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#AnalyticsXC o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.
Holiday Demand Forecasting
Yue LiSenior Research Statistician DeveloperSAS
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C o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.
Background
Holiday demand modeling techniques
Weekend day
Holiday dummy variable
Two-stage methods
Results
Discussions
Outline
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C o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.
Background: Motivation Importance of accurate electric demand forecasting
system operations and planning
energy trading
demand side management
Challenges of holiday electric demand forecasting
Limited historical data
Changing demand profile across holidays and/or across years for the same holiday
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Background: Data Hourly load and temperature data of ISO (Independent
System Operator) New England
1
ˆ1| | 100%
N
t t
t t
y yMAPE
N y
Source: http://www.ferc.gov/market-oversight/mkt-electric/new-england.asp
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Background: Data 10 US Federal Holidays
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Background: Data Hourly demand on each holiday from 2004 to 2008
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Background: Benchmark Model
0 1 2 3 4 5( ) ( )
t t t t t t t t i tiLoad Trend M W H W H f T f T
242 3 2 3
1 2 3 4 5 6 1
1 where ( ) and
24t i t i t t i t t i t t i t t i t t i t t t hh
f T T M T M T M T H T H T H T T
Load by month (M) Weekly load profile (W) Load by hour (H)
PROC GLM in SAS/STAT®
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Modeling Holiday as WeekendsSunday Saturday
Alternate the weekday code to weekend (Saturday or Sunday)
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Modeling Holidays using Dummy Variables
Thanksgiving Day Weekly load profile
Not similar to any weekday or weekend day
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Modeling Holidays using Dummy Variables
0 1 2 3 4 5( ) ( ) +Holiday
t t t t t t t t i tiLoad Trend M W H W H f T f T
0 1 2 3 4 5( ) ( ) +Holiday*
t t t t t t t t i t tiLoad Trend M W H W H f T f T H
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Two-stage Method - Naive
Final forecasts = Load forecasts + Residual forecasts
• Stage 1: load forecasted from benchmarkmodel with or without treating holiday asweekend day
• Stage 2: residual forecasted from the mean ofthe model fit residuals
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Two-stage Method – Rule Based
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Two-stage Method – Rule Based
Final forecasts = Load forecasts + Residual forecasts
• Stage 1: load forecasted from benchmarkmodel with or without weekday effect
• Stage 2: For holiday occurred on each day-of-week: compare the hourly residual profilewith non-holiday residual profile in each day-of-week and the holiday model fit residuals tocome up with the rule. Use the rule togenerate residual forecasts
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Two-stage Method – Rule BasedExample Rules:
If New Year’s Day is on Monday then treat it as Sunday
If New Year’s Day is on Friday then treat it as Sunday
If July 4th is on Monday then treat it as Sunday
If July 4th is on Friday then treat it as Saturday
If Veteran’s Day is on Friday then treat it as Friday
If Christmas Day is on Monday then treat it as Sunday
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Two-stage Method – Rule Based Data driven rule based model
For same-date holidays
Different residual patterns for different Day-of-Week
Can incorporate experts’ judgements
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Results
Out-of-sample MAPEs of Holiday Demand Forecasts from Different Holiday Demand Modeling Techniques
July 4th Thanksgiving
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Results
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Discussion
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Discussion
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DiscussionGradient Boosting Model (GBM)
A stage wise method to fit residuals
Popularity
SAS® Enterprise Miner
1%
3%
5%
7%
9%
11%
13%
15%
1 2 3 4 5 6 7 8 9 10
Holi
day M
AP
E
Holiday
Benchmark GBM Residual
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Conclusion Different holiday demand forecasting requires different
technique
Overall, the two-stage methods perform well
The availability of the historical data also impacts the selection of holiday demand modeling method
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