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Seattle Public Utilities Water Demand Forecast Model

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Boston Chicago Columbus. Denver East Bay MUD Eugene. Los Angeles MWDSC New York ... expertise, and data) and general agreement on the proper model specification. ... – PowerPoint PPT presentation

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Title: Seattle Public Utilities Water Demand Forecast Model


1
Seattle Public Utilities Water Demand Forecast
Model
  • July 6, 2006
  • Bruce Flory, SPU Principal Economist

2
Introduction
  • Goals of Demand Forecasting
  • Literature Review
  • Model Structure, Inputs and Assumptions
  • Modeling Uncertainty

3
Goals
  • Good Decisions
  • Identify causal factors
  • Account for uncertainty
  • Flexible
  • Scenarios uncertainty
  • Transparent
  • Transferable

4
Literature Review
  • Sources
  • Web research
  • Academic literature on demand forecasting, price,
    and conservation
  • Other water electric utilities and consultants
  • Forecasting methods were reviewed from
  • Boston Chicago
    Columbus
  • Denver East Bay MUD
    Eugene
  • Los Angeles MWDSC
    New York
  • Phoenix Portland
    San Diego
  • San Francisco Tacoma
    Tampa Bay
  • Tucson Washington DC

5
Lit Review Forecast Methods
  • Trend Analysis
  • Per Capita Flow Factors
  • Sectoral Disaggregation
  • Fixed Flow Factor Approach
  • End Use Models
  • Econometric Modeling
  • Variable Flow Factor Approach

6
Lit Review - Conclusions
  • Econometric modeling is the technique of choice
    given adequate resources (time, expertise, and
    data) and general agreement on the proper model
    specification.
  • Such agreement does not exist
  • Correct price variable
  • Difficulty with statistically separating out
    impacts of price, code, programs, public
    information, new technology
  • State of the art is unsettled
  • However, much more agreement exists on the
    magnitude of price and income elasticities

7
Lit Review Conclusions (cont.)
  • Take advantage of the econometric analysis done
    by others using a Variable Flow Factor approach
  • Best achieves goals
  • Assumptions can easily be made and varied (for
    sensitivity analysis)
  • Simple yet takes account of many explanatory
    variables
  • Modest data requirements

8
Lit Review Elasticities
9
Variable Flow Factor Model
  • Current water demand flow factors by sector for
    Seattle and each wholesale customer.
  • Impacts of variables such as price, income and
    conservation on water flow factors for each
    sector over time.
  • Forecasts of households and employment.

10
Inputs for Variable Flow Factor Model
  • For Base Year Water Flow Factors
  • Current consumption
  • Current households and employment
  • Other Inputs Affecting Future Flow Factors
  • Future income growth (forecast)
  • Income elasticity of demand (literature review)
  • Future growth in water prices (forecast)
  • Price elasticity of demand (literature review)
  • Future conservation savings (from CPA model)
  • Conservation overlap function

11
Forecast Model Inputs (cont.)
  • Forecasts of households and employment
  • Source PSRC
  • Forecasts extrapolated beyond 2030
  • Other Adjustments to Forecast
  • Forecast of non-revenue water
  • Forecast of other sources of supply
  • Potential new wholesale customers
  • Block contracts

12
Demand Model Structure 1
13
Demand Model Structure 2
14
Demand Model Structure 3
15
Actual and Forecast Water Demand 1980-2060
16
Uncertainty
  • High confidence in forecast, yet....
  • Actual unlikely to be exactly as forecast
  • Sources of uncertainty
  • Forecasts of input variables
  • Elasticities
  • Other assumptions

17
Discrete vs. Continuous
  • Discrete Specific events that produce
    significant sometimes abrupt changes
  • Example Weyerhaeuser Water Right
  • Handled by running individual what-if scenarios
  • No probabilities assigned
  • Continuous uncertainty that surrounds many inputs
    to the model
  • Represented by probability distribution around a
    mean value

18
Continuous Uncertainty
19
Model Inputs with Continuous Uncertainty
  • Projections of growth in single and multi-family
    households
  • Projections of employment growth
  • Projected annual growth rate in real water prices
  • Price elasticities of demand
  • Projected annual growth rate in real household
    income
  • Income elasticity of demand
  • Projected conservation savings

20
Modeling Demand Uncertainty
  • Estimate range of uncertainty around model inputs
  • Assign probability distributions
  • Run Monte Carlo simulations
  • 10,000 iterations

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24
Implications
25
Seattle Public Utilities Water Demand Forecast
Model
  • Discussion
  • Q A
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