Other EIA Models
Regional Short-Term Energy Model (RSTEM)DescriptionThe Regional Short-Term Energy Model (RSTEM) model is a new and greatly expanded version of the Short-Term Integrated Forecasting System (STIFS) model. While the STIFS model was almost entirely framed as a national-level model, the RSTEM uses regional as well as national data, providing a national forecast with regional detail. RSTEM is used to generate short-term (12 to 24 months), monthly forecasts of U.S. supplies, demands, imports, stocks, and prices of various forms of energy. The RSTEM model consists of over 4000 equations, including identities. The estimated equations are regression equations that, together with the identities, form a system of interrelated forecasting equations. The selection of functional form and the estimation technique is generally done on an equation-by-equation basis. The general method of estimation is ordinary least squares, or two-stage least squares. Some equations incorporate a correction for autocorrelation of the error term. RSTEM is an integrated information system, bringing together energy quantities and prices from various sources within EIA (and from elsewhere) in a consistent, time series format. This energy information is coupled with other economic and non-economic information to form a modeling database from which forecasting equations are estimated, saved and later used to produce monthly projections and reports. Other models that run outside the RSTEM system are needed to generate some forecast information, such as the macroeconomic forecasts. The most detailed regional forecasts are in the natural gas and electricity markets, partly because these markets tend to have strong regional differences and have available regional data. However, considerable effort has been made to provide regional forecasts for key petroleum products, as well. The only regional consideration for coal demand is for the demand from the electric power sector, although that is the bulk of the market. The same is true for renewables. Petroleum Products Supply Model DescriptionThe driving forces in the Petroleum Product Supply Model are estimated refinery inputs and refined product demands. Estimated refinery outputs of individual products yield share weights with which to disaggregate total refinery inputs. Net product imports and inventory change bear the burden of balancing product supply with product demand. New additions to the Petroleum Products Supply Model are:
The objective of the Regional Residential Heating Oil Model is to generate residential retail price forecasts for the four census regions: Northeast, South, Midwest, and West. Regional residential heating oil prices are estimated as a function of the wholesale distillate fuel price, regional stocks, and weather factors. Regional residential heating oil prices are then aggregated to the U.S. level by weighting regional prices by estimated regional demand factors. Regional residential heating oil prices are estimated as a function of the wholesale distillate fuel price, regional stocks, and weather factors. Regional residential heating oil prices are then aggregated to the U.S. level by weighting regional prices by estimated regional demand factors. Similarly, the national forecast for gasoline prices in the Regional Motor Gasoline Model will be determined from regional supplies and demand and both the national average price and demand are generated along with regional prices and demands. The Regional Residential Propane Model generates residential price forecasts for the four census districts: Northeast, South, Midwest, and West. Regional residential propane prices are estimated as a function of the wholesale propane price to the petrochemical sector, regional stocks, and weather. Regional residential propane prices are then aggregated to the U.S. level by weighting regional prices by estimated regional demand factors. Petroleum Products Demand Model DescriptionNonutility petroleum products consist of the following: motor gasoline, jet fuel, nonutility distillate fuel oil, nonutility residual fuel oil, liquefied petroleum gases (LPGs), and other (minor) petroleum products. The major determinants of demand for these products are: transportation activity, economic activity (i.e., gross domestic product, manufacturing output, etc.), prices and weather. Most of the estimating relationships incorporate monthly seasonal dummies and dummy variables (Dxxxx) to capture one-time events or conditions. Utility demand for distillate and residual fuel oil is derived separately through simulation of the electricity model (see Electricity Supply and Demand section). “Other” Petroleum Products Demand Model DescriptionMost discussion on petroleum product demand focuses on the five major products used in the transportation, residential, commercial, and utility sectors: motor gasoline, jet fuel, distillate fuel, residual fuel, and liquefied petroleum gas. However, the third largest category of product demand is "other" petroleum products, which is made up of 14 different products and represents about 14 percent of total petroleum product demand. Energy Prices Model DescriptionKey primary energy prices (including West Texas Intermediate oil prices and Henry Hub natural gas prices) are determined in part by expert opinion and not simply the result of models. The prices are important in their own right, because they are widely used for budget planning and other purposes by Federal and local government agencies, as well as corporate planners. These prices are also used in the projections of individual energy market prices at the national and regional level (e.g. motor gasoline, heating oil, diesel fuel, natural gas delivered to consumers and delivered electricity) and help determine overall energy supply and demand in the model. Electricity Model DescriptionThe STIFS model determines monthly aggregate U.S. electricity demand by three major sectors (residential, commercial, industrial) and provides a national-level supply balance. In STIFS, U.S. electricity supply is comprised of two major components: domestic net electricity generation (that is, electric power actually transmitted to the transportation grid by electric utility-owned and nonutility-owned power plants) and net electricity imports (principally from Canada). Generation sources (fuels used in power production) identified in STIFS are coal, petroleum, natural gas, nuclear power, hydroelectric and other renewables, including wind and solar, wood and waste, and geothermal. A catchall category representing the total of transportation and distribution losses of electricity and other items, including any pure statistical discrepancy between electricity supply and electricity demand, rounds out the demand/supply balance. New additions to the Electricity Demand Model are the: Regional Electricity Demand Module The Regional Electricity Demand Module provides average monthly demand forecasts for the national electricity balance and for the regional demand details. Time series energy-econometric models of energy consumption, supply, and prices have been built for the electricity markets. These consumption markets for each region and particular states are broken out into three sectors: residential, commercial, and industrial. Coal Model DescriptionThe RSTEM model determines total coal consumption as the total demand for four major sectors: electric power; coke plants; residential and commercial; and general industry. Electric power sector demand, the largest component of U.S. coal demand, is determined in RSTEM's electricity model. Natural Gas Model DescriptionNatural gas demand is calculated for six sectors, including four major consumption or end-use categories as well as estimated consumption of natural gas by pipelines and natural gas consumption by gas field and natural gas plan operations. In addition, a small amount of gas exports is accounted for. Weather (particularly in the residential and commercial sectors), household formation (residential sector), commercial employment (commercial sector), natural gas prices relative to competing fuel prices, and industrial output (industrial sector) are all important factors in the short-term determination of natural gas demand. New additions to the Natural Gas Model are the: Regional Natural Gas Demand Model The Regional Natural Gas Demand Model is designed to provide analytical and forecasting support by the nine U.S. Census Divisions. The discussion is confined to the non-power end-use sectors (residential, commercial and industrial). The demand for natural gas in the electric power sector is determined through the interaction of the electricity demand and supply components of RSTEM and is documented separately. The natural gas consumption market equations are aggregated into regional and national determinations of non-power end-use natural gas demand. The Natural Gas Supply and Pricing Module provides a procedure for determining equilibrium spot natural gas prices, in the context of equating broad national supply aggregates to demand aggregates built up from detailed sectoral demand representations by Census Division (or RSTEM electric supply regions in the case of power sector natural gas demand). Spot natural gas price forecasts from this module are designed to allow for efficient calculation of regional end-use sector delivered natural gas price forecasts for use in regional end-use demand flows and regional natural gas storage forecasts. Macro Bridge ModelThe Macro Bridge is designed to address the problem of maintaining regional macroeconomic forecasts (which are only available on a quarterly basis) consistent with monthly national macroeconomic forecasts, the latter of which are to serve as the basis for assumptions about growth in aggregate output, income and employment for its monthly model simulations. The national and regional macroeconomic forecasts are both supplied by Global Insight (GI). Once each quarter, the baseline forecasts for both the regional and national macroeconomic forecasts are expected to be entirely consistent. For interim monthly forecasts, however, a procedure is required to adjust the quarterly regional forecasts so that they reflect aggregate economic activity that is consistent with the monthly national forecasts. The Macro Bridge program utilizes simple scaling routines that align and update GI regional macroeconomic data and forecasts with monthly macroeconomic data and forecast updates from the GI quarterly model of the U.S. economy. Last Model UpdateFebruary 2006 Part of Another ModelNo Sponsor
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Non-DOE Data Input Sources
Most of the data sources provide monthly data and are used directly. Quarterly data are interpolated into monthly series. DOE Data Input SourcesThe historical energy data used to estimate the model come primarily from various EIA electronic databases, which merge data regularly reported in several EIA publications: Quarterly Coal Report, Petroleum Supply Monthly, Petroleum Marketing Monthly, Electric Power Monthly, Natural Gas Monthly, and Monthly Energy Review. Because of data limitations there are inconsistencies in the level of disaggregation of each type of fuel. For example, electricity and natural gas demands are represented by market sector, but petroleum products are generally represented only as national totals or for a combination of sectors (distillate and residual fuel oil are exceptions). Market-level data are available for the regulated industries (electricity and natural gas) while product-level data are available for most petroleum product markets, particularly for data frequencies higher than annual. Computing Environment
Short-Term Nuclear Annual Power Production Simulation (SNAPPS)DescriptionSNAPPS forecasts the short-Term monthly and annual electric power generation by U.S. commercial nuclear power plants. SNAPPS is a relatively simple, straightforward accounting model programmed in Microsoft Excel. The model consists of codes that provide accounting for each nuclear reactor’s generation for the projection period. Last Model UpdateNovember 2004 Part of Another ModelNo Sponsor
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Archive Media and Installation Manual(s)
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DOE Data Input SourcesForms and Publications
Models and Other
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System for the Analysis of Global Energy Markets (SAGE)DescriptionSAGE is an integrated set of regional models that provide a technology-rich basis for estimating regional energy consumption. For each region, estimates of 42 end-use energy service demands (e.g., car, commercial truck, and heavy truck road travel; residential lighting; steam heat requirements in the paper industry) are developed on the basis of economic and demographic projections. Projections of energy consumption to meet the energy demands are estimated on the basis of each region’s existing energy use patterns, the existing stock of energy-using equipment, and the characteristics of available new technologies, as well as new sources of primary energy supply. SAGE provides projections of total world primary energy consumption, as well as projections of regional energy consumption by primary energy type (oil, natural gas, coal, nuclear, and hydroelectric and other renewable resources) and projections of net electricity consumption. Projections of carbon dioxide emissions resulting from fossil fuel use are also provided. All projections are computed in 5-year intervals through the year 2030. Last Model UpdateOctober 2005 Part of Another ModelNo Sponsor
DocumentationEnergy Information Administration, System for the Analysis of Global Energy Markets, Model Documentation (SAGE), 2003, Volume 1, DOE/EIA-M 072(2003)/1 (Washington, DC, August 2003). http://tonto.eia.doe.gov/FTPROOT/modeldoc/m072(2003)1.pdf Energy Information Administration, System for the Analysis of Global Energy Markets, Model Documentation (SAGE), 2003 - Volume II - Data Implementation Guide, DOE/EIA-M 072(2003)/2 (Washington, DC, August 2003). http://tonto.eia.doe.gov/FTPROOT/modeldoc/m072(2003)2.pdf Archive Media and Installation Manual(s)Archived on a CD-R Coverage
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Computing EnvironmentSoftware Requirements: SAGE is a PC based application requiring Microsoft Windows 2000 Professional (or later) operating system as well as Microsoft Office 2000 (or later). While the SAGE source code, written in the General Algebraic Modeling System (GAMS), is publicly available at EIA’s website, three other commercial software packages are required to use this source code. These consist of GAMS along with a powerful commercial linear programming solver (e.g., XPRESS/CPLEX), and VEDA-SAGE, the data handling and results analysis “shell” overseeing all aspects of working with SAGE.
Wellhead Gas Productive Capacity Model (GASCAP)DescriptionGASCAP estimates the historical wellhead productive capacity of natural gas for the lower 48 States and projects the productive capacity for 4 years. The Short-Term Energy Outlook (STEO) output for low, base and high cases is used to estimate the number of active rigs and oil and gas well completions. The projected oil production is used to estimate the oil-well gas production (which is assumed to be producing at capacity) using a constant gas-oil ratio. The gas demand is also taken from STEO. The difference between demand and oil-well gas production is assumed to be the gas-well gas demand and the production as long as capacity exceeds demand. Last Model UpdateSeptember 2002. All SAS programs were moved from the mainframe to PC Enterprise Guide. Part of Another ModelNo Sponsor
DocumentationEnergy Information Administration, Model Documentation for the Wellhead Gas Productive Capacity Model, DOE/EIA-M052 (Washington, DC, March 1995) Archive Media and Installation Manual(s)None Coverage
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World Oil Refining, Logistics, and Demand Model (WORLD)DescriptionThe WORLD model is a linear programming model which simulates the operation of the worldwide petroleum industry based on user-specified assumptions regarding the time horizon and scenario of interest. The WORLD model simulates regional effects. Insights at the level of individual countries or refinery type can be obtained, but only where the model has been appropriately disaggregated. Last Model UpdateMay 2003 Part of Another ModelNo Sponsor
DocumentationEnergy Information Administration, WORLD Oil Refining Logistics Demand Model, DOE/EIA-M058 (Washington, DC, March 1994) http://tonto.eia.doe.gov/FTPROOT/modeldoc/m05894.pdf. Archive Media and Installation Manual(s)See Integrating Module of the National Energy Modeling System Coverage
Modeling Features
Non-DOE Input SourcesVarious industry sources for refinery processes, crude oil assays, and refined product specifications.
DOE Data Input SourcesEnergy Information Administration, International Energy Annual, DOE/EIA-0219 (Washington, DC, annually)
Computing EnvironmentSee Integrating Module of the National Energy Modeling System Software Requirements
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