPAs. The percent changes from Water Year 2011 to Water Year 2012 in observed units by water demand planning area are shown in Table 2. Table 2. Sector Driver Trend by WDPA from WY 2011 to WY to 2012 WY % Changes PAS NPR NWH SCH COT PIN STP SF % Change 1.24% 0.13% 3.00% 0.56% 1.17% 0.06% 0.17% MF % Change 1.65% 0.73% 6.45% 4.47% 0.28% 0.27% 0.04% NR % Change 1.09% -0.32% 3.56% -0.77% 0.43% -0.21% 0.59% 2.2 Explanatory Variables Each sectoral model has a set of explanatory variables that explains the rate of water use by sector, WDPA, and month. Examples of explanatory variables include income, real marginal price, persons per household, and density. By conducting regression analyses on historic water use, weather, and socioeconomic data, coefficients for each explanatory variable are determined that measure the relationship between the explanatory variable and the per unit sector water use by WDPA and month Persons Per Household Information used to determine the Water Year 2012 persons per household was obtained from the American Community Survey. Results comparing the changes from Water Year 2011 in the number of persons per household for both single family and multi-family are shown on Table 3. The year to year changes are due to changes in the ACS data from Water Year 2011 to Water Year For a detailed discussion of the development of the long-term demand forecast models and their coefficients, see Hazen and Sawyer, 2010a. P a g e 5

15 Table 3. Changes in Persons Per Household from WY 2011 to WY 2012 Table 4. Persons Per Household Projection Errors The projection errors shown in Table 4 indicate the projections tend to underestimate the persons per household. However the project error for 2011 is generally smaller than the 2010 projection error. Since the American Community Survey data is a relatively new product, there is insufficient information provided to determine the cause of the projection errors Income Table 5 shows the difference in actual Water Year 2012 income by WDPA compared with the Water Year 2011 projected income for Water Year Mean household income levels in 2012 across the WDPAs are generally lower than the 2011 projected incomes for P a g e 6

16 Table 5. Real Mean Household Income Projection Errors from WY 2011 to WY 2012 The comparisons shown in Table 5 show some relatively large differences in income between Water Year 2011 projections for Water Year 2012 and new estimates using Water year 2012 data. All of these changes reflect analogous changes in American Community Survey data between 2011 and As with persons per household, because the American Community Survey only samples households in each census block group, changes in variables derived from this dataset likely represent year-to-year changes in sampling error Real Marginal Price (RMP) Table 6 shows the difference in actual Water Year 2012 price in real terms by WDPA compared with the WY 2011 projected price for WY The largest error between WY 2012 projections and observations was for the City of Tampa. Rate increases by the City of Tampa, Pasco and Hillsborough (NWH and SCH) counties were not anticipated and were likely causes of over forecasting demand. P a g e 7

17 Table 6. Price Projection Errors from WY 2011 to WY Housing Density Housing density is based on parcel level geo-coded account and unit data. Housing density was not expected to change much from 2010 to 2012 so the 2010 values were used in the 2012 model evaluations. Housing density was assumed to remain constant over the forecast period Fraction of Reclaimed Water Use Accounts The fraction of reclaimed water use accounts was updated with Water Year 2012 data from the member governments. Tables 7a and 7b show the projection errors for the Water Year 2012 fraction of single-family reclaimed accounts and fraction of multi-family reclaimed accounts. Reclaimed fractions were assumed to remain constant over the forecast period. P a g e 8

18 Table 7a. WY 2012 Projection Errors for Single family Reclaimed Fraction WDPA WY 2012 SF Reclaimed Frac 2011 SF Reclaimed Fraction from Base Year 2010 Forecast Difference from Base Year 2011 Forecast % Change from Base Year 2011 Forecast PAS % NPR % NWH % SCH % COT % PIN % STP % Table 7b. WY 2012 Projection Errors for Multi-family Reclaimed Fraction WDPA WY 2012 MF Reclaimed Frac 2011 MF Reclaimed Fraction from Base Year 2010 Forecast Difference from Base Year 2011 Forecast % Change from Base Year 2011 Forecast PAS % NPR % NWH % SCH % COT % PIN % STP % Weather Variables Table 8 shows the differences between long-term average monthly rainfall and Water Year 2012 actual monthly rainfall by WDPA. The table also highlights the spatial variability of rainfall between the WDPAs. P a g e 9

19 Table 8. WY2012 Actual Rainfall Minus Long-term Average Rainfall 2 PAS NPR NWH SCH COT PIN STP Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep WY total Notes: Negative numbers means actual rainfall less than long-term averages. Red shading indicates rainfall less than average (darker the shading = less rainfall). Green shading indicates rainfall greater than average (darker the shading = more rainfall). Table 9. WY 2012 Average Max. Daily Temperature minus Long-term Average Max. Daily Temperature PAS NPR NWH SCH COT PIN STP Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep WY 2012 avg Notes: Negative numbers mean actual temperatures were cooler than long-term averages. Green shading indicates temperature cooler than average (darker green = much cooler). Red shading indicates temperature warmer than average (darker red = much warmer). P a g e 10

20 Water Year 2012 day temperatures were slightly cooler than normal across the region except for December 2011 through March 2012, where temperatures were generally warmer than normal, with March much warmer. Of significant importance, May 2012 temperatures were below normal (generally highest demand period annually).the annual rainfall total across the region was about 4 inches above the long-term normal with some variability both temporally and spatially. As shown in Table 8 annual rainfall in the St. Petersburg WDPA was significantly less than long-term normal annual rainfall; however rainfall totals in all other WDPAs were higher than long-term normal annual totals. Above normal monthly totals across the region occurred during the months of May through August, with June totals over 10 inches above normal. Significantly below normal monthly totals occurred in November through March and again in September of Seasonal differences between actual weather and long-term normal weather are used to predict future water use. Since long-term normal weather is assumed in all future months throughout the forecast period, observed water use in any future year will differ from the forecast, in part, due to actual weather conditions for that year. Warmer and drier than normal weather generally leads to higher water use, while cooler and wetter than normal conditions cause actual water use to be less than what would be predicted under long-term normal climate. This effect can be seen in May and June 2012, where above- normal rainfall conditions caused significant reductions in peak monthly demand. Input data for the weather variables include both current-year weather data and long-term weather data. The demand forecast models use one or more of the weather parameters listed below. All weather data are log transformed as inputs into the long-term demand forecasting models (Hazen and Sawyer, 2010a). Long-term normal monthly rainfall Number of days in a month with daily rainfall greater than 1 inch Number of days in a month with daily rainfall greater than 0.01 inch Departure from number of days in the month with daily rainfall greater than 0.01 inches from the long-term number Departure from total monthly rainfall from long-term normal Long-term average maximum daily temperature for each month Departure from long-term average maximum daily temperature for each month P a g e 11

21 3. Water Year 2012 Predicted versus Actual Water Use Results Model performance was evaluated by entering observed and estimated Water Year 2012 weather conditions, explanatory variables and drivers into the demand forecasting models to develop predicted unit usage rates and total use by sector by WDPA. Predictions of retail water use are then compared to the actual retail water use data collected from the member governments. Retail water use represents the member governments retail sales and does not include the member governments wholesale and unbilled (nonrevenue-generating) water delivery. Table 10 shows the results of comparing the retail sector model predictions with actual retail water use by WDPA for Water Year The results show the forecasting models overpredicted retail water use for Water Year 2012 by 0.8% or 1.5 mgd 2. Data issues hampered evaluation of Pinellas WDPA model predictive accuracy. Observed Pinellas disaggregated water use was significantly lower than expected based on data transfer issues from February 2012 through September This issue is being rectified. Table 10. Water Year 2012 Retail Predicted vs. Actual Retail Water Use WDPA WY 2012 Predicted Retail Water Use (mgd) WY 2012 Actual Retail Water Use (mgd) Predicted - Actual (mgd) % Difference PAS % NPR % NWH % SCH % COT % PIN % STP % Total % Total water use (inclusive of retail, wholesale, and unbilled consumption) in Water Year 2012 within Tampa Bay Water s service area was mgd. Using socioeconomic projections for 2 The annual evaluation conducted for the WY 2011 demand forecast models showed that the forecasting models over-predicted retail water use for Water Year 2011 by 1.4% or 2.1 mgd. P a g e 12

22 2012 through 2035, obtained in 2011, and observed weather for WY 2012 the forecasting models under-predict total water demand by 0.6% or 1.4 mgd 3. When the new socioeconomic projections (obtained in 2012) and observed Water Year 2012 are used in the forecasting models, the forecasted total water use for Water Year 2012 is mgd, an under-prediction of 2.4%. Non-predicted increases in water rates by some member governments would drive demand downward. These results are shown in Table 11. Table 11. Comparison of Predicted Regional Total Water Demands Based on 2011 Base Year Socioeconomic projections and 2012 Base Year Socioeconomic projections Socioeconomic Data Source 2011 Base Year Model Weather Scenario Long-term Normal 2012 Observed 2012 Total Regional Demand, MGD % Error from WY 2012 observed Total Demand -0.60% (under prediction) -0.64% (under prediction) 2012 Base Year Model Long-term Normal 2012 Observed (under prediction % (under prediction) Table 12 shows the comparison between model predicted retail water use and actual retail water use for each WDPA by water use sector model - single-family (top panel), multi-family (middle panel) and non-residential (bottom panel). Model predicted retail water use by sector and WDPA improved from the 2011 evaluation. 3 Total water use in Water Year 2011 within Tampa Bay Water s service area was mgd. The prior year forecast predicted total water use for Water Year 2012 at mgd, a difference of 3.9 mgd. P a g e 13

23 Table 12. Predicted vs. Actual Water Use by Sector and WDPA Single Family Sector WDPA Predicited Observed Differnce % Difference PAS % NPR % NWH % SCH % COT % PIN % STP % TBW % Sector Multi Family Sector WDPA Predicited Observed Differnce % Difference PAS % NPR % NWH % SCH % COT % PIN % STP % TBW % Non-Residential Sector WDPA Predicited Observed Differnce % Difference PAS % NPR % NWH % SCH % COT % PIN % STP % TBW % P a g e 14

24 Table 13 shows a comparison of predicted retail water use and actual retail water use for Water Years 2010 through Results show high accuracy for predicting retail water use for Tampa Bay Water s service area over all three years, with the exception of Pinellas County in Table 13. Predicted vs Actual Retail Use for Water Years 2010, 2011, and 2012 WDPA WY 2012 Retail WY 2011 Retail WY 2010 Retail Predicted- % Difference Predicted-Actual % Difference Predicted-Actual Actual (MGD) (MGD) (MGD) Single-family water use accounts for the largest percentage of retail water use in Tampa Bay Water s service area. For this sector, comparisons of predicted and observed demand show the most consistent agreement. A major reason for single-family predictive consistency is a close correspondence between accounts and housing units (e.g., one house typically has one water account). While there is no accepted standard for long-term demand forecast model prediction errors, a threshold of 15 percent deviation between predicted and observed water use is assumed to be a reasonable target for Tampa Bay Water s sectorally disaggregated forecasts (Hazen and Sawyer, 2010b). This threshold is based on experience with a variety of demand forecasting models. The evaluation of the models predictive capabilities is used to address two questions: 1. Should the coefficients of the demand models be updated? % Difference PAS % % % NPR % % % NWH % % % SCH % % % COT % % % PIN % % % STP % % % Total % % % At this time, updating the model coefficients is not recommended. Model performance remains good across the sectors and WDPAs. Prediction errors for single family sector models were less than 15% for all WDPAs, with the exception of Pinellas County due to data transfer issues, and overall prediction errors generally improved since WY There appears to be no persistent bias in the prediction errors. It is anticipated that the process of updating the model coefficients will be recommended within the next year. At that point, a total of seven years of new consumption, weather, and P a g e 15

25 economic data will exist beyond data available and used at the last model revision. This new period of record data will include economic boom, economic downturn and recovery periods. In addition, new data sources, such as the American community Survey are becoming available that aligns socioeconomic projections to census tract and block group geographies. The current models are based on water use and explanatory data specified at traffic analysis zone level to provide spatial disaggregation. 2. Should the models be calibrated to the new base year and then the prediction updated? Calibrating a model means that the predicted demand value for the anchor (or base) year for forecasting future water demands is updated to the match the current year observed demand precisely. Calibration represents a structural shift upward or downward in the predictions of the model, depending on prediction error. Calibration is typically recommended when prediction errors become large due to incorrect tracking of economic trends, when prediction errors persist in the same direction over time due to an underlying bias in model parameters, or when predictions do not adequately follow recently observed weather trends. Decisions to calibrate should not be influenced by any demand management anomalies, such as water shortage restrictions, which are not anticipated and accounted for in the models, or when there are known problems with accuracy of the values of observed WDPA demands that cannot be effectively mitigated. Results from the assessment of Water Year 2012 model predictions indicate very good overall performance of the models and prediction of retail water use by WDPA. Prediction errors by WDPA and month are offsetting and not persistent. In addition, no demand management anomalies or water restrictions confounded the analysis. The differences in 2035 regional forecast values vary by only 8 mgd between uncalibrated and calibrated versions of the water demand models. Furthermore, as shown in later sections the calibrated model produces a 2035 forecast that is only about 4 mgd different than the forecast produced using WY2011 as the base year. The calibrated forecast model slightly over-predicts preliminary regional water use values collected for WY2013, which is consistent with above normal rainfall occuring in spring 2013 and the fact the WY2013 forecast assumes normal weather conditions. However, the uncalibrated forecast model slightly under-predicts WY2013 water use under the assumption of normal weather. Although both of the calibrated and uncalibrated versions of the model perform well in the short run, substitution of actual weather into the models for WY2013 P a g e 16

26 would be expected to move predictions from the calibrated model closer to observed values and move predictions from the uncalibrated model farther away from observed values. Because (a) the 2035 forecast differs by only a small amount between the uncalibrated and calibrated models and (b) the calibrated model can be anticipated to better reproduce WY2013 demands during the next model evalutaion period, it is recommended that the calibrated version of the model be used as a basis for the forecast update. 4. Long-term Demand Forecast Update Once the evaluation of model prediction performance is complete, the recent year evaluated becomes the base year for the long-term demand forecast update, utilizing observed socioeconomic conditions. Next, the long-term forecast of demand (i.e., for the years subsequent to the base year) utilize revised socio-economic projections if they are available. The revised long-term demand forecast is then presented to the Board of Directors each December and used to estimate how much water Tampa Bay Water budgets for delivery in the upcoming water year. Data sources used for the 2012 evaluation were: Updated American Community Survey (ACS) 5-year average observations over statistical estimates at block group level for Total, Single family, and Multi-family Population Occupied Single family and Multi-family Housing Units Mean Household Income ACS data aggregated to WDPA MOODY S County-level projections for Total population Single family, multi-family and total dwelling units Total employment Median household income FDOT TAZ-level projections for 2006, 2025, 2035 (updated 2009) Total dwelling units Total population Total employment and employment by Service, Industrial, Commercial classes FDOT data facilitates disaggregation of county-level projections to Traffic Analysis Zones (TAZ) then re-aggregation to WDPA Tampa Bay Water P a g e 17

27 historical marginal price for water and sewer at 8000 gallon per month for single family residential use (for each member) base-year consumption and accounts by sector and WDPA base-year wholesale and total delivery by WDPA Income Data Use Moody s data to project annual % growth in mean nominal household income at the county level Annual growth projections for mean household income are only directly available at Metropolitan Service Area (MSA) level County-level annual growth projections are only available for median household income Use the county median household growth rates to break the MSA mean rates apart to counties Grow WDPA base-year mean household incomes (from ACS) using countylevel mean income growth rates Adjust to 2006 dollars Total population in the three counties based on the 2010 census is 2.61 million people. The estimated population for the three counties in 2012 is 2.67 million people. The total population served by Tampa Bay Water and the Member Governments is approximately 2.35 million people, about 86% of the total population. The regional population growth rate through the year 2035 based on Moody s projections has slightly decreased from 1.1% per year in 2011 to 1% per year in As shown on Figure 2, population in Pinellas County is projected to remain relatively constant through the forecast period of 2035, population for Hillsborough County is projected to increase through the forecast period at a higher rate, while population in Pasco County is projected to increase as a much slower rate. P a g e 18

28 Population Total Population Pasco County Pinellas County Hillsborough County Date Figure 2. Total County Population Projections 4.1 Update of Model Variable Projection Data This section presents results of updated socioeconomic data used to develop the 2012 calibrated base year long term demand forecast Single family and Multi-family Units For prior demand forecasting, it was assumed that total households in a county was the sum of single family and multi-family occupied housing unit projections by county. No separate forecasts for total housing by county were provided (Hazen and Sawyer 2010a). Now forecasts are available for total housing units by county and occupied single-family and multi-family households by county. Hazen and Sawyer revised the unit projection method, which is described in Hazen and Sawyer (2010a) (see Appendix E), to accommodate this new data. The revised method includes: 1. Raw WDPA nonsectoral projections: disaggregate Moody s total nonsectoral housing projections by county into WDPA using the FDOT County to WDPA dwelling unit ratios. P a g e 19

29 2. Raw WDPA sectoral projections: use ratios between sectoral housing stock projections to further disaggregate WDPA total household projections to single family and multi-family sectors. 3. Final WDPA sectoral projections: grow base-year sectoral single family and multifamily units according to interannual percent changes in Raw WDPA sectoral projections. The projections of total number of housing units over the forecast period have changed over the past three years (Figure 3). Updated projections for demographic and socioeconomic variables show that the projected growth rate for the total number of housing units in the Tampa Bay region increased from 0.85% per year in the 2010 forecast to 1.02% per year in the 2011 forecast and up to 1.16% in the 2012 forecast. The highest growth rates are projected to occur in the City of Tampa and Hillsborough County water service areas. The growth rates for Pinellas County and the City of St. Petersburg are projected to remain flat. Figure 3. Projections of Housing Units in Tampa Bay Water Service Area P a g e 20

30 Figures 4 and 5 show how the projected numbers of single family units and multi-family units have changed for the forecast period over the past three years. Projections for single family housing units remained relatively consistent between 2012 and 2011 and were slightly higher than 2010 projections. Projections for multi-family family housing units increased in 2012 compared to both 2011 and 2010 projections. Figure 4. Comparison of Projections for Single Family Units P a g e 21

31 Figure 5. Comparison of Projections for Multi-family Units The 2012 projections of single family and multi-family units by WDPA are shown on Figures 6 and 7. P a g e 22

32 Figure 6. Single family Unit Projections by WDPA Figure 7. Multi-family Unit Projections by WDPA P a g e 23

33 The updated projections for housing units show more projected units by 2035 than was projected in 2011, with significant growth projected in the multi-family sector. The single family and multi-family percent increases for 2035 by WDPA are shown in Table 14. Table 14. Percent Difference Between 2011 and 2012 Housing Unit Projections for 2035 Year PAS NPR NWH SCH COT PIN STP TBW Total 2035 SF Units Change -3.57% -1.70% 1.46% -2.38% -0.95% 0.51% 0.83% -1.04% 2035 MF Units Change 7.75% 9.57% 12.68% 9.62% 6.40% 17.89% 17.87% 12.65% Employment Projections Newer employment projections suggest lower long-term employment than previous projections (i.e and 2010) (Figure 8 and Table 15). The updated projections show a delay in expected rebound in employment growth rates, but slightly more optimistic numbers in the near and mid-terms than the 2011 projections. Employment projections are highest for the COT WDPA (Figure 9) P a g e 24

34 Figure 8. Tampa Bay Region-Wide Employment Projections 2012 compared with 2011 and 2010 Table 15. Percent Difference in 2035 Employment Projections 2012 compared with 2011 Year PAS NPR NWH SCH COT PIN STP TBW Total 2035 EMPLOYMENT Change -2.73% -0.37% 1.47% -4.19% -0.31% 7.07% 7.72% 1.36% P a g e 25

35 Figure Employment Projection by WDPA Income Projected mean household income levels for the forecast period are shown on Table 16. Updated growth rates for income show slower growth in income for all WDPAs except Pasco County and New Port Richey. Table 16. Base Year 2012 Income Projections by WDPA 2012 Base Year Income Projection Year PAS NPR NWH SCH COT PIN STP 2012 $51,936 $39,328 $67,456 $61,146 $51,112 $54,791 $48, $58,139 $44,026 $77,383 $70,144 $58,633 $56,446 $50, $57,825 $43,788 $77,741 $70,468 $58,904 $52,654 $47,039 P a g e 26

36 Price All sector models use real marginal price (RMP) of water and sewer as an explanatory variable 4. Member government water and sewer rates, for monthly consumption of 8000 gallons, for Water Year 2012 were obtained and adjusted for inflation based on 2006 dollars, which corresponds to the year of the price deflator used in developing the forecasting models. Water Year 2012 actual water rates increased over what was predicted in 2011 for some members. The real (inflation-adjusted ) regional growth rate, 1.66%, is applied to the real marginal price (RMP) for each WDPA starting with the base year The results of projecting real marginal price out to the year 2035 by WDPA are shown in Figure 10. Figure Projections of Real Marginal Price by WDPA Persons Per Household 4 The approach used for projecting marginal price of water for the demand forecasting models was documented in the 2010 Annual Demand Evaluation and Update Technical Memorandum, November P a g e 27

37 Results of updated persons per household for the single family and multi-family sectors for each WDPA are shown in Figures 11 and 12. Figure Single Family Persons Per Household Projections by WDPA P a g e 28

38 Figure Multi-Family Persons Per Household Projections by WDPA Housing Density Housing densities based on updated property appraisal information developed last year are used in the 2012 base year forecast Table 17. Table 17. Updated Housing Densities WDPA SF Housing Density (units/acre) MF Housing Density (units/acre) PAS NPR NWH SCH COT PIN STP P a g e 29

39 Fraction of Reclaimed Accounts The fraction of single family sector accounts and multi-family sector accounts with reclaimed water service for each of the WDPA was updated for the 2012 base year forecast. The reclaimed fractions for each sector and WDPA remain constant for the forecast period. Tables 18 and 19 provide the assumptions. Table 18. Fraction of Reclaimed Accounts for Single Family Sector WY 2012 Ave SF Reclaimed Accts WY 2012 Ave SF Accts WY 2012 Ave SF Reclaimed Fraction PAS NPR NWH SCH COT PIN STP Table 19. Fraction of Reclaimed Accounts for Multi-family Sector PAS NPR NWH SCH COT PIN STP WY 2012 Ave MF Reclaimed Accts WY 2012 Ave MF Accts WY 2012 Ave MF Reclaimed Fraction Updated Long-term Demand Forecast Results This section provides the results of the updated long-term demand forecast models and compares these results to the forecast performed last year using Water Year 2011 as the base year. P a g e 30

40 Figure 13 provides a comparison of retail water use projections last year (base year 2011) and current projections (base year calibrated). For the current forecast, the effects of more housing and employment outweigh effects of slow income growth and higher projected prices. Figure 13. Comparison of Retail Water Use Projections for Tampa Bay Water Area Total projected water demand includes wholesale water and unbilled. Wholesale accounts are identified by the member governments. Pinellas County has the most number of wholesale accounts. Unbilled represents the difference between total retail water delivered by the members (including wholesale accounts) and total water used by a member government. In spring 2010 Tampa Bay Water received updated information from Pinellas and Pasco counties reflecting changes in the wholesale water quantities. Table 20 shows these changes. Changes in the amounts of water Pinellas County expects to deliver to customers are included in the updated long-term demand forecast. P a g e 31

41 Table 20. Changes in Wholesale Customer Water Demand Figure 14 provides the forecasts for each component (retail, unbilled, and wholesale water use) of the total regional water demand through the 2035 forecast period. Figure 14. Total Retail, Unbilled and Wholesale Water Use Projections P a g e 32