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ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
Methods for Forecasting Supply and Demand
Presented by:Douglas J. GothamPurdue University
Presented to:Institute of Public Utilities
56th Annual Regulatory Studies Program
August 12, 2014
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
2
Using the Past to Predict the Future
• What is the next number in the following sequences?0, 2, 4, 6, 8, 10, ….
0, 1, 4, 9, 16, 25, 36, ....
0, 1, 2, 3, 5, 7, 11, 13, ....
1, 3, 7, 15, 31, ....
0, 1, 1, 2, 3, 5, 8, 13, ....
8, 6, 7, 5, 3, 0, ….
8, 5, 4, 9, 1, 7, ….
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
3
A Simple Example
900
920
940
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1020
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1060
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1100
1 2 3 4 5 6
1000
1010
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?
?
?
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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A Little More Difficult1000
1100
1210
1331
1464
1610
?
?
?
900
1000
1100
1200
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1 2 3 4 5 6
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
Much More Difficult18831
18794
18193
19944
20855
20858
19275
19054
20315
21002
?
?
17,000
18,000
19,000
20,000
21,000
22,000
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
6
Much More Difficult• The numbers on the previous slide were
the summer peak demands for Indiana from 2002 to 2011
• They are affected by a number of factors– Weather– Economic activity– Price– Interruptible customers called upon– Price of competing fuels
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Question• How do we find
a pattern in these peak demand numbers to predict the future?
1984
1986
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2008
2010
0
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25000
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
8
Methods of Forecasting
• Palm reading• Tea leaves• Tarot cards• Ouija board• Crystal ball• Polling
• Astrology• Dart board• Sheep entrails• Hire a consultant• Wishful thinking
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
9
Alternative Methods of Forecasting
• Top-down– trend analysis (aka time series)– econometric
• Bottom-up– survey-based– end-use
• Hybrid– statistically-adjusted end-use
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
10
Time Series Forecasting• Linear Trend
– Fit the best straight line to the historical data and assume that the future will follow that line
• works perfectly in the 1st example
– Many methods exist for finding the best fitting line; the most common is the least squares method
Y X
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
11
Time Series Forecasting• Polynomial Trend
– Fit the polynomial curve to the historical data and assume that the future will follow that line
– Can be done to any order of polynomial (square, cube, etc.) but higher orders are usually needlessly complex
21 2 ...Y X X
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
12
Time Series Forecasting• Logarithmic Trend
– Fit an exponential curve to the historical data and assume that the future will follow that line
• works perfectly for the 2nd example
XY
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
13
Example
• Use linear time series analyses to project Indiana peak demand from 2010 to 2029 using historical observations over 3 time periods– 1980-2009– 1990-2009– 2000-2009
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
14
Trend 1 - Starting in 1980
1980
1982
1984
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Actual
Trend
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Trend 2 - Starting in 1990
1990
1992
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2016
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Trend
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
16
Trend 3 - Starting in 2000
2000
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2020
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ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Comparison
1980
1982
1984
1986
1988
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1992
1994
1996
1998
2000
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2010
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2014
2016
2018
2020
2022
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10000
15000
20000
25000
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Actual
Trend 1
Trend 2
Trend 3
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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ResultsYear Trend 1 Trend 2 Trend 3
2010 20981 20963 20793
2015 22766 22716 22376
2020 24551 24470 23959
2025 26337 26224 25542
2030 28122 27978 27124
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Advantages• Relatively easy• The statistical functions in most
commercial spreadsheet software packages will calculate many of these for you
• Requires little data
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Disadvantages
• Does not account for changing circumstances
• Choice of historical observations can impact results
• May not work well when there is a lot of variability in the historical data– If the time series curve does not perfectly
fit the historical data, there is model error
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Acceptability
• Trend analysis was a popular forecasting methodology until the 1970s
• The inability to handle changing conditions led to considerably inaccurate forecasts
• They have been largely discredited– MISO’s forecasting whitepaper lists it as an
“unacceptable” method
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Econometric Forecasting• Econometric models attempt to quantify the
relationship between the parameter of interest (output variable) and a number of factors that affect the output variable.
• Example– Output variable– Explanatory variable
• Economic activity• Weather (HDD/CDD)• Electricity price• Natural gas price• Fuel oil price
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Estimating Relationships• Each explanatory variable affects the output
variable in a different way. The relationships (or sensitivities) can be calculated via any of the methods used in time series forecasting– Can be linear, polynomial, logarithmic, moving
averages, …
• Relationships are determined simultaneously to find overall best fit
1 1 2 2 3 3 ...Y X X X
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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A Simple Example• Suppose we have 4
sets of observations with 2 possible explanatory variables
Output Y Variable X1 Variable X2
110 100 100
113 120 110
114 130 90
121 150 120
80 90 100 110 120 130 140 150 160100
110
120
130
80 90 100 110 120 130 140 150 160100
110
120
130
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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A Simple Example
• Including both variables provides a perfect fit– Perfect fits are not usually achievable in
complex systems
Y = 0.2X1 – 0.1X2 + 100
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Advantages• Improved accuracy over trend analysis• Ability to analyze different scenarios• Greater understanding of the factors
affecting forecast uncertainty
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Disadvantages
• More time and resource intensive than trend analysis
• Difficult to account for factors that will change the future relationship between the drivers and the output variable– utility DSM programs– government codes and standards
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Acceptability
• Econometric methods became popular as trend analysis died out in the 70s and 80s
• They continue to be used today• MISO’s forecasting whitepaper lists it as
an “acceptable” method
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Survey-Based Forecasting
• Also referred to as “informed opinion” forecasts
• Use information from a select group of customers regarding their future production and expansion plans as the basis for a forecast
• Commonly done with large users
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Advantages
• Simplicity• The ability to account for expected
fundamental changes in customer demand for large users, especially in the near-term– new major user or customer closing a
facility
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Disadvantages
• Tend to be inaccurate beyond first few years– most customers do not know what their
production levels will be five or ten years in the future
– few customers expect to close shop– new customers after first couple years are
unknown• Lack of transparency
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Acceptability
• Survey-based forecasts may be acceptable for short-term applications or if used in conjunction with another method in the longer term
• MISO’s forecasting whitepaper lists it as an “unacceptable” method
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
33
End Use Forecasting• End use forecasting looks at individual
devices, aka end uses (e.g., refrigerators)• How many refrigerators are out there?• How much electricity does a refrigerator use?• How will the number of refrigerators change
in the future?• How will the amount of use per refrigerator
change in the future?• Repeat for other end uses
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
34Source: Van Buskirk, Robert. “History and Scope of USA Mandatory Appliance Efficiency Standards.” (CLASP/LBNL).
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Advantages• Account for changes in efficiency levels (new
refrigerators tend to be more efficient than older ones) both for new uses and for replacement of old equipment
• Allow for impact of competing fuels (natural gas vs. electricity for heating) or for competing technologies (electric resistance heating vs. heat pump)
• Incorporate and evaluate the impact of demand-side management/conservation programs
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Disadvantages
• Tremendously data intensive• Primarily limited to forecasting energy
usage, unlike other forecasting methods– Most long-term planning electricity
forecasting models forecast energy and then derive peak demand from the energy forecast
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Acceptability
• End-use modeling was first developed in the 1970s but started to gain popularity with the increase in DSM in the 1990s
• MISO’s forecasting whitepaper lists it as an “acceptable” method
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Hybrid Forecasting
• Hybrid models employ facets of both top-down and bottom-up models
• Most common is called the statistically-adjusted end-use (SAE) model
• In reality, most end-use models are hybrid to some degree in that they rely on top-down approaches to determine the growth in new devices
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
39
SAE Models
• SAE models incorporate features of both econometric and end-use models
• Adjust the end-use estimated loads using a statistical regression to match observed loads
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
40
Disadvantages
• Increased model complexity• More time and resource intensive
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Advantages
• In general, hybrid approaches attempt to combine the relative advantages and disadvantages of both model types
• Can better capture externalities that affect customer decisions when compared to end-use models– green options
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
42
Acceptability
• Hybrid models have been gaining in popularity in recent years
• MISO’s forecasting whitepaper lists it as an “acceptable” method
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
43
Forecasting Example
• SUFG has electrical energy models for each of 8 utilities in Indiana
• Utility energy forecasts are built up from sectoral forecasting models– residential (end-use & econometric)– commercial (end-use & econometric)– industrial (econometric)
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Another Example
• SUFG is developing independent forecasting models for MISO– econometric– individual state level (15 states)
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
45
Another Example• The Energy Information Administration’s
National Energy Modeling System (NEMS) projects energy and fuel prices for 9 census regions
• Energy demand (end-use)– residential– commercial– industrial– transportation
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
46
Sources of Uncertainty• Exogenous assumptions
– forecast is driven by a number of assumptions (e.g., economic activity) about the future
• Stochastic model error– it is usually impossible to perfectly estimate the
relationship between all possible factors and the output
• Non-stochastic model error– bad input data (measurement/estimation error)
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
47
Energy → Peak Demand• Constant load factor / load shape
– Peak demand and energy grow at same rate
• Constant load factor / load shape for each sector– Calculate sectoral contribution to peak demand
and sum– If low load factor (residential) grows fastest, peak
demand grows faster than energy– If high load factor (industrial) grows fastest, peak
demand grows slower than energy
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
48
Energy → Peak Demand
• Day types– Break overall load shapes into typical day
types• low, medium, high• weekday, weekend, peak day
– Adjust day type for load management and conservation programs
– Can be done on a total system level or a sectoral level
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
Load Diversity• Each utility does not see its peak demand at
the same time as the others• 2011 peak demands occurred at:
– Duke Energy – 7/20, 2 PM– Hoosier Energy – 7/21, 6 PM– Indiana Michigan – 7/21, 2 PM– Indiana Municipal Power Agency – 7/21, 2 PM– Indianapolis Power & Light – 7/20, 3 PM– NIPSCO – 7/21, 4 PM– SIGECO – 7/21, 4 PM– Wabash Valley – 7/20, 8 PM
• Statewide peak – 7/21, 4 PM
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
50
Load Diversity Example
1AM
2AM
3AM
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12AM
0
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A
B
C
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Example (continued)
1AM
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5AM
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60005700
Total
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
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Load Diversity• This analysis is normally performed for
all hours of the year• Thus, the statewide (or regional) peak
demand is less than the sum of the individual peaks
• Actual statewide/regional peak demand can be calculated by summing up the load levels of all utilities for each hour of the year
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
53
Diversity Factor
• The diversity factor is an indication of the level of load diversity
• Historically, Indiana’s diversity factor has been about 96 – 97 percent– that is, statewide peak demand is usually
about 96 percent of the sum of the individual utility peak demands
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
54
Resource Expansion Models• Determine amount, type, and location of
demand/supply resources to be developed to reliably meet future electricity demand
• Detailed information on existing generation resources but limited detail on existing transmission resources
• EGEAS, NEEM, NEMS, NESSIE, ReEDS, Strategist
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
55
Production Costing Models
• Simulates the network operation over a year to determine costs, emissions, impacts of congestion
• Detailed information on generation and transmission infrastructure (both existing and future)
• GE MAPS, GRIDVIEW, PROMOD, UPLAN
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
56
Power Flow Models• Determine adequacy of transmission system
to meet demand without violating physical constraints (undervoltage, overcurrent, etc.)
• Examine contingencies (can system operate reliably if a line/transformer/generator goes down?)
• Load/generator output fixed for a given hour• Models laws of physics that drive actual path
of power flow (not an optimization)• Power World, PSS/e, PSLF
ENERGY CENTERState Utility Forecasting Group (SUFG)
ENERGY CENTERState Utility Forecasting Group (SUFG)
57
Further Information
• State Utility Forecasting Group– http://www.purdue.edu/dp/energy/SUFG/
• Energy Information Administration– http://www.eia.doe.gov/index.html