# Presentation: 672345

## Short-Term Energy Forecasting: Methodology and Examples

- Dr. Mark Rodekohr
- May 2000
- Kathmandu, Nepal

## Outline

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

## 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

## PC Forecast Model

## Graphics Example

## What Do Our Customers Read?

## Creating a Forecasting Model

- 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.

## 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**

## 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

## 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

- 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.

## 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.

## 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 30

## Forecasting Oil Prices U.S. Imported Crude Oil Price

## OPEC Compliance with Rate Cuts

## Long Term Crude Oil Price Outlook

- Less than 1% increase in real world oil price.
- Rate of increase declines after 2005.

**Real World Oil Prices, 1970-2020**
**(1997 US dollars)**

- Source: EIA, *Annual Energy Outlook 1999*,

## Retail Gasoline Price Impacts

**1.0**

**1.1**

**1.2**

**1.3**

**1.4**

**1.5**

**1.6**

**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**

**Dollars per Gallon**

**Base**

**10% Higher Oil Price**

## Crude Oil Price Forecasts

**Solid line - historical crude oil price**
**Dashed lines - Forecasts published during**
**                         ****1st quarter of each year**

**U.S. Refiners’ Average Imported Oil Acquisition Cost**

## Assume Crude Oil Volatility is 33.3%
Assume Current Price is $15 / barrel

Then, We Expect for 2/3 of Next Year:
Oil will fluctuate between $10 and $20 per barrel
$15 - 0.333 ($15) = $10
$15 + 0.333 ($15) = $20

- Assume Current Price is $15 / barrel
- Then, We Expect for 2/3 of Next Year:
- Oil will fluctuate between $10 and $20 per barrel
- $15 - 0.333 ($15) = $10
- $15 + 0.333 ($15) = $20

- Price Volatility: Example

**Notes:**
Volatility is not particularly useful for making price forecasts. Measures of price volatility are useful, however, for comparing price risk across commodity markets and over time.

## Price Volatility1998 NYMEX Crude Oil Futures

- Price Volatility1998 NYMEX Crude Oil Futures

**Notes:**
As expected, the volatility of futures prices declines the farther out the contract. This observed relationship for 1998 NYMEX WTI crude oil contracts also holds for other years, and also NYMEX natural gas contracts and IPE Brent crude contracts.

Increases in spot price volatility are reflected in increases in the volatility of futures prices. In general, volatility of prices 8 months forward is about 1/2 of spot price price volatility. Higher volatility in spot and futures prices is usually associated with sustained increases or declines in the price level rather than in larger random price fluctuations.

## Price VolatilityCrude Oil and Petroleum Products

- Price VolatilityCrude Oil and Petroleum Products

**Notes:**
The price volatility of petroleum products are closely tied to the price volatility of crude oil.  Residual fuel oil and bunker fuel price volatility may also be influenced by natural gas prices and weather because of electric utility demand for No. 6 oil.

Data Sources:
 Crude - WTI Midland spot price, 8/11/93 - 9/2/98 (Reuters)
 Unl Gasoline USG - Unleaded regular prompt U.S. Gulf Coast, 1/1/93 - 9/2/98 (Reuters)
 Bunker 180 CST USG - Houston U.S. Gulf spot price, 4/30/93 - 8/28/98 (Reuters)

## Price VolatilityCrude Oil and Natural Gas

**Natural Gas**

**WTI Midland**

- Price VolatilityCrude Oil and Natural Gas

**Notes:**
The price of natural gas has been significantly more volatile than the price of crude oil. But, this may be due to greater seasonality in natural gas prices, which has not been corrected for.

Data Sources:
 Crude - WTI Midland Spot Price, 8/11/93 - 9/2/98 (Reuters)
 Natural Gas - 1st nearby futures, New York Mercantile Exchange, 1/13/94 - 9/2/98 (Reuters)

## Price VolatilityCrude Oil and Grains

- Price VolatilityCrude Oil and Grains

**Notes:**
Data Sources:
 Crude, Light Sweet - 1st nearby futures, New York Mercantile Exchange (DRI)
 Corn, No, 2 Yellow, 2nd nearby futures, Chicago Board of Trade -  (DRI)
 Wheat, 2nd nearby futures, Chicago Board of Trade -  (DRI)

## Price VolatilityCrude Oil and Metals

- Price VolatilityCrude Oil and Metals

**Notes:**
Data Sources:
 Crude, Light Sweet - 1st nearby futures, New York Mercantile Exchange (DRI)
 Gold, Bar, 2nd nearby futures, Commodity Exchange (DRI)
 Silver, 1,000 oz., 2nd nearby futures, Chicago Board of Trade (DRI)

## Price VolatilityCrude Oil and Financials

- Price VolatilityCrude Oil and Financials

**Notes:**
Data Sources:
 Crude, Light Sweet - 1st nearby futures, New York Mercantile Exchange (DRI)
 T-Bonds, 15-year, 2nd nearby futures, Chicago Board of Trade (DRI)
  S&P 500 Stock Index, 2nd nearby futures, Chicago Mercantile Exchange (DRI)

## Price VolatilityCrude Oil and World Commodities

- Price VolatilityCrude Oil and World Commodities

**Notes:**
Data Sources:
 Crude, Light Sweet - 1st nearby futures, New York Mercantile Exchange (DRI)
 Coffee, Arabica, 2 nearby futures, Coffee, Sugar, Cocoa Exchange (DRI)
 Sugar, Raw No. 14 Domestic , 2 nearby futures, Coffee, Sugar, Cocoa Exchange (DRI)

## Nepal Energy Demand Overview

- All of the following data are
- For commercial fuels exclusively

## Economic Growth and Energy Demand Growth, 1988 - 1997

**Petroleum (0.8%)**

**Natural Gas (2.2%)**

**Electricity (2.8%)**

**Electricity (6.6%)**

**Petroleum (9.3%)**

- United States

- Nepal

- 5.2% / year

- 2.4% / year

## Nepal: Total Energy Use

- Average Annual Growth Rate: 8.4%

## Nepal Per Capita Energy Demand

- Average Annual Growth Rate = 5.5%

## Nepal: Energy Use per GDP $

- Average Annual Growth: 3.8%

## Nepal Hydro Generation

- Average Annual Growth: 10.8%

## Nepal: A Simple Energy Demand Model

- Express total energy demand as a function of GDP
- Then add population to the equation

## Model Results

## Petroleum Demand Shares

**Nepal 1997**

**United States 1998**

**Source: Energy Information Administration**

## Petroleum Demand

## Simple Model of Nepal Petroleum Demand

- Variables:
**Demand** = total annual average petroleum demand (thousand barrels per day)
**GDP** = real GDP (billion $’s, 1985 basis)
**Price** = real oil price (US $/bbl) – (results shown with and without the price variable)

## Petroleum Demand Results

## Motorization and Income 

- Motorization is highly correlated with income.  Differences in land use can lead to different levels, but not different paths, of motorization.
- Severe constraint, as in Singapore and Hong Kong, is the only way to limit motorization.

**Source:** World Bank

## Nepal Electricity Demand per Capita

- Average Annual Growth Rate: 6.3%

- “In 1986, the level of electricity consumption was only 20 kWh per person
- per year (compare with 11 kWh per person per *day* in the U.S.)”

**Notes:**
U.S. per capita natural gas demand = 76,000 cu feet per year

## Simple Model of Nepal Electricity Demand

- Expresses electricity use per person as a function of gross domestic product.

## Slide 59

**Notes:**
Demand Side Inputs
 Residential sector - number of houses, weather, and time trends 
 Commercial sector - total employment, weather, and time trends
 Industrial sector - industrial output indexes, weather
 “Other” sector - real GDP

Supply Side Inputs
 Oil-fired generation - balancing item
 Gas-fired generation - market price ratios, total electricity generation, hydroelectric generation, nuclear generation, and geothermal generation
 Coal-fired generation - market price ratios, total electricity generation, hydroelectric generation, nuclear generation, and geothermal generation
 Nuclear power generation - exogenous
 Hydroelectric generation - exogenous
 Other renewables generation - time trend
 Net sales by nonutilities - exogenous
 Net Imports - exogenous
 Distribution losses and discrepancy - total electricity demand

## Conclusion

- Short-Term forecasting can be a valuable analysis tool used by a wide variety of analysts both inside and outside of the central government.
- Short-Term forecasting models do not need to be elaborate.
- Start with simple models and add complexity as needed.