DEMAND & ENERGY
BANDWIDTH FORECAST
1999 – 2008
February 2000
Prepared by the Southwest Power Pool
BANDWIDTH WORKING GROUP
(BWG)
Southwest Power Pool
BANDWIDTH WORKING GROUP
Robert Shields, Chair
Senior Rate Analyst
Arkansas Electric Cooperative Corp.
Kelly Harrison
Manager, Revenue & Forecasting
Western Resources
Brett Parks
Forecasting Analyst
Central Louisiana Electric Company, Inc.
Sharad Rastogi
Senior Planning Engineer
Western Farmers Electric Cooperative
Chris Jamieson
Engineer I
Southwest Power Pool
Southwest Power Pool
DEMAND & ENERGY BANDWIDTH FORECAST
1999-2008
TABLE OF CONTENTS
EXECUTIVE SUMMARY .................................................................................................. i
INTRODUCTION ............................................................................................................. 1
METHODOLOGY ............................................................................................................ 1
ECONOMIC ANALYSIS .................................................................................................. 5
WEATHER ANALYSIS .................................................................................................... 6
LOAD FACTOR ANALYSIS ............................................................................................ 6
CONCLUSIONS AND RECOMMENDATIONS ............................................................... 7
NORTHERN SUBREGION APPRAISAL......................................................................... 9
SOUTHERN SUBREGION APPRAISAL ....................................................................... 12
Appendix
Study Results
Southwest Power Pool
DEMAND & ENERGY BANDWIDTH FORECAST
1999-2008
DATA REPORT
TABLE OF CONTENTS
Counties in Partially Served States
State Level Econometric Data
Plots of State Level Econometric Data
SAS Programming Code to Create Subregional Modeling Variables
SAS Datasets
SAS Modeling Code and Output
(This Data Report is contained in a separately bound document and is available upon request from the SPP Office.)
Demand & Energy Forecast
1999-2008
EXECUTIVE SUMMARY
The Bandwidth Working Group developed high and low growth rate scenario bands about the current
Southwest Power Pool demand and energy forecast as reported to the U.S. Department of Energy in the
April 1999 EIA-411 Report. The results of this effort were produced through econometric modeling of the
two newly formed Sub-regions. Aggregating the Sub-regional results produced the SPP Regional values.
The following table summarizes growth rates that bound the most likely range of occurrence under normal
weather conditions. The results of the 1995 forecast are also shown for comparison.
Annual Compound Forecast Growth Rates
(%/Year 1999–2008)
(%/Year 1995–2004)
Low
Base
High
Low
Base
High
Peak Demand
1.3
2.0
2.6
1.0
1.7
2.3
Annual Energy
1.4
2.1
2.8
1.1
1.8
2.4
SPP Peak Demand Forecast
SPP Net Energy Forecast
1988-2008
1988-2008
50
240
220
Thousands of GWh
Thousands of MW
45
40
35
30
25
1988
200
180
160
140
1993
1998
EIA-411
2003
120
1988
2008
1993
Economic
1998
EIA-411
2003
2008
Economic
Member systems continue to forecast similar growth of future demand and energy requirements
compared to the 1999 EIA-411 Report. The annual compound growth rate on peak demand for the next
10 years increased from 1.7 %/yr in 1995 to 2.0 %/yr in 1998. Actual peak demand has grown at 3.0 %/yr
from 1988 to 1998. The annual compound growth rate on energy for the next 10 years increased from
1.8 %/yr. in 1995 to 2.1 %/yr. in 1998. Actual energy has grown at 3.4 %/yr.
SPP
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Demand & Energy Forecast
1999-2008
The econometric predictor variables applied in the 1999-2008 forecast are projected to be more stable
than in the 1995-2004 forecast. This can be seen throughout the two subregional assessments. The
regional forecast growth rates for the high, low and base economic scenarios for both the 1999 and 1995
forecasts are shown on the previous page. The demand and energy growth rates for both the high and
low economic scenarios have about the same variance from the base forecast in 1999 compared to 1995.
Confidence intervals representing a 95% probability of occurrence were developed for a 30-year average
to account for extreme weather effects. These variables were then place in the base energy forecast
model in place of the normal values to generate energy bands, which represent the effects due to
extreme weather. The extreme weather demand bands were derived from the extreme weather energy
bands using one standard deviation from the 10-year mean load factor. Should weather extremes occur
in any given year in addition to a low or high growth scenario, the bands are broadened as shown in the
following table and charts. These weather uncertainty percentages can be applied to all economic growth
scenarios.
SPP Peak Demand Forecast
SPP Net Energy Forecast
1988-2008
55
250
50
230
45
210
Thousands of GWh
Thousands of GWh
1988-2008
40
35
190
170
150
30
25
1988
1993
EIA-411
1998
2003
Economic
130
1988
2008
Weather
1993
1998
EIA-411
Economic
2003
2008
Weather
Average Extreme Weather Band (% from base 1999-2008)
SPP
Low
High
Peak Demand
94.6
108.1
Annual Energy
95.3
104.8
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January 2000
Demand & Energy Forecast
1999-2008
INTRODUCTION
The Bandwidth Working Group (BWG) was organized in April of 1990 for the purpose of developing a
methodology and producing high and low growth scenario bands about the Southwest Power Pool’s
(SPP) most recent U.S. Department of Energy OE-411 Report demand and energy forecast. The 2000
SPP Demand and Energy Bandwidth Forecast Report presents the results of the fourth effort of the BWG
and represents a continuation of a recommended methodology for such efforts to be performed on an
every-other-year basis.
The BWG was formed under the Forecasting Subcommittee (FS) of SPP, which contained a
representative from each member system. The FS was created at the recommendation of a former
Bandwidth Load Forecasting Task Force under the Engineering & Operating Committee (EOC). This task
force was formed in November 1986 following direction to investigate bandwidth forecasts from the May
1986 Board of Directors’ meeting. This initial task force produced a bandwidth forecast from available
data and computer resources within its membership. Their effort concluded with a recommendation to
continue research and development efforts in this area to produce a recommended methodology for
future SPP bandwidth forecasts.
The Forecasting Subcommittee’s responsibilities were transferred to the EOC in 1997. The Bandwidth
Working Group was retained and now reports to the EOC. The U.S. Department of Energy OE-411
Report is now referred to as the EIA-411 Report. In the previous OE-411 and EIA-411 Reports, SPP was
divided into three subregions; Northern, Southeastern, and West Central. Because of the change in
member systems in the Southeastern subregion between 1995 and present, SPP has decided to combine
the Southeastern and West Central subregions into a newly formed Southern subregion in the 2000 EIA411. The BWG has also adopted the newly formed Southern Subregion even though the 1999 EIA-411
Report still uses the original three sub-regions.
METHODOLOGY
General
Based on research efforts of the previous task force, the NERC Load Forecasting Working Group and the
forecasting methods of major electric utilities throughout the U.S., the BWG determined general
characteristics for SPP bandwidth forecasts.

Forecast bands will be produced through econometric modeling

Forecast bands will be produced for SPP and its two sub-regions
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Demand & Energy Forecast
1999-2008

Forecast bands will span the 10 year EIA-411 Report forecast

Annual energy and peak demand forecast bands would be developed

Demand will be derived from forecast energy through projected load factor

Ten years of historical data will be used

The discrete time increment of data will be calendar quarters
The former Weather Normalization Task Force (WNTF) developed weather variables for the econometric
models.
Computer Software
A vast array of software and statistical techniques exist and are used by electric utilities in forecasting
efforts. Software options identified as being appropriate for SPP use included EPRI’s Forecast Master
Plus and Fortell, Microcast, Micro TSP, and SAS. The wide industry acceptance of SAS software and its
hardware independent coding capabilities were reasons for its selection to handle the computational
needs of the BWG. Many SPP members and other regional councils also use SAS software.
Economic Data
Because of the need for high and low scenarios, the large geographical area of SPP, the varying
economic climate of each Subregion, the fact that SPP encompasses all of some states while only a
portion of others, an extensive database of economic variables was required. Economic data sources
identified as being appropriate for the SPP effort included WEFA, DRI, Woods & Poole, Perryman
Consultants, and U. S. government. Based on quotes received and the widespread use of DRI data by
member systems, the BWG selected DRI as the data source for the 1991 bandwidth effort. Because of
the poor quality of this data, the BWG distributed a “Request for Proposal” to all of the vendors listed
above. Only DRI and Perryman responded and the BWG selected Perryman Consultants to provide the
economic data for the 1993 effort. Forecasting models were tested using various combinations of the
following variables purchased from Perryman Consultants.
Number of Households
Industrial Production Index
Total Employment
Real Disposable Income
Real Residential Natural Gas Price
Real Residential Electricity Price
Data supplied by Perryman covered the period from the first quarter of 1980 to the last quarter of the year
2011 and is based on the most current available data. SPP encompasses all of two states; Kansas and
Oklahoma, and a portion of five states; Arkansas, Louisiana, Mississippi, Missouri, New Mexico, and
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Demand & Energy Forecast
1999-2008
Texas (two separate areas). State data for the econometric variables was received for states wholly
within SPP territory. County level data for the partially served states was aggregated to create the state
level variables. Economic data was provided in two separate parts for Texas. One portion included the
panhandle (SWPS territory), which is now included in the Southern Subregion. The second portion
included the northeastern area (SOEP territory), which is also in the Southern Subregion.
Weather Data
Normal weather data was developed by the WNTF based on a weighted analysis of actual data for
heating and cooling degree-days for 12 sites across SPP. The WNTF requested 30 years of National
Oceanic and Atmospheric Administration (NOAA) data in TD-3210 Summary of Day digital data format for
12 sites within the SPP Region. The list of these sites with their associated NOAA identification number
is listed by SPP Subregion.
NOAA Weather Stations
Northern Sub-Region
Southern Sub-Region
Wichita, KS - 03928
Kansas City, MO - 03947
Dodge City, KS - 13985
Springfield, MO - 13995
Topeka, KS - 13996
Shreveport, LA - 13957
N. Little Rock, AR - 13963
Ft. Smith, AR - 13964
Oklahoma City, OK - 13967
Tulsa, OK - 13968
Baton Rouge, LA - 13970
Amarillo, TX - 23047
The former WNTF determined early in the investigation that, at a minimum, a variable should be
developed for degree-days. A degree-day, as used in this study, was calculated as the sum of the daily
minimum temperature and maximum temperature, quantity divided by two with sixty-five subtracted from
the result (sixty-five degree base). Positive results were labeled as cooling degree-days (CDD). For
negative results, the absolute value is taken and the values labeled as heating degree-days (HDD). The
station daily values were then summed for each calendar month.
Since a quarterly time increment was chosen by the BWG, the WNTF determined that monthly degreedays should be weighted by the energy level represented by each weather station as the monthly data
was aggregated into a quarterly data series. Member systems were assigned by a particular weather
station for this weighting process by location of service territories as follows.
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Demand & Energy Forecast
1999-2008
Weather Station Assignments
Northern Sub-Region
KACY
SIKE
INDN
SPRM
EMDE
KACP
MIDW
SUNC
03947
13995
03947
13995
13995
03947
13985
13985
MIPU
WEPL
KAPL
KAGE
Southern Sub-Region
03947
13985
13996
03928
AREC
CELE
CLWL
LAFA
LEPA
GRRD
NTEC
OKGE
13963
13970
13963
13970
13970
13968
13957
13967
OMPA
PSOK
SOEP
SWPA
SWPS
WEFA
13967
13968
13957
13964
23047
13967
Modeling
Datasets for each Subregion and SPP were established with 11 years of historical and 10 years of
forecasted quarterly data. Simulations were run on this data using the SAS software. Using statistical
routines, the BWG selected the variables which resulted in the forecast that was statistically sound and
best reproduced the EIA-411 Report energy forecast submitted by member systems. In some cases,
economic activity could normally precede a load change. In an effort to improve the ability of the model to
match historical, actual, and EIA-411 Report forecasted energy, some of the predictor variables were
lagged by a specific time period in 1991. This year's models did not utilize this approach.
Regression for the base energy forecast established relationships between energy and the predictor
variables for the historical time period 1988 thorough 1998. These relationships were then applied to the
forecast of these variables to predict future energy requirements. This step was performed using
standard regression analysis. Obtaining an exact match of the base forecast to the EIA-411 Report
forecast was not attempted. Perryman's high and low scenarios of the expected forecast of the economic
variables were used to develop bands about the EIA-411 Report forecast. All models were reviewed with
respect to the following statistical parameters.
T Test > 2.0,
R2  1.0,
Non-correlated variables,
Signs of variable coefficients in the correct direction,
Reasonable elasticity of variable coefficients,
Durbin-Watson Test  2.0,
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Demand & Energy Forecast
1999-2008
Assumptions
The base forecast obtained from the econometric modeling is an adequate approximation to the EIA-411
Report forecast and suggests that the high and low scenario bands produced from the models are a good
approximation to apply to the base EIA-411 Report values. In addition, historical weather data was used
to develop sensitivities due to weather extremes. These variations along with an analysis of load factor
variations were used to develop demand bands. Demand Side Management (DSM) impacts and Non
Utility Generation (NUG) effects are not explicitly modeled. These impacts are assumed to be included in
the adjustment of the modeled forecast to the EIA-411 Report forecast and in the input data series. Any
DSM and NUG impacts included in the EIA-411 Report forecast are therefore inherent in the base
forecast, but the level of uncertainty is not known.
ECONOMIC ANALYSIS
Economic variables utilized in preparation of the subregional bandwidth forecasts vary due to the large
and diverse territory that encompasses SPP. Variables were selected for use in the subregional forecast
models based on their statistical significance to the forecast model equation. The following is a detailed
description of the economic variables included in the model by subregion. All growth rate references are
in terms of annual compound growth rates.
The primary economic driver for the Northern subregion is the number of households. The sources for
this variable are primarily from census data with frequency conversions as described by Perryman's
report included in the Data Report. Household growth rates for the low, base and high-economic growth
scenarios over the forecast period were 1.25, 1.51 and 1.73 percent per year respectively. This
compares favorably to the entire SPP Region for the low, base and high-energy growth scenarios at 1.29,
1.55 and 1.77 percent per year over the forecast period.
The combination of the Southeastern and West Central subregions led to the forecast model
development of a new Southern subregion. The West Central subregion model utilized three economic
variables: real disposable income, number of households, and real price of electricity. The real
disposable income series and the number of households series were combined to create a series called
“real disposable income per household.” This created variable did not pass the statistical T test and was
not used to model the Southern Subregion. The Southeastern Subregion’s primary economic driver was
the industrial production index. After testing the significance of the four variables, the newly formed
Southern Subregion forecast model used two of the economic variables: the real price of electricity and
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January 2000
Demand & Energy Forecast
1999-2008
the industrial production. Projected annual growth for the real price of electricity series under the low,
base, and high-economic growth scenarios were –0.10, -0.64, and –1.30 percent per year, respectively,
for the subregion. Projected growth for the entire SPP region for the corresponding scenarios were –
0.10, -0.64, and –1.30 percent per year. The resulting industrial production index series for the low, base,
and high-economic growth scenarios produced growth rates of 2.15, 2.52, and 2.89 percent per year,
respectively, for the forecast period. These rates are projected at 2.16, 2.53, and 2.90 percent per year
for corresponding SPP Regional scenarios.
WEATHER ANALYSIS
Following review, the decision was made to continue with the methodology used in the previous effort to
capture weather effects in the energy modeling process. As such, the ad hoc Weather Normalization
Task Force was disbanded and the members absorbed into the BWG. Actual quarterly heating and
cooling degree-day data streams were acquired from the NOAA for 1969 through 1998. Taking a 30-year
average, developed normal quarterly heating and cooling degree-day values. These normal quarterly
values were used for 1999 through 2008 in modeling the base forecast.
Extreme quarterly data was developed from the 30-year data using 2 standard deviations (95%
confidence) from the mean on annual maximum degree-days. These values were then prorated back to
quarterly data based on the 30-year quarterly analysis. Additional analysis of the historical weather data
also verified the belief of the WNTF that there is no correlation between quarterly heating and cooling
degree-days (weather is random). The models were also evaluated with the 30-year average (normal)
weather variables in place of actual data for the past 10 years to (weather normalize) historical annual
energy for the two Subregions.
LOAD FACTOR ANALYSIS
Demand bands for expected weather conditions were derived from the econometric energy bands by
applying load factors as projected by member systems in EIA-411 Report. Ten years of historic load
factors were analyzed to determine a methodology of developing high and low growth scenario bands on
demand. The expected load factor, as projected in the EIA-411 Report, was again used to convert the
base, high economic growth, and low economic growth energy values to demand. With currently
available data, the use of other than base EIA-411 Report load factors for conversion of high and low
growth scenario bands could not be supported. Detailed analysis by customer class would be required to
justify the use of a variation from base load factors. In order to determine the effect of extreme weather
SPP
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Demand & Energy Forecast
1999-2008
on peak demand, quarterly maximum and minimum load factors representing one standard deviation
either side of the mean were calculated and used to develop the demand bands. Load factor values one
standard deviation below the mean applied to the high-energy band were used to calculate the high
demand band. Load factor values one standard deviation above the mean applied to the low energy
band were used to calculate the low demand band. The BWG continued to use the load factors one
standard deviation either side of the mean (as opposed to the mean) to account for the greater amount of
weather related uncertainty associated with peak demand as compared to annual energy. This step does
not, however, allow a definitive statement as to statistical confidence interval as is applied to the bands
about energy.
The BWG discontinued the efforts to adjust historic demand and energy for abnormal weather. Energy is
somewhat normalized for the subregional and regional areas. Also, the attempt to weather normalize
non-coincidental demand is not meaningful with comparable coincident data for comparison. An attempt
to collect this coincidental data was being directed by the former Forecasting Subcommittee.
CONCLUSIONS AND RECOMMENDATIONS
The BWG has determined that the methodology developed is still a logical process from which future
bandwidth forecasts should continue to be developed. This study effort produced results that are
reasonable and helpful in assessing a degree of confidence in the EIA-411 forecast. To obtain betterforecast models in future BWG reports, more study will be needed to find better econometric variables
that model such large and diverse subregions.
The BWG successfully developed models that substantially replicated the EIA-411 historic data for the
two subregions, yet the developed models for the forecasted years are comparably not as reasonably
accurate as past forecast models. In using the 1999 data purchased from Perryman Consultants, the
Northern forecast indicated a considerable higher rate of load growth than was projected in the EIA-411
Report, and the Southern model indicated a lower rate of load growth than the EIA-411. Since the
models are used only as a reference point to find the economic high and low bands for the EIA-411
forecast, it was decided that no further attempt should be made to get more accurate models.
The BWG does not believe this finding indicates a flaw in the EIA-411 Report forecast. The EIA-411
forecast is an aggregate of SPP members’ forecast. Each SPP member has vastly more data and a more
intimate knowledge of its service territory. Thus, each member should be able to produce the best
SPP
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January 2000
Demand & Energy Forecast
1999-2008
possible forecast for its service territory. For this reason, the BWG believes the EIA-411 Report contains
the best forecast available.
The possibility of high load growth should be brought to the attention of SPP resource planners. In
making long term resource decisions, SPP planners should at least be aware that the range of
possibilities are varied. The BWG recommends that the forecast bands continue to be updated on an
every-other-year basis.
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Demand & Energy Forecast
1999-2008
NORTHERN SUBREGION APPRAISAL
EIA-411 Report Observations
The Northern Subregion of SPP consists of member systems operating in the states of Kansas and
Missouri. An analysis of data reported by member systems in the EIA-411 Reports over the last 10 years
reveals the results shown in the following table.
Annual Compound Growth Rates (%/year)
1988 - 1998 Actual
1999 - 2008 Forecast
Peak Demand
2.4
2.15
Annual Energy
2.9
2.24
Forecast Model
A routine called PROC REG, within the SAS software, was used to model the Subregion energy growth.
The variables selected for the model were cooling degree-days, heating degree days, and households.
The model passed all standard statistical tests. The functional format and parameter estimates of the
model, and calculated results, can be found in the Data Report. A graphical comparison of the model
results to EIA-411 Report historical and forecast energy is shown.
SPP Northern Subregion
Actual & Forecast Energy
Thousands of GWh
90
80
70
60
50
40
1988
1993
1998
EIA-411
SPP
9
2003
2008
Model
January 2000
Demand & Energy Forecast
1999-2008
An objective of this effort was to develop a model which replicated the EIA-411 Report forecast as closely
as possible; then, use this model to develop the scenario bands around the base EIA-411 Report
forecast. The results shown for 1988 through 1998 indicate how well the model would have predicted
history given perfect knowledge of weather and economics. As a result, in Perryman Consultants’ high
growth rate in the Number of Households variable, the Northern Subregion model took on a higher annual
growth rate than was predicted by the member systems in the EIA-411. Perryman Consultants’ number
of households had a predicted growth rate of 0.70 for the 1995-2004 forecast. The variable’s growth rate
for the 1999-2008 forecast was 1.55. The model was accepted as adequate for developing the energy
bands for the EIA-411 forecast.
Economic Scenario Bands
The high and low growth scenario data series were inserted in the model in place of the base forecast
and the bands were then calculated. The percent difference between the modeled scenario forecasts
and the modeled base forecast was applied to the EIA-411 Report forecast and the resulting bands for
demand and energy are shown.
SPP Northern Subregion
Demand Forecast
Energy Forecast
1988-2008
1988-2008
17
80
16
70
Thousands of GWh
Thousands of MW
15
14
13
12
11
60
50
10
9
40
1988
1993
1998
EIA-411
2003
2008
1988
Economic
1993
1998
2003
EIA-411
Economic
Annual Compound Forecast Growth Rates (%/Year 1999-2008)
SPP
Low
Base
High
Peak Demand
1.55
2.15
2.68
Annual Energy
1.64
2.24
2.77
10
January 2000
2008
Demand & Energy Forecast
1999-2008
The BWG believes these results adequately reflect the extreme economic possibilities for the Northern
Subregion over the next 10 years. Also, the BWG believes the forecast as reported by member systems
in the EIA-411 Report represents the most probable requirement for electricity. However, these results
are based on the occurrence of normal weather for the forecast period. The effects of extreme weather
within the Northern Subregion was also investigated and quantified.
Weather Bands
Data series were developed for heating and cooling degree-days that represent a 95% confidence
interval around the 30-year average for these variables. These variables were then placed in the base
energy forecast model in place of the normal values to generate energy bands, which represented the
effects due to extreme weather. The extreme weather demand bands were derived from the extreme
weather energy bands using one standard deviation from the mean load factors, as previously described.
These same procedures are applicable to weather extremes for the high and low growth scenario bands.
SPP Northern Subregion
Extreme Weather Demand Bands
Extreme Weather Energy Bands
1988-2008
1988-2008
80
17
16
70
Thousands of GWh
Thousands of MW
15
14
13
12
11
60
50
10
9
1988
1993
1998
EIA-411
2003
40
2008
1988
Weather
1993
1998
EIA-411
2003
Weather
Annual Extreme Weather Band (% from base 1999-2008)
SPP
Low
High
Peak Demand
94.1
108.3
Annual Energy
95.3
104.8
11
January 2000
2008
Demand & Energy Forecast
1999-2008
SOUTHERN SUBREGION APPRAISAL
EIA-411 Report Observations
The Southern Subregion of SPP consists of member systems operating in the state of Arkansas,
Louisiana, western Mississippi, New Mexico, Oklahoma, northeastern Texas, and the Texas Panhandle.
An analysis of data reported by member systems in the EIA-411 Reports over the last 10 years indicate
the results shown in the following table for actual and forecast peak demand and energy for the
subregion.
Annual Compound Growth Rates (%/year)
1988 - 1998 Actual
1999 - 2008 Forecast
Peak Demand
3.3
1.86
Annual Energy
3.7
2.00
Forecast Model
A routine called PROC REG within the SAS software was used to model the subregion energy growth.
The variables selected for the model were cooling-degree days, heating-degree days, real price of
electricity, and industrial production index. The model passed all standard statistical tests. The functional
format and parameter estimates of the model and calculated results can be found in the Data Report. A
graphical comparison of the model results to EIA-411 Report historical and forecast energy is shown on
the graph below.
SPP Southern Subregion
Actual & Forecast Energy
170
Thousands of Gwh
160
150
140
130
120
110
100
90
80
1988
1993
1998
EIA-411
SPP
12
2003
2008
MODEL
January 2000
Demand & Energy Forecast
1999-2008
An objective of this effort was to develop a model that replicated the EIA-411 Report forecast, as close as
possible, and then to use this model to develop the scenario bands around the base EIA-411 Report
forecast. The results shown for 1988 through 1998 indicate how well the model would have predicted
history given all known weather and economic information. An acceptable statistical model was
developed for the Southern Subregion, using data supplied by Perryman Consultants. The model was
accepted as adequate for developing the energy bands for the EIA-411 forecast.
Economic Scenario Bands
The high and low growth scenario data series were inserted in the model in place of the base forecast
and the bands were then calculated. The percent difference between the modeled scenario forecasts
and the modeled base forecast was applied to the EIA-411 Report forecast and the resulting bands for
demand and energy are shown below. Growth rates for the forecast period are also shown.
SPP Southern Subregion
Demand Forecast
Energy Forecast
1988-2008
1988-2008
35
170
160
150
Thousands of GWh
Thousands of MW
30
25
20
140
130
120
110
100
90
15
1988
1993
1998
EIA-411
2003
80
1988
2008
Economic
1993
1998
EIA-411
2003
Economic
Annual Compound Forecast Growth Rates (%/year 1999-2008)
SPP
Low
Base
High
Peak Demand
1.23
1.86
2.61
Annual Energy
1.36
2.00
2.75
13
January 2000
2008
Demand & Energy Forecast
1999-2008
The BWG believes these results adequately reflect the extreme economic possibilities for the Southern
Subregion over the next 10 years. Also, the BWG believes the forecast as reported by member systems
in the EIA-411 Report represent the most probable requirement for electricity. These results are based
on the occurrence of normal weather for the forecast period. The effects of extreme weather within the
Southern Subregion were also investigated and quantified.
Weather Bands
The BWG developed data series for heating and cooling degree-days that represent a 95% confidence
interval around the 30-year average for these variables. These variables were then placed in the base
energy forecast model in place of the normal values to generate energy bands that represent the effect
due to extreme weather. The extreme weather demand bands were derived from the extreme weather
energy bands using one standard deviation from the mean load factor, as previously described. The
table and graphs below show the percent variation from the base forecast.
SPP Southern Subregion
Extreme Weather Demand Bands
Extreme Weather Energy Bands
1988-2008
1988-2008
35
170
160
150
Thousands of Gwh
Thousands of MW
30
25
20
140
130
120
110
100
90
15
1988
1993
1998
EIA-411
2003
80
1988
2008
Weather
1993
1998
EIA-411
2003
Weather
Average Extreme Weather Band (% from base 1999-2008)
SPP
Low
High
Peak Demand
94.8
108.0
Annual Demand
95.3
104.8
14
January 2000
2008
APPENDIX
Subregional Results Calculation Summary ( by column number)
Energy
Demand
(8) = (3)/(2)*(1)
(17) = (8)*100000/(16)/8760
(9) = (4)/(2)*(1)
(18) = (9)*100000/(16)/8760
(10) = (5)/(2)*(1)
(19) = (14)*100000/(16)/8760
(11) = (10)/(1)*100
(20) = (10)*100000/note/8760
(12) = (6)/(2)*(1)
(21) = (20)/(15)*100
(13) = (12)/(1)*100
(22) = (13)*100000/note/8760
(14) = (7)/(2)*(1)
(23) = (22)/(15)*100
(16) = (1)/(15)/8760*100
Note:
Low Weather
High Weather
Load Factor (%)
Load Factor (%)
10 Yr. Mean + 1 Std, Dev.
10 Yr. Mean - 1 Std. Dev.
Northern
52.93
50.61
Southern
57.04
55.05
SPP
55.60
53.63
SPP
15
January 2000
SPP ENERGY AND DEMAND BANDWIDTH FORECAST 1999-2008
SUMMATION OF SUBREGION ENERGY DATA (GWH)
EIA-411
ENERGY
YEAR
1988
132799
1989
134175
1990
138924
1991
141247
1992
142602
1993
154669
1994
161605
1995
163396
1996
167948
1997
174709
1998
183103
1999
184044
2000
187333
2001
189777
2002
193822
2003
198238
2004
202475
2005
208142
2006
212002
2007
217041
2008
222206
BASE
133399
133972
141966
146299
139518
150482
157330
164303
168732
172098
187079
185307
188889
193883
199223
203631
209284
213751
219498
224148
229878
LOW
ECON
184044
186969
187660
190443
193511
196445
200671
203252
206875
210686
HIGH
ECON
184044
187664
192134
197636
203525
209192
216436
221664
228262
234859
LOW
LOW
HIGH
HIGH
NORMAL
WEATHER WEATHER (%) WEATHER WEATHER (%) WEATHER
132105
135998
137107
139120
148398
155631
162815
164497
169216
176791
177478
174471
94.80
193771
105.28
177774
94.90
197045
105.18
180340
95.03
199365
105.05
184440
95.16
203354
104.92
188844
95.26
207782
104.81
193135
95.39
211965
104.69
198731
95.48
217704
104.59
202661
95.59
221492
104.48
207665
95.68
226567
104.39
212838
95.78
231724
104.28
SUMMATION OF SUBREGIONAL DEMAND DATA (MW)
EIA-411
YEAR
DEMAND
LF (%)
1988
28395
53.39
1989
28460
53.82
1990
30006
52.85
1991
29825
54.06
1992
31268
52.06
1993
32906
53.66
1994
33344
55.33
1995
34657
53.82
1996
35003
54.77
1997
36198
55.10
1998
37724
55.41
1999
38180
55.03
2000
38795
55.12
2001
39305
55.12
2002
40092
55.19
2003
41045
55.13
2004
41834
55.25
2005
42964
55.30
2006
43684
55.40
2007
44656
55.48
2008
45643
55.57
LOW
ECON
38180
38717
38867
39393
40067
40589
41424
41883
42568
43281
HIGH
ECON
38180
38865
39790
40876
42133
43213
44664
45662
46949
48226
NORMAL
LOW
LOW
HIGH
HIGH
WEATHER WEATHER WEATHER (%) WEATHER WEATHER (%)
28232
28847
29618
29366
32552
33112
33594
34891
35268
36631
36581
35762
93.67
41291
108.15
36448
93.95
41996
108.25
36983
94.09
42500
108.13
37826
94.35
43352
108.13
38726
94.35
44290
107.91
39606
94.67
45181
108.00
40749
94.85
46396
107.99
41561
95.14
47210
108.07
42587
95.37
48290
108.14
43649
95.63
49389
108.21
SPP ENERGY AND DEMAND BANDWIDTH FORECAST 1999-2008
NORTHERN SUBREGION ENERGY DATA (GWH)
YEAR
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
EIA-411
ENERGY
(1)
43241
42034
44550
45213
45162
49790
51456
51488
52401
53758
57075
57657
59152
60482
61853
63029
64354
65804
67360
68949
70590
BASE
(2)
43053
42607
44629
46661
44549
48328
50183
51660
52586
53831
58082
59993
62811
65553
68289
71033
73784
76539
79299
82062
84827
LOW
ECON
(3)
59993
62541
64841
67122
69398
71667
73930
76184
78428
80661
MODEL RESULTS
HIGH
LOW
HIGH
NORMAL
ECON WEATHER WEATHER WEATHER
(4)
(5)
(6)
(7)
42157
43200
44313
45461
46991
48709
50603
52081
53057
54547
57175
59993
56613
63428
63045
59431
66246
66172
62173
68988
69306
64909
71724
72461
67653
74468
75636
70403
77219
78829
73159
79974
82040
75919
82734
85268
78682
85497
88511
81447
88263
LOW
ECON
(8)
57657
58897
59825
60796
61578
62508
63561
64714
65895
67123
HIGH
ECON
(9)
57657
59372
61053
62774
64296
65969
67773
69688
71642
73656
MODEL RESULTS ADJUSTED TO EIA-411
LOW
LOW
HIGH
HIGH
NORMAL
WEATHER WEATHER (%) WEATHER WEATHER (%) WEATHER
(10)
(11)
(12)
(13)
(14)
42342
42619
44235
44051
47638
50183
51887
51907
52870
54473
56184
54408
94.37
60958
105.73
55969
94.62
62387
105.47
57363
94.84
63651
105.24
58791
95.05
64964
105.03
60030
95.24
66077
104.84
61406
95.42
67350
104.66
62898
95.58
68757
104.49
64489
95.74
70278
104.33
66109
95.88
71835
104.19
67777
96.02
73449
104.05
NORTHERN SUBREGIONAL DEMAND DATA (MW)
YEAR
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
EIA-411
DEMAND
LF (%)
(15)
(16)
9872
50.00
9427
50.90
10169
50.01
10173
50.74
10555
48.84
11056
51.41
10794
54.42
11354
51.77
11514
51.95
11820
51.92
12487
52.18
12634
52.10
12920
52.26
13194
52.33
13479
52.38
13769
52.26
14072
52.21
14378
52.25
14699
52.31
15025
52.39
15350
52.50
LOW
ECON
(17)
12634
12864
13051
13249
13452
13668
13888
14122
14360
14596
HIGH
ECON
(18)
12634
12968
13319
13680
14046
14425
14808
15207
15612
16017
RESULTS
NORMAL
LOW
LOW
HIGH
HIGH
WEATHER WEATHER WEATHER (%) WEATHER WEATHER (%)
(19)
(20)
(21)
(22)
(23)
9667
9558
10097
9912
11134
11143
10884
11446
11617
11977
12292
11734
92.88
13750
108.83
12071
93.43
14072
108.92
12372
93.77
14357
108.82
12680
94.07
14653
108.71
12947
94.03
14904
108.25
13244
94.11
15191
107.95
13565
94.35
15509
107.86
13908
94.62
15852
107.84
14258
94.89
16203
107.84
14618
95.23
16567
107.93
SPP ENERGY AND DEMAND BANDWIDTH FORECAST 1999-2008
SOUTHERN SUBREGION ENERGY DATA (GWH)
YEAR
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
EIA-411
ENERGY
(1)
89558
92141
94374
96034
97440
104879
110149
111908
115547
120951
126028
126387
128181
129295
131969
135209
138121
142338
144642
148092
151616
BASE
(2)
90347
91364
97337
99639
94969
102154
107147
112643
116146
118266
128998
125314
126078
128330
130934
132598
135500
137212
140199
142085
145051
LOW
ECON
(3)
125314
125971
126881
128630
129385
131395
132172
134283
135262
137347
MODEL RESULTS
HIGH
LOW
HIGH
NORMAL
ECON WEATHER WEATHER WEATHER
(4)
(5)
(6)
(7)
90554
92592
95788
98638
98205
102708
107905
113330
116949
119603
124152
125314
119043
131685
126187
119807
132449
130103
122059
134701
133804
124663
137304
136540
126327
138968
140505
129229
141871
143309
130941
143583
147308
133928
146570
150267
135815
148456
154223
138780
151422
LOW
ECON
(8)
126387
128072
127835
129647
131933
133937
137110
138538
140980
143564
HIGH
ECON
(9)
126387
128292
131081
134862
139229
143222
148663
151976
156619
161203
MODEL RESULTS ADJUSTED TO EIA-411
LOW
LOW
HIGH
HIGH
NORMAL
WEATHER WEATHER (%) WEATHER WEATHER (%) WEATHER
(10)
(11)
(12)
(13)
(14)
89764
93379
92872
95069
100760
105448
110928
112590
116346
122318
121294
120062
95.00
132812
105.08
121805
95.03
134658
105.05
122977
95.11
135713
104.96
125648
95.21
138390
104.87
128815
95.27
141705
104.80
131729
95.37
144615
104.70
135833
95.43
148947
104.64
138172
95.53
151214
104.54
141556
95.59
154732
104.48
145061
95.68
158275
104.39
SOUTHERN SUBREGION DEMAND DATA (MW)
YEAR
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
EIA-411
DEMAND
LF (%)
(15)
(16)
18523
55.19
19033
55.26
19837
54.31
19652
55.78
20713
53.70
21850
54.79
22550
55.76
23303
54.82
23489
56.16
24378
56.64
25237
57.01
25546
56.48
25875
56.55
26111
56.53
26613
56.61
27276
56.59
27762
56.79
28586
56.84
28985
56.97
29631
57.05
30293
57.13
LOW
ECON
(17)
25546
25853
25816
26145
26615
26921
27536
27762
28208
28685
HIGH
ECON
(18)
25546
25897
26472
27196
28087
28787
29856
30455
31337
32209
RESULTS
NORMAL
LOW
LOW
HIGH
HIGH
WEATHER WEATHER WEATHER (%) WEATHER WEATHER (%)
(19)
(20)
(21)
(22)
(23)
18566
19289
19521
19455
21419
21969
22710
23445
23651
24653
24289
24027
94.05
27541
107.81
24376
94.21
27924
107.92
24610
94.25
28143
107.78
25145
94.48
28698
107.83
25779
94.51
29385
107.73
26362
94.96
29989
108.02
27183
95.09
30887
108.05
27651
95.40
31357
108.18
28328
95.60
32087
108.29
29030
95.83
32821
108.34