Holiday Demand Forecasting
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1 Holiday Demand Forecasting Yue Li Senior Research Statistician Developer SAS #AnalyticsX
2 Outline Background Holiday demand modeling techniques Weekend day Holiday dummy variable Two-stage methods Results Discussions
3 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
4 Background: Data Hourly load and temperature data of ISO (Independent System Operator) New England MAPE 1 N N t 1 y ˆ t yt 100% y t Source:
5 Background: Data 10 US Federal Holidays
6 Background: Data Hourly demand on each holiday from 2004 to 2008
7 Background: Benchmark Model Load Trend M W H W H f ( T ) f ( T ) t 0 1 t 2 t 3 t 4 t 5 t t t i t 1 where f ( T ) T M T M T M T H T H T H and T T t i 1 t i t 2 t i t 3 t i t 4 t i t 5 t i t 6 t i t t h 1 t h i PROC GLM in SAS/STAT Load by month (M) Weekly load profile (W) Load by hour (H)
8 Modeling Holiday as Weekends Sunday Saturday Alternate the weekday code to weekend (Saturday or Sunday)
9 Modeling Holidays using Dummy Variables Thanksgiving Day Weekly load profile Not similar to any weekday or weekend day
10 Modeling Holidays using Dummy Variables Load Trend M W H W H f ( T ) f ( T ) +Holiday t 0 1 t 2 t 3 t 4 t 5 t t t i t i Load Trend M W H W H f ( T ) f ( T ) +Holiday* H t t t t t t t i t i t t
11 Two-stage Method - Naive Final forecasts = Load forecasts + Residual forecasts Stage 1: load forecasted from benchmark model with or without treating holiday as weekend day Stage 2: residual forecasted from the mean of the model fit residuals
12 Two-stage Method Rule Based
13 Two-stage Method Rule Based Final forecasts = Load forecasts + Residual forecasts Stage 1: load forecasted from benchmark model with or without weekdayeffect Stage 2: For holiday occurred on each dayof-week: compare the hourly residual profile with non-holiday residual profile in each dayof-week and the holiday model fit residuals to come up with the rule. Use the rule to generate residual forecasts
14 Two-stage Method Rule Based Example 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 4 th is on Monday then treat it as Sunday If July 4 th 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
15 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
16 Results July 4 th Thanksgiving Out-of-sample MAPEs of Holiday Demand Forecasts from Different Holiday Demand Modeling Techniques
17 Results
18 Discussion
19 Discussion
20 Holiday MAPE #analyticsx Discussion Gradient Boosting Model (GBM) A stage wise method to fit residuals Popularity SAS Enterprise Miner 15% 13% 11% 9% 7% 5% 3% 1% Holiday Benchmark GBM Residual
21 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
22 #AnalyticsX
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