# Presentation: 769183

## Regionalizing the Short-Term Energy OutlookMeeting of the American Statistical Association (ASA)Committee on Energy Statistics

- Margot Anderson
- Director, Energy Markets and End Use
- Energy Information Administration
- April 28, 2005

## Topics

- Purpose and Objectives
- Schedule
- Overview and Key Components
- Challenges

## Purpose and Objectives

- Maintain a national perspective for EIA’s short-term outlook while providing regional detail to better understand U.S.  energy markets.
**Add relevance: ** Provide EIA customers with outlook relevant to their state or region.
**Add depth: **Gain better understanding of local/regional supply problems (pipeline/transmission constraints, regulations, etc.), which national model ignores or distorts.
**May add accuracy:  **National-level forecast based on regional forecasts may be more accurate .

## Schedule

- Began, mid-2003: Completion, late summer 2005.
- Ongoing: Model development, testing, documentation
- Expert Review: Spring/Summer 2005
- Web-Products:
- Continue monthly release (expand output?)
- Create downloadable PC version?
- Expand web presentation and query system to include regional detail?
- Provide all documentation

## Overview and Key Components 

- RSTEM expands EIA’s existing Short-Term Integrated Forecasting System model (STIFS) to include regional demand and price determinations for most energy products.
- Econometric model captures demand, inventories and prices and is used to forecast energy markets for key U.S. sectors and  regions.
- RSTEM allows more detailed treatment of the key trends and forces in major domestic energy markets than was possible under STIFS.
- The frequency of the model database remains monthly, and the forecast horizon remains at 12 to 24 months ahead.

## RSTEM Components

- Electricity
- Demand
- Prices
- Load Generation
- Dispatch Model
- Natural Gas
- Prices and Storage
- Supply
- Demand

- Petroleum
- Heating Oil
- Gasoline
- Propane
- Macro Bridge

## Degree of Regionalization Varies

- Significant regional detail for natural gas and electricity
- Strong regional differences AND available regional data.
- Census Division detail on electricity and natural gas demand.
- Market area price determination for spot natural gas.
- Census-Division (and some State) level electricity supply determination.
- More limited regional detail:
- PADD-level pricing for key petroleum products (gasoline, #2 distillate, propane).
- Census Division detail on household heating/cooling costs

## Variable Counts: STIFS & RSTEM

|  | STIFS
 | RSTEM |  |  |
| --- | --- | --- | --- | --- |
|  |  | Econometric | Dispatch | Total |
| Endogenous | 891 | 3,071 | 91 | 3,162 |
| Exogenous | 485 | 757 | 12,000 | 12,757 |
| Total | 1,376 | 3,828 | 12,091 | 15,919 |

**Notes:**
For the dispatch model,
Endogenous: whatever is currently calculated by the model and extractable under the current coding; exogenous: all separately identifiable input data elements that are needed for a solution for any month.  
The 12000 exogenous variables are mostly the capacities and heat rates of all the fossil fuel generating units in the electric power sector in the United States.  
Currently the model needs each one of these to calculate production costs and generation by fuel by region.  
If we ever summarize the regional cost curves by fuel with some functional representation, the number of exogenous variables would be reduced dramatically

## Judging Model Adequacy

- Used generally accepted criteria:
- Evaluated data for measurement error;
- Checked overall fit (R-squared);
- Tested independence of residuals;
- Examined residuals for systematic patterns;
- Tested significance of coefficients;
- Checked for bias in forecasts (mean absolute percent error)
- Etc.

## Selected RSTEM Challenges

- Determining how electricity generation will be dispatched in different regions each month.
- Avoiding aggregation errors.
- Maintaining model and data quality.
- Developing publicly accessible outputs and products.
- Developing appropriate diagnostic and model evaluation tools.

**Notes:**
Determining how electricity generation will be dispatched in different regions each month. Dispatch decisions are based on least cost strategies that vary by region, weather and plant availability all of which are difficult to determine on a monthly basis. 
Avoiding aggregation errors.  RSTEM consists of many regional and sub- regional equations, which are subject aggregation error.
Maintaining model and data quality.  Cost of maintaining more complicated model is higher than currently used version. This requires staff time to look at the data (which has well defined problems) and the projections in much more detail that was otherwise the case.
Developing publicly accessible outputs and products.  Producing relevant, publicly accessible understand results which the public has not become used to.  This requires staff time to research and answer the questions related to a new product.
Developing appropriate diagnostic and model evaluation tools.  As model grows in complexity, need to develop automatic tools to check data and modeling errors. Need to develop tool to assess accuracy of forecast.