WS-Demand Forecasting Scope

Short-Term Operational Forecasting for Urban Water Consumption

Prepared by: Prepared for:

March 2001

1650 38th St. Suite 201E + Boulder, CO 80301 + www.waterstoneinc.com + (303) 444-3500 fax. + (303) 444-1000 tel. i

TABLE OF CONTENTS

Page

1. INTRODUCTION 1

2. WATER DEMAND FORECASTING SYSTEM 2

2.1 EVALUATION OF DEMAND FORECASTING METHODOLOGIES 3

2.2 WATER DEMAND FORECASTING METHODOLOGIES 4

2.3 ANALYSIS OF WATER CONSUMPTION DATA: MOON LAKE TREATMENT PLANT 5

2.4 FORECASTING RESULTS 8

2.5 SOFTWARE AND HARDWARE IMPLEMENTATION 13

3. CONCLUSIONS AND RECOMMENDATIONS 14

4. ADDITIONAL RESEARCH/MODELING NEEDS 15

5. SCOPE AND BUDGET 15

6. REFERENCES 20

1650 38th St. Suite 201E + Boulder, CO 80301 + www.waterstoneinc.com + (303) 444-3500 fax. + (303) 444-1000 tel. ii

LIST OF FIGURES Figure Page

1 Classification of Exponential Smoothing Techniques 6

2 Recorded Daily and Monthly Average Water Consumption (in MGD)

at Moon Lake Water Treatment Plant 7

3 Observed and Forecasted Monthly Average Water Consumption

at Moon Lake WTP 8

4 Scatter Plot of Observed and Forecasted Monthly Average

Water Consumption Values 9

5 Observed and Forecasted Daily Water Consumption at Moon Lake WTP 11

6 Scatter Plot of Observed and Forecasted Daily Water Consumption Values 11

7 Residual Distribution and Expected Normal Density Function for

Regression Analysis of Daily Water Consumption Values 12

8 Maximum and Minimum Forecasted Daily Water Consumption in 1999 13

1650 38th St. Suite 201E + Boulder, CO 80301 + www.waterstoneinc.com + (303) 444-3500 fax. + (303) 444-1000 tel. 1

Statement of Work Tampa Bay Water Demand Forecasting System

1. INTRODUCTION

Tampa Bay Water is Florida’s largest wholesale water supplier. Tampa Bay Water’s member

governments – Hillsborough, Pasco and Pinellas Counties and the cities of New Port Richey, St.

Petersburg and Tampa – serve more than 1.8 million residents in the Tampa Bay region. The

mission of Tampa Bay Water is to provide its members with reliable supplies of high-quality water

to meet present and future needs in an environmentally and economically sound manner.

Tampa Bay Water currently operates twelve wellfields. Eleven of these wellfields are operated as

an integrated system under the Optimized Regional Operations Plan (OROP). Current plans call

for the addition of new water supply sources and facilities to the existing system for the primary

purpose of meeting projected water demands for the 2003-2006 timeframe, and to replace existing

water supplies lost as a result of reducing overall production from the eleven integrated system

wellfields. These new sources will include limited new groundwater development, surface water,

desalted brackish water, and desalted seawater. A large off-line reservoir is also to be included in

the system.

The coordinated operation of these wellfields is guided by an operational optimization model that

determines the pumping rates required to satisfy forecasted demands at eleven points of

connection while emphasizing that the environmental impacts are minimized. Optimized

production planning is conducted every two weeks but is expected to change such that shorter-

time forecasts are made. The accuracy of forecasted demand for short time periods (e.g., one to

two days, one week, etc.) is essential for providing reliable water supply solutions and meet

contractual obligations. The following section outlines the requirements of the Water Demand

Forecasting System (WDFS) including data needs, implemented algorithms, and forecasting

evaluation tools. The WDFS is expected to be one of the software modules employed by a

Decision Support System (DSS). Integrating database, analysis software modules, and a

graphical user interface, the DSS is described in another statement of work.


2. WATER DEMAND FORECASTING SYSTEM

The objective of this statement of work is to describe a water demand forecasting system (WDFS)

that will provide accurate short-term forecasts at each of the eleven points of connection as well as

total regional water demand. Since water consumption depends on factors that could only be

predicted in a probabilistic sense, the WDFS must recognize these factors and generate stochastic

demand predictions. Ideally, the WDFS will provide full characterization of the cumulative

distribution function for future demands. Furthermore, while the underlying physical processes

(e.g., weather) are usually assumed to be statistically stationary, this is not the case for water

demand forecasts. Water demands depend on both people and nature. Therefore, the WDFS

must be capable of continuously updating the relationships between demand and weather

conditions to reflect recent trends.

The current OROP employs bi-weekly and monthly demand forecasts. However, Tampa Bay

Water has identified the need for shorter-term forecasts that will be utilized for operational

purposes. Particularly, the WDFS must provide daily and bi-daily forecasts.

To summarize, The WDFS must have the following properties:

1. Forecast daily water demands in each of the eleven connection points and for the total

service area.

2. Capability to aggregate demand forecasts to daily, bi-daily, weekly, bi-weekly, monthly, and

annual averages.

3. Capability to select historical or forecast weather as a basis for water demand forecasts

over a specified time horizon.

4. Provide an archive of historical weather data and the graphical interface to view, edit, and

adjust these data to facilitate demand forecast sensitivity analysis. Edited data should be

flagged as such before saving back to the database.

5. Provide capability to update and to save actual weather data and weather forecasts.

6. Provide capability for continuous updating of the utilized models in order to reflect the most

recent trends in water consumption.


2.1 EVALUATION OF DEMAND FORECASTING METHODOLOGIES

Each potential demand forecasting method should be evaluated by using it to predict water

demands for the climatic conditions of the year 2000. Generally, a longer evaluation record is

preferred but the amount of reliable data available for the WDFS is limited (starting in October

1991). The data for 2000 should not be used in the development of the method. The reliability of

the forecast should be evaluated for the various points of connection, water demand areas, and

the total service area for time increments of days, weeks and months for different seasons of the

year, and for the annual total. The evaluation should be carried out by graphical plots of forecast

vs. observed, time series plots, and relevant statistical measures (e.g., correlation coefficient,

residual analyses, Theil’s U, Durbin-Watson statistic).

Preliminary evaluation of water consumption data at the Moon Lake Water Treatment Plant (WTP)

(formerly known as Little Road WTP) in western Pasco county connection point has been carried

out to:

• identify potential forecasting methodologies,

• identify potential data needs,

• illustrate the required properties of the WDFS, and

• illustrate the evaluation of demand forecasting methodologies.

This evaluation utilized a two-step set of models representing the effects of four factors on water

use; namely trend, seasonality, autocorrelation, and climatic correlation. First, monthly average

water use was evaluated to identify the trend and seasonality factors. Second, daily deviation

from the monthly average was modeled based on autocorrelation and climatic correlation factors.

This method combines several approaches that have been successfully implemented to forecast

urban water demand at other cities including Melbourne, Australia (Zhou, et al., 2000), Arlington,

Texas (Homwongs et al., 1994), Victoria, British Columbia (Kenward and Howard, 1998), and

several other U.S. cities (Maidment et al., 1986 and Saleba, 1985). The following sections explain

the utilized models, their rationale, and application results.

Available data from the water treatment plant is water consumption data. Water demand is a

theoretical concept that is not directly observed or measured. Demand may differ from

consumption because of past inability to meet demand, effects of rate changes, and water

consumption management policies. For example, water demand curtailment policies could be

implemented to reduce actual water consumption from the demand values requested by a


municipality. The following preliminary evaluation utilized water consumption data directly. That

is, the data was not adjusted to calculate water demands. Generally, some preprocessing of the

consumption data will likely be necessary to isolate these effects before the data are used to

develop the demand forecasting method. Past studies elsewhere have shown that per capita

water demands depend on long-term evolution and trends in water consumption, seasonally

dependant use of water, and daily weather factors. As a minimum, the following data are required

to adjust water consumption data:

• A long record of water consumption (at least five to six years), and the coincident daily

precipitation, and maximum and minimum temperature and relative humidity. Potentially

daily average wind speed can be required,

• The record of significant consumer rate changes and times when temporary curtailment of

water use was requested,

• Dates and amounts of major changes in the water supply and distribution system, and

• Dates and measures of the effects of climate perturbations on local weather.

2.2 WATER DEMAND FORECASTING METHODOLOGIES

Water consumption varies widely over time. Several factors combine to explain this variability

including socioeconomic factors (e.g., population growth, demographics, and rate structures),

seasonal factors (e.g., winter vs. summer), daily climatic factors (e.g., precipitation, temperature,

and relative humidity), and day-to-day activities (e.g., weekday vs. weekend). Several approaches

have been used to forecast urban water demand. Saleba (1985) classified these approaches into

three major categories:

• End use forecasting,

• Econometric forecasting, and

• Time series forecasting.

End use forecasting uses specific water use data for the population of interest. While requiring

large amounts of data, this approach has shown little success in application. This lack of success

lead many researchers to econometric forecasting and time series forecasting. Econometric

forecasting is based on the identification of historical relationships (econometric models) between

water use (response variable) and socioeconomic and environmental factors (explanatory

variables). Once these relationships are established, they are assumed to hold into the future and

are used to forecast water use based on forecasts of the explanatory variables. Finally, the most-


widely used time series forecasting depends on the direct identification of the patterns existing in

the water consumption data.

Based on literature review and the requirements of Tampa Bay Water for the water demand

forecasting system (WDFS), an approach was adopted based on time-series forecasting

supplemented by climatologic factors correlation. The idea is to isolate the long-term trend

components (weather independent) then identify the broad seasonal variability (dependent on

general weather patterns rather than day-to-day weather variability). Once these two components

are identified, the residual is modeled based on autocorrelation (short term) and correlation with

recent weather variability. The considered weather factors included, minimum and maximum

temperature, precipitation, minimum and maximum relative humidity, wind speed, and pan

evaporation. These data were obtained from the Tampa International Airport weather station

(maintained by NOAA).

2.3 ANALYSIS OF WATER CONSUMPTION DATA: MOON LAKE TREATMENT PLANT

The applied methodology follows a two-step approach. In the first step, the trend and seasonality

components of the time series are forecasted using an exponential smoothing method applied to

time-averaged water consumption data. Both weekly and monthly averaged demand values were

evaluated using few exponential smoothing algorithms. These algorithms are based on three

smoothing steps; overall smoothing, trend smoothing, and seasonal smoothing. The trend could

be modeled using linear, exponential or saturated growth models. Seasonality could either be

additive or multiplicative. Figure 1 shows the potential combinations of trend and seasonality. For

example, the Winters’ method assumes multiplicative seasonality and a linear trend.


FIGURE 1. Classification of Exponential Smoothing Techniques

For the Moon Lake WTP data (shown in Figure 2), a multiplicative seasonal component always

provided better answers (more accurate forecasts). However, it could be seen from Figure 2 that

the data does not display any clear trend pattern except for an abnormal low consumption period

within the first year. A no-trend model was accurate after the first year was excluded from the

analysis. However, a saturated-growth trend model was the best model for the entire record.

Therefore, average monthly water demand forecasting was based on a saturated-growth trend

model with multiplicative seasonal factors.


FIGURE 2. Recorded Daily and Monthly Average Water Consumption (in MGD) at Moon Lake

Water Treatment Plant.

Figure 2 shows that daily water consumption varies significantly around the monthly average

values. The second step in the applied methodology predicts these daily deviation values based

on (1) autocorrelation and (2) correlation with meteorological factors. Three types of

autocorrelation were evaluated:

• Long-term autocorrelation (annual),

• Short-term autocorrelation (weekly), and

• Memory autocorrelation (correlation between similar consecutive days).

The Moon Lake WTP data displayed no long-term autocorrelation, perhaps because of the short

record. However significant autocorrelation was found at the weekly interval. In other words,

water consumption over the same day of the week were correlated. Furthermore, water

consumption between consecutive days (memory effects) were correlated after separating

weekdays from weekends.

Several meteorological variables were evaluated for significant correlation with daily deviations

from monthly average values. Both daily recorded values as well as moving averages over three

to seven days were considered. The evaluated variables are daily recorded values of:

• Minimum, maximum, and average temperature

• Minimum, maximum, and average relative humidity


• Precipitation

• Average Wind Speed

2.4 FORECASTING RESULTS

The first step of the applied methodology is to forecast monthly average demands. Figures 3 and

4 show a comparison between observed and forecasted values. With a simple correlation

coefficient of 0.97 and Theil’s U of 0.29, the model captures the seasonal and trend components

accurately. Only the first six years were used for model initialization (estimating seasonal factors

and model parameters). The model predictions between Jan. 97 and Dec. 99 are indicative of the

model’s predictive power.

FIGURE 3. Observed and Forecasted Monthly Average Water Consumption at Moon Lake WTP.

4

8

12

16

Jan-91 Jan-93 Jan-95 Jan-97 Jan-99

Mon

thly

Ave

arge

Wat

er

Con

sum

ptio

n (M

GD

)

ObservedForecasted

Calibration Period Verification Period


FIGURE 4. Scatter Plot of Observed and Forecasted Monthly Average

Water Consumption Values.

After an accurate model was established for average monthly values, daily deviations from

monthly average water consumption were evaluated using regression analysis based on

autocorrelation and correlation with meteorological variables. A multiple regression model was

setup using the following explanatory variables:

• Lagged daily deviation (from monthly average) for one week and one year,

• Previous similar one-day lagged daily deviation,

• Daily precipitation amount,

• Moving average (3-day and 7-day) precipitation,

• Number of days since last precipitation,

• Maximum daily temperature,

• Moving average (3-day and 7-day) maximum daily temperature,

• Average daily temperature,

• Moving average (3-day and 7-day) average daily temperature,

• Maximum daily relative humidity,

• Moving average (3-day and 7-day) maximum daily relative humidity

• Minimum daily relative humidity,

• Moving average (3-day and 7-day) minimum daily relative humidity,

• Average daily relative humidity, and

• Moving average (3-day and 7-day) average daily relative humidity.

R2 = 0.9013

4

8

12

16

4 8 12 16

Observed

Fore

cast

ed



All the considered variables were checked for correlation with daily deviations using stepwise

regression and a series of Box-Cox transformations. In general, daily water consumption

deviations were much more correlated with short-term moving averages (3-day) than with 7-day

moving average values and daily values for meteorological variables. Similarly, daily water

consumption deviations’ autocorrelation was much more pronounced for short lags (one and

seven days) than for long lags (one year). Using the first six years of data for model fitting, the

final regression model had a correlation coefficient of about 0.86 with a Theil’s U value of about

0.7 and included the variables shown in the following table.

Explanatory Variable

Transformation

Regression

Coefficient

Std. Err.

Of Reg.

Coeff.

Percent

Contribution

to

Correlation

One-week lagged daily deviation X

X * |X|

0.73

-0.12

0.03

0.01

19.5

15.2

Previous similar one-day lagged daily deviation X

X * |X|

0.13

0.07

0.03

0.01

19.4

18.1

Three-day moving average of daily precipitation Log(1+X) -0.49 0.05 10.8

Number of days since last precipitation Log(1+X) 0.22 0.08 10.1

Three-day moving avg. of average daily temp. Log(X) -2.68 0.46 5.4

Average daily relative humidity Log(X) 2.66 0.40 1.3

Model prediction accuracy between Jan. 97 and Dec. 99 (the validation period) was very similar to

that of the fitting period. The daily forecasting model explains about 70% of the variability of daily

consumption values. The unexplained 30% is assumed to be random and could only be

forecasted in a stochastic sense. Clearly, a random component is present for daily consumption

values and no highly accurate predictions should be expected from a deterministic model. The

motivation for the above methodology is that this random component should be eliminated when

monthly average values were calculated. Furthermore, using meteorological variables for the daily

model allows a stochastic evaluation of forecasted demands using an ensemble of possible

meteorological conditions. Figures 5 and 6 show observed and forecasted daily water

consumption values for the entire record. Consistent with the underlying statistical model, Figure 7

shows that the residuals from the regression model follow a normal distribution.


FIGURE 5. Observed and Forecasted Daily Water Consumption at Moon Lake WTP.

FIGURE 6. Scatter Plot of Observed and Forecasted Daily Water Consumption Values.

4

8

12

16

Jan-91 Jan-93 Jan-95 Jan-97 Jan-99

Daily Consumption Daily Forecast

Verification Calibration

R2 = 0.6745

4

8

12

16

4 8 12 16

Observed

Fore

cast

ed



FIGURE 7. Residual Distribution and Expected Normal Density Function for

Regression Analysis of Daily Water Consumption Values.

To forecast stochastic consumption values, several ensembles of possible future meteorological

values could be utilized. For each ensemble, one set of forecasted demands is generated. The

empirical distribution function of future daily water consumption is then constructed by sorting the

forecasted consumption values for each day. The methodology does not depend on the specific

forecasting technique used to generate possible future meteorological values. To illustrate the

approach, daily meteorological values recorded between 1984 and 1998 are used to predict daily

values for 1999 and water consumption values are forecasted (relative humidity data was not

available before 1984 at the utilized weather station). Figure 8 shows minimum, maximum, and

average forecasted daily consumption for 1999. Longer weather records or probabilistically-

generated records could be used to evaluate the entire distribution function or a specific percentile.


FIGURE 8. Maximum and Minimum Forecasted Daily Water Consumption in 1999.

2.5 SOFTWARE AND HARDWARE IMPLEMENTATION After the evaluation of possible methodologies is accomplished and an approach is selected, the

WDFS will be implemented using an ANSI language (e.g., C++). Different compilations of the

demand forecasting modules (e.g., DLL, ActiveX) will be used to link these modules to the

graphical user interface (GUI) within Tampa Bay Water’s Water Resources Decision Support

System. The software implementation must follow these guidelines:

• The development project shall be managed using industry-standard application

development project management, with the vendor designating a Project Manager, and

Tampa Bay Water designating a Customer Project Manager, who will interact with each

other on all project issues.

• The application will be written using industry standard-methods for application development

(subject to Tampa Bay Water technical review/approval), including modularity, a two-tiered

architecture, industry-standard computer language (Visual Basic using an object-oriented

approach preferred), user-developer team testing, change management, and thorough

documentation (technical as well as user).

• The application will be implemented on a Microsoft/Intel platform, specifically, Windows

2000 SERVER for servers, Windows 2000 PRO for PC clients, and using a client/server

architecture.

• Microsoft SQL Server 2000 will be used as the database management system to store and

manage the application’s data in a relational database. Detailed database schema

documentation, using an industry-standard tool (e.g. ERwin) will be a deliverable.


• The application will be integrated with the Tampa Bay Water Decision Support System

(which is currently in the Approval phase) using an industry-standard interface architecture

(to be determined jointly by Tampa Bay Water, the Demand Forecast vendor, and the

Decision Support System vendor).

• Microsoft Visual SourceSafe will be used as the source code control system. All developed

source code will be a deliverable to Tampa Bay Water.

3. CONCLUSIONS AND RECOMMENDATIONS

The results clearly show that the outlined approach can be effectively used to forecast monthly

and daily water consumption. This approach could be easily implemented for other points of

connection, planning areas, and total demand values. It is recommended that the outlined

approach be used to forecast monthly and daily water consumption for the points of connection

used by the OROP program for forecasting weekly or bi-weekly production schedules.

The weather data used for the above analysis was obtained from the weather station at Tampa

International Airport. It is recommended that other available weather data (ideally with longer

records) should be considered and evaluated for applicability at different points of connection and

for total service area demand values. Furthermore, more accurate weather prediction approaches

could be implemented to generate more accurate stochastic demand forecasts. It must be noted

that the above approach only uses short-term weather forecasts (one to three days). The

accuracy of this approach is highest when only one day is forecasted each time and newly

collected weather and demand data are immediately added to the database.

Forecasting weekly average demand values using all possible smoothing algorithms was not very

successful. The smallest Theil’s U value was about 0.9 indicating that a naive forecast (next

week’s value is forecasted to be this week’s observed value) appears to be as accurate as the

smoothing algorithm. It is not clear whether weekly average values can be estimated directly

based on historic demand values alone or whether some dependence on meteorological factors

and randomness must be considered. It is recommended that forecasting weekly average

demand values not be considered as an approach to forecasting short-term demands for use

within the OROP.


4. ADDITIONAL RESEARCH/MODELING NEEDS The conversion of water consumption data into water demand data should be evaluated and, if

found feasible then implemented. Then, other forecasting methods including pattern recognition

techniques, decomposition techniques, and ARIMA models (including Box Jenkins) could be

evaluated along with the above smoothing and regression techniques. It is recommended that

investigation and evaluation of water demand data be conducted as Phase III of this effort.

5. SCOPE AND BUDGET The WDFS will be developed over a period of about six to ten months. This development will be

accomplished over two phases. During the first phase, water consumption data will be collected

and evaluated for each point of connection along with the relevant meteorological data and water

demand management policies and rate structures. Then, evaluation of water demand (or

consumption) will be carried out and forecasting techniques will be applied and evaluated for each

point of connection and for total water demand. In phase 2, the WDFS will be implemented as a

component of the Tampa Bay Water’s Water Resources Decision support system (DSS) with the

appropriate input/output specifications as required by the DSS structure.

Evaluating the effect of water use restrictions and other water conservation measures on actual

water consumption will be pursued in a separate phase (Phase III). The following pages include

detailed description of the tasks required to complete each phase followed by approximate time

and budget requirements.


Task 1: Relevant Data Collection This task involves the collection of water consumption values and relevant weather data for each

point of connection. The collected data includes:

• Daily water consumption values, and

• Relevant daily meteorological data from all representative water stations. The required

daily data is precipitation, minimum and maximum temperature, minimum and maximum

relative humidity, wind speed, and pan evaporation.

After the data is collected, data gaps must be filled for daily meteorological and water consumption

data and the entire data sets must be evaluated for quality control. These checks include

evaluation of point values for significant deviation from normal or average values. For example,

maximum temperature during any day in July is expected to be within a certain range.

The collected data will be arranged into a database. The database documentation must indicate

data sources, QA/QC procedures, filled data gaps, and the method(s) used to fill those gaps.

Task 2: Evaluation of Forecasting Methodologies This task involves the evaluation of possible short-term forecasting methodologies for each of the

11 points of connection as well as for Tampa Bay Water’s total water demand using the

techniques outlined in this evaluation. It is expected that different forecasting algorithms could be

needed for different points of connection. This evaluation must include a rigorous evaluation of

considered forecasting methodologies including graphical plots of forecasted vs. observed, time

series plots, and relevant statistical measures (e.g., correlation coefficient, residual analyses,

Theil’s U, Durbin-Watson statistic).

Task 3: Application of Forecasting Methodologies This task involves the application of the best short-term forecasting methodologies that were

identified in task 2 for each point of connection and total water demand. Application results must

include forecast water demand values as required by the Optimized Regional Operations Plan

(OROP) and Tampa Bay Water’s Water Resource Decision Support System (DSS).


Task 4: Software Prototype Development This task involves the development of software prototype for implementation of the water demand

forecasting system (WDFS). The WDFS will enable users to:

• Access database and perform various analyses on underlying data,

• Forecast long-term and short-term water demand, and

• Evaluate accuracy of the implemented demand forecasting methodologies.

Development of the prototype includes the development of screen shots of the graphical user

interface including input screens, dialogues, database access routines, and output presentation.

The WDFS prototype will be presented to Tampa Bay Water staff and their feedback will be used

to enhance the user interface.

Task 5: Software and Interface Development This task involves the development of software modules and documentation for both the graphical

user interface and the forecasting modules. The development will follow the prototype established

in Task 4 except as requested or approved by Tampa Bay Water staff. The software

implementation must follow these guidelines:

• The development project shall be managed using industry-standard application

development project management, with the vendor designating a Project Manager, and

Tampa Bay Water designating a Customer Project Manager, who will interact with each

other on all project issues.

• The application will be written using industry standard-methods for application development

(subject to Tampa Bay Water technical review/approval), including modularity, a two-tiered

architecture, industry-standard computer language (Visual Basic using an object-oriented

approach preferred), user-developer team testing, change management, and thorough

documentation (technical as well as user).

• The application will be implemented on a Microsoft/Intel platform, specifically, Windows

2000 SERVER for servers, Windows 2000 PRO for PC clients, and using a client/server

architecture.

• Microsoft SQL Server 2000 will be used as the database management system to store and

manage the application’s data in a relational database. Detailed database schema

documentation, using an industry-standard tool (e.g. ERwin) will be a deliverable.

• The application will be integrated with the Tampa Bay Water Decision Support System

(which is currently in the Approval phase) using an industry-standard interface architecture


(to be determined jointly by Tampa Bay Water, the Demand Forecast vendor, and the

Decision Support System vendor).

• Microsoft Visual SourceSafe will be used as the source code control system. All developed

source code will be a deliverable to Tampa Bay Water.

• Standard (e.g., ANSI) language for the computational modules.

The developed software will be delivered for at least one month of testing by Tampa Bay Water

staff and the staff comments will be implemented in the final software version.

Task 6: Annual Maintenance and Evaluation Evaluation and maintenance of the WDFS should be carried out on an annual or bi-annual basis.

The evaluation must include rigorous quantification and presentation of forecasting algorithms

performance. Software maintenance will be needed to maintain software integrity, handle

operating system and compiler upgrade issues, fix reported problems, and add users-required

features. This task also involves preparation of a report describing any software changes,

evaluation results, and recommendations whenever needed.

Phase

No.

Task

Approximate Time Range

(Weeks)

Budget Estimate

1 Relevant Data Collection 4 – 6 $30,000

2 Evaluation of Forecasting Methodologies 6 – 8 $40,000

1

3 Application of Forecasting Methodologies 3 – 5 $30,000

4 Software Prototype Development 3 – 5 $20,000

2 5 Software and Interface Development 6 – 8 $45,000

6 Annual Evaluation and Maintenance 3 – 5 $25,000

Total (does not include annual evaluation and maintenance) 22 – 32 $165,000


PHASE III – Relationships Between Water Consumption and Demand (This effort will be solicited separately) Water consumption is affected by several operational conservation programs, consumer

conservation programs, rate structure, and ordinances developed by local governments to

encourage efficient water consumption. In this phase, the relationships between actual water

consumption and different demand management practices will be investigated. The objective is to

develop a quantitative understanding of how water conservation plans and other demand

management practices affect actual consumption. To a great extent, this phase will depend on the

amount of data available for analysis.

Task 1: Relevant Data Collection This task will involve an exploration effort to collect available relevant data. The collected data

includes all the data collected during Phase I and:

• Major water demand management policies such as:

o Timing, description, and enforcement practices of water use restrictions including

both operational practices, consumer conservation programs, and local water use

ordinances, and

o Formal and anecdotal information related to water consumption responses to water

use restrictions.

• Significant changes in consumer rate structures.

Task 2: Investigation of Available Data This task involves the investigation of whether sufficient data is available to develop a quantitative

understanding of how water use restrictions affect actual water consumption. It is not clear at this

stage whether sufficient data is available for such evaluation. If the conclusion is that available

data is not sufficient, this task will involve the development of an action plan (including a timeline)

that will result in obtaining the necessary data over time.

Task 3: Convert Consumption Data to Demand This task is conditional of the Task 2 findings. If these findings show that available data is

sufficient, this task will involve the evaluation of the effects of major rate changes and water use

restrictions on water consumption. This evaluation will then be used to convert water consumption


data to demand values. Furthermore, the results from this evaluation must provide quatitative

tools that could be used for evaluation of:

• Effects of future demand management policies, and

• Water demand curve (price vs. demand).

6. REFERENCES

Homwongs, C., Sastri, T., and Foster III, J.W., Adaptive Forecasting of Hourly Municipal Water Consumption, ASCE Journal of Water Resources Planning and Management, 120(6), 888-905,1994. Kenward, T.C. and Howard, C.D.D., Forecasting for Urban Water Demand Management, 26th Annual ASCE Conference on Water Resources Planning and Management, Tempe, Arizona, June 1998. Maidment, D.R. and Miaou, S.P., Daily water Use in Nine Cities, Water Resources Research, 22(6), 845-851,1986. Saleba, G.S., Water Demand Forecasting, Proceedings of AWWA Seminar on Demand Forecasting and Financial Risk Assessment, Denver, Colorado, 1985. Shvarster, L., Shamir, U., and Feldman, M., Forecasting Hourly Water Demands by Pattern Recognition Apprach, ASCE Journal of Water Resources Planning and Management, 119(6), 611-627, 1993. Zhou, S.L., McMahon, T.A., Walton, A., and Lewis, J., Forecasting Daily Urban Water Demand: A Case Study of Melbourne, Journal of Hydrology, 236, 153-164, 2000.

Documents

WS-Demand Forecasting Scope