# Presentation: 689064

## Short-Term Energy Forecasting

- A Presentation to the Bangladesh Ministry of Energy and Natural Resources
- September 20 - 23, 1999
- Tancred C. Lidderdale
- Energy Information Administration
- U.S. Department of Energy

## Presentation Outline

- Overview of Short-Term Energy Forecast Methods and Products
- Techniques for Creating a Bangladesh Short-Term Energy Forecast
- Forecasting Energy Prices and Transportation Demand
- Forecasting Natural Gas and Electricity Markets

## Overview of Short-Term Energy Forecast Methods and Products

-   EIA Short-Term Integrated Forecasting System (STIFS)
-   STIFS-Based Products

## The Short-Term Integrated Forecasting System (STIFS)

**National-level model of ****U.S. energy demand, supply, prices**
**Data Frequency:  Monthly data supplied by EIA and other sources**
**Forecast Horizon: 15 to 24 months out**
**Structure:**
- Over 1,000 variables
- 100 estimated series (46 demand, 36 supply, 18 price)
**Software: PROC MODEL (SAS).**

## STIFS-Based Products

**Short-Term Energy Outlook**
- Printed Publication - 2 times / year
- Published on Internet - monthly
**PC Model** - monthly
- Microsoft Windows version of the SAS-compiled STIFS simulation/forecasting model.  Updated monthly and available for download from the EIA FTP site.
**Analysis Reports** - periodic
- Impacts of price shocks, energy taxes, environmental regulations, etc. of supply, demand and prices
**Model Documentation** - periodic

## Short-Term Energy OutlookInternet Page

## Slide 7

## PC Forecast Model

## Slide 9

## What Do Our Customers Read?

## Creating a Forecasting Model

## Put the Model in Context

- Who is the audience?
- What will the forecasts be used for?
- What resources (staff and computers) will you have for developing and maintaining the model?

## Model Scope and Detail

- Forecast supply as a national total, by regions and/or by individual production units (e.g., natural gas field or electric utility plant)
- Forecast demand as a national total, by regions and/or by demand sectors
- Measurement units (e.g., physical units, Btu’s, oil equivalent, greenhouse gases, etc.)

## Formal Model Development

- Identify the concepts to be forecasted
- Derive forecasting relationships
- economic theory, engineering or institutional knowledge
- Consider alternative forecasting methodologies
- structural vs. non-structural

## Structural vsNon-Structural Models

- Structural Models - view and interpret economic data through the lens of a particular economic theory.
- Non-Structural Models - attempt to exploit the reduced-form correlations in observed time series, with little reliance on economic theory.

## Non-Structural ModelsUnivariate Processes

- Autoregressive Process - current value is a weighted average of its own past values plus a random shock.
- Moving-Average Process - current value is a weighted average of current and lagged random shocks alone.
- Autoregressive Moving Average (ARMA) Process

**Notes:**
Autoregressive and Moving Average Processes - convert random shocks into serially correlated responses.  A random shock to demand this month will persist over several future months.

Autoregressive Process order p:

	Y(t) = A0 + A1 * Y(t-1) + A2 *Y(t-2) +...+ Ap * Y(t-p) + e(t)

Moving-Average Process order q:

ARMA process must be “stationary”, i.e., not exhibit a time trend or seasonality.  Most series must be “differenced” 1 or more times to eliminate trends.

## Non-Structural ModelsMultivariate Processes

- Augmented ARMA Model
- Vector Autoregressive Model

**Notes:**
Augmented ARMA:

- Granger-Sims causality. X Granger-Sims causes y if the history of x is useful for forecasting y, over and above the history of y.

- Cointegration.  Two series are cointegrated if each contains a stochastic trend, yet there exists a linear combination of the two trends that does not. For example, each of two series x and y may contain stochastic trend, but the spread between x - y may not.

Vector Autoregression - regress each of a set of variables on past values of itself and past values of every other variable in the system

## Structural ModelsMultivariate Processes

-  Multivariate Regression
- Ordinary least squares
- Two-stage least squares
- Three-stage least squares
- Full information maximum likelihood
- Two-stage least absolute deviations
-  Neural Networks

## Recommendation: Keep It Simple

- Use forecasts produced by other government, academic, industry, or private organizations.
- macroeconomic variables
- capacity variables
- world oil price
- Use simple non-structural equations to start. Add “structure” later, estimating using ordinary least squares .

## Forecast Model Energy Linkages

**Coal**
**Demand**

**Electricity**
**Supply/**
**Demand**

**Natural Gas**
**Supply/**
**Demand**

**Petroleum**
**Products**
**Demand**

**Petroleum**
**Products**
**Supply**

**Petroleum**
**Prices**

**Electricity,**
**Natural Gas,**
**and**
**Coal Prices**

**Electricity generation from**
**coal, natural gas and residual fuel**

**Natural Gas Stocks and Demands**

**Natural Gas Prices**

**Diesel and residual fuel prices**

**Industrial sector**
**demand for**
** ****natural gas**

**Natural gas**
**liquids**
**production**

**Motor gasoline,**
**distillate, and Jet fuel prices**

**Inventories of motor gasoline,**
**distillate, and residual fuel**

**Motor gasoline, distillate  and **
**residual fuel demands**

**Motor gasoline, distillate residual**
**fuel, and Jet fuel prices**

**Natural Gas price to electric utilities**

**Electric utility demand - **
**distillate and residual fuel**

**Electricity**
**generation**
**from coal**

**Natural gas price**
**to electric utilities and**
**industrial sector**

**Forecast Model Energy Linkages**

## Forecasting Process

**Update Historical Database**
- Copy all data history from original sources
- Manually enter data not available in historical databases
- Generate required data transformations (e.g., deseasonalized series)
**Update Model Structure**
- Revise equations (if necessary) and re-estimate model with new historical data
**Enter Exogenous Forecast Data **
**Output and Distribution**
- Produce forecast and evaluate results. Adjust forecast results using “add” factors where necessary
- Make final runs, save solutions, generate reports

## Forecast Model Data Flow

## Software Issues

- Data Management
- Estimation Methods and Analysis
- Simulation Methods
- Output Options

**Notes:**
Data Management:
 Is it easy to create a single database from many different sources?

Estimation methods and Analysis
 Are necessary estimation techniques available
 Can estimation results be evaluated graphically?

Simulation Methods

Output Options

## PC-Based Software

## EIA Short-Term Forecast Contacts

** ****	****David Costello (202/586-1468)**
**World Oil Prices/International Petroleum **
**	****Doug MacIntyre (202-586-1831)**
**Macroeconomic**** **
**	****Kay A. Smith (202-586-1455)**
**Energy Product Prices **
**	****Neil Gamson (202-586-2418)**
**Petroleum Demands **
**	****Michael Morris (202-586-1199)**
**Petroleum Supply **
**	****Tancred Lidderdale (202-586-7321)**
**	****A.H. Payne (214-720-6160)**
**Natural Gas **
**	****Evelyn Amerchih (202-586-8760)**
**	****Hafeez Rahman (214-720-6160)**
**Coal **
**	****Elias Johnson (202-586-7277)**
**	****Byung Doo Hong (202-426-1126)**
**Electricity**** **
**	****Evelyn Amerchih (202-586-2867)**
**	****Rebecca McNerney(202-426-1251)**

**EIA Short-Term Forecast Contacts**

## Some Demonstrated Model Properties

## Energy Demand Impacts

**10% Colder Winter**

**Percent Deviation from Base**

- -1

- 0

- 1

- 2

- 3

- 4

- 5

- 6

- 04/97

- 07/97

- 10/97

- 01/98

- 04/98

- 07/98

- 10/98

## Energy Demand Impacts

- Total Energy

- Demand

- Total Petroleum

- Demand

- Total Nat. Gas

- Demand

- Total Coal

- Demand

- Total Electricity

- Sales

- Percent Deviation from Base

**0.0**

**0.5**

**1.0**

**1.5**

**01/97**

**04/97**

**07/97**

**10/97**

**01/98**

**04/98**

**07/98**

**10/98**

**Energy Demand Impacts**
**1 Percent per Year Higher Economic Growth**

## A U.S. Hydroelectric Power Scenario

**10**

**15**

**20**

**25**

**30**

**35**

**01/95**

**07/95**

**01/96**

**07/96**

**01/97**

**07/97**

**01/98**

**07/98**

**04/95**

**10/95**

**04/96**

**10/96**

**04/97**

**10/97**

**04/98**

**10/98**

**A U.S. Hydroelectric Power Scenario**
**Continued High Hydroelectric Availability**

**Base**

**No Change from**
**Previous Year**

**Billion Kilowatt-hours**

## U.S. Electricity Supply Impacts

**Elec. Utility**

**Hydro.**

**Generation Bill**

**Kwh**

**Elec. Utility Coal**

**Generation Bill**

**Kwh**

**Elec. Utility Nat.**

**Gas Gen. Bill**

**Kwh**

**Deviation from Base**

**-2**

**-4**

**-6**

**0**

**2**

**4**

**6**

**8**

**04/97**

**07/97**

**10/97**

**01/98**

**04/98**

**07/98**

**10/98**

**U.S. Electricity Supply Impacts**
**Continued High Hydroelectric Availability**

**Billion Kilowatt-hours**

## U.S. Natural Gas Market Impacts

**High Hydro Case**

**Total Natural Gas**

**In Underground**

**Storage**

**Natural Gas Spot**

**Price**

**Total Nat. Gas**

**Demand**

**Percent Deviation from Base**

**-1**

**-2**

**-3**

**-4**

**-5**

**-6**

**0**

**1**

**2**

**04/97**

**07/97**

**10/97**

**01/98**

**04/98**

**07/98**

**10/98**

## Evaluating Forecast Error

## Evaluating the Forecast Model

- Within-sample forecasting error
- Post-sample forecasting error with predicted values for right-hand side variables
- Post-sample forecasting error with actual values for right-hand side variables.

**Notes:**
Within-Sample Forecasting Error:
 Difference between actual historical outcomes and predicted outcomes using an equation that was estimated using all historical data.

## Measurements of Error

- Regression Model Error
- Mean Error
- Mean Square Error (MSE)
- Root Mean Square Error (RMSE)
- Mean Absolute Error (MAE)
- Mean Percentage Error (MPE)
- Mean Absolute Percentage Error (MAPE)
- Thiel Statistics

## Forecast Uncertainty

- Account for the stochastic nature of model equations and exogenous (right-hand side) variables
- Use a Monte Carlo procedure
- Requires information on
- regression equation error distributions
- regression equation estimated parameter covariances
- probability distribution of exogenous variables
- Repeated simulations following random “draws” from the distributions of the equation error, parameters, and exogenous variables

## Slide 36