Modeling & Forecasting US Electricity Consumption Prepared for 30 th USAEE North American Conference, Washington DC By Mark Hutson, PhD Candidate & Prof. Fred Joutz Department of Economics George Washington University

Objectives Simultaneously model Sector Electricity Consumption and Macroeconomic Activity Analyze feedbacks between the electricity market and the economy using monthly data Sample period is January 1987 through December 2010 Attempt to find a congruent model with long-run relationship and short-run dynamics Simultaneously model Residential, Commercial, and Industrial Electricity Demand with the Macroeconomy The determinants of consumption considered are real electricity prices, real prices of substitute, real macroeconomic variable relevant for each sector, cooling degree days and heating degree days.

Summary of Results We find a model which is congruent (white noise residuals, stable, consistent with economic intuition, explains previous results) We find a LR relationship in each sector of the electricity market – Residential Consumption elasticity is positive with income and negative with own-price – Commercial Consumption elasticity is positive with the commercial natural gas price, commercial employment, commercial hours worked, and a slight upward trend, and has a negative own-price elasticity – Industrial Consumption elasticity is positive with two substitute fuel prices (residual fuel oil, NG) and real manufacturing output, and negative with own-price We find additional error correction terms in retail sales, income, and service sector employment We successfully model three electricity sectors and the Macroeconomy simultaneously using 18 endogenous variables, 28 identities, and 6 error- correction terms

Model Selection The Model was built using the following process: – Base Sectoral Model Specification – Autometrics down-selection of variables – Identification of the LR Error-Correction Terms – Integration into a single model

Base Sector Model Specification Models of Sectoral Electricity were initially specified as dynamic single-equation relationships The left-hand side variable is the change in electricity demand in each case Each differenced variable included in the model was introduced with lags of 1-, 2-, 3-, and 12-months Additionally, where applicable, contemporaneous determinants of demand were included – Macroeconomic variables – Weather (contemporaneous, 1-, and 12-months only)

Base Sector Model Specification (continued) Long-Run relationships also entered the base model through the levels of these variables: – All long-run variables were included contemporaneously (where appropriate), and at 1- and 12-month lags – Additionally, a budget share variable (measuring nominal expenditure on electricity relative to income) was included at the 0-, 1-, and 12-month lag (Deaton & Muellbauer, 1980)

7 Autometrics – General to specific modeling algorithm Hendry and Krollzig (2001): There are five basic steps: 1.Specification of the GUM (General Unrestricted Model) by the empirical modeler. 2.Tests for mis-specification usually through residual diagnostics. 3.Begin Model Reduction Process. Investigation of possible paths for variable selection. Elimination of “irrelevant” variables.* 4.Test terminal models or paths for congruency. 5.Evaluate terminal models for final model(s) through encompassing tests. *Note that Impulse Saturation was used, which adds and potentially relevant impulse variables to improve fit and stability of parameter estimates End Result: Retain only Variables in GUM that Matter!!

The Modeled Variables Electricity Demand Residential Electricity DemandCommercial Electricity DemandIndustrial Electricity Demand Energy Variables Sectoral Electricity Prices: - Residential -Commercial - Industrial Electricity Substitute Prices: - Residual Fuel - Sectoral Natural Gas - Distillate Fuel Macro Variables Disposable Personal Income - Private Service Sector Employment -Hours of Private Service Sector Employees -Retail Sales - Industrial Production – Manufacturing - Total Capacity Utilization - Manufacturing Employment - Personal Consumption Expenditures Price Deflator (2005) - Producer Price Index – All Commodities (2005)

Variables Down-Selected from the Model Using General to Specific Approach Residential - Personal Consumption Expenditures - Electricity Budget Share (Percent of Income Spent on Electricity) - Employment to Population Ratio Commercial - Wages of Private Service Sector Employees - Electricity Budget Share (Percent of Income Spent on Electricity) - Producer Price Index – Finished Goods & Services (Alternative Price Deflator) Industrial - Total Industrial Production (rather than Manufacturing) - Manufacturing Wage - Manufacturing Weekly Hours

Identification of Error-Correction Terms The reduced models contained RHS variables in differences and levels Variables in the levels at 1-month lag were considered for the error correction term A well-specified error-correction term was identified in each electricity sector model and in three economic sectors

Residential Electricity Demand Equation Short-Run Variables VariableLagsAgg Sign Change in Electricity Demanded1, 12+ Level of Electricity Demanded12+ Heating Degree-Days0, 1, 12+ Deviations from Normalized Heating Degree-Days1, 12- Cooling Degree-Days0, 12+ Deviations from Normalized Cooling Degree-Days0, 1, 12+ Change in Real Electricity Price1- Impulse & Seasonal Variables Added Constant- Impulse VariablesJanuary, 1990; September, 1990; June, 1992 Centered SeasonalsFebruary, April, May, June, September

Residential Electricity Demand Equation Error Correction Term – All in Natural Logarithms VariableSignElasticity Electricity Demand+1 Real Disposable Income+0.4 Real Electricity Price-0.1

Commercial Electricity Demand Equation Short-Run Variables VariableLagsAgg Sign Change in Real Retail Sales3+ Change in Private Service Employment2+ Level of Private Service Employment12+ Heating Degree-Days1+ Deviations from Normalized Heating Degree-Days1- Cooling Degree-Days1+ Level of Real Electricity2+ Impulse & Seasonal Variables Added Constant, Trend- Impulse VariablesJuly, 1996 Centered SeasonalsFebruary, June, July, August, September

Commercial Electricity Demand Equation Long-Run Variables – All in Natural Logarithms VariableSignElasticity Electricity Demand+1 Real Electricity Price-.085 Real Natural Gas Price+.040 Hours of Private Service Sector Employees+.676 Private Service Sector Employment+.918 Trend

Industrial Electricity Demand Equation Short-Run Variables (In LN unless noted with *) VariableLagsAgg Sign Change in Electricity Demand1, 2+ Level of Electricity Demand12+ Change in Distillate Price2+ Change in Capacity Utilization*0- Cooling Degree-Days1+ Deviations from Normalized Cooling Degree-Days1- Change in Industrial Production (Manufacturing)0, 1+ Change in Manufacturing Employment0+ Impulse & Seasonal Variables Added Constant- Impulse VariablesDec ‘98, Jan ‘99, Feb ‘00, Jul ‘00, Jul ‘07, Feb ’04, Feb ‘08, Jun ‘09 C. SeasonalsJan, Feb, Mar, Apr, May, Jun, Jul, Nov

Industrial Electricity Demand Equation Long-Run Variables – All in Natural Logarithms VariableSignElasticity Electricity Demand+1 Real Electricity Price-.125 Real Price of Residual Fuel Oil+.037 Real Price of Natural Gas-.044 Industrial Production Index (Manufacturing Only)+.192

The Macro Error-Correction Terms Income Error-Correction Term VariableSignElasticity Real Disposable Income+1 Hours of Private Service Employees Constant Trend-.0025

The Macro Error-Correction Terms Private Service Sector Employees Error-Correction Term VariableSignElasticity Private Service Sector Employment+1 Real Retail Sales+.577 Constant Real Retail Sales Error-Correction Term VariableSignElasticity Real Retail Sales+1 Private Service Sector Employment Constant Trend

Example of Forecasts

Feedbacks Captured - Electricity Demand - Electricity Prices - Natural Gas Prices - Residual Fuel Oil Prices Real Economy - Real Disposable Income - Private Service Sector Employment - Hours of Private Service Employees -Real Retail Sale Electricity Markets

Next Steps Further testing of forecasting Capabilities Expanding the macro-modeling capabilities of the model Further refine the substitute equation models

The Electricity Error Correction Terms

The Economic Error Correction Terms