Post on 10-May-2015
description
The Case of the Curious Correlations
Is this what you would expect?
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20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
The miracle of air conditioning
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2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)# Higher temperatures lead
to higher HVAC load
Lower temperatures lead to lower HVAC load
But what about this ?
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20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
Why would electric demand start to rise again as the temperature continues to fall ?
And why the weaker correlation ?
Electric heating ? Probably not too much – this is Texas.
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20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)# R²#=#0.32024#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#
Q1?2012)#
R²#=#0.93592#
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5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#
Q2?2012)#
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5,000#
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20,000#
25,000#
30,000#
20# 40# 60# 80# 100#
Q3?2012#
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5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#
Q4?2012#
Digging into the data
The tight, positively correlated data is concentrated in Q2 and Q3
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30,000#
20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)# R²#=#0.32024#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#
Q1?2012)#
R²#=#0.93592#
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5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#
Q2?2012)#
R²#=#0.90115#
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5,000#
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25,000#
30,000#
20# 40# 60# 80# 100#
Q3?2012#
R²#=#0.45417#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#
Q4?2012#
Digging into the data
The weaker, negatively correlated data is concentrated in Q1 and Q4
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2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
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20# 40# 60# 80# 100#
January#
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20# 40# 60# 80# 100#
February#
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20# 40# 60# 80# 100#
March#
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20# 40# 60# 80# 100#
April#
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20# 40# 60# 80# 100#
May#R²#=#0.90612#
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20# 40# 60# 80# 100#
June#R²#=#0.76431#
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10,000#
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20# 40# 60# 80# 100#
July#R²#=#0.93766#
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20# 40# 60# 80# 100#
August#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
October#
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20# 40# 60# 80# 100#
November#
R²#=#0.58202#
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10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
December#
Digging deeper into the data
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10,000#
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20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
R²#=#0.20403#
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10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
January#
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10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
February#
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20# 40# 60# 80# 100#
March#
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20,000#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
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20,000#
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20# 40# 60# 80# 100#
June#R²#=#0.76431#
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10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
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10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
August#
R²#=#0.96721#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
September#
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10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
October#
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10,000#
20,000#
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20# 40# 60# 80# 100#
November#
R²#=#0.58202#
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10,000#
20,000#
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20# 40# 60# 80# 100#
December#
Digging deeper into the data
April looks odd, compared to March and May. Investigate further by looking at 2011 and 2013 data.
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2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
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20# 40# 60# 80# 100#
January#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
June#R²#=#0.76431#
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10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
July#R²#=#0.93766#
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10,000#
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30,000#
20# 40# 60# 80# 100#
August#
R²#=#0.96721#
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10,000#
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20# 40# 60# 80# 100#
September#
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10,000#
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20# 40# 60# 80# 100#
October#
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20# 40# 60# 80# 100#
November#
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December#
Digging deeper into the data
Temperature is dominant driver of electric load in some months . . .
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2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
R²#=#0.20403#
0#
10,000#
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30,000#
20# 40# 60# 80# 100#
January#
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20# 40# 60# 80# 100#
February#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
November#
R²#=#0.58202#
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20# 40# 60# 80# 100#
December#
Digging deeper into the data
But understanding what drives loads in other months requires more sophisticated models . . .
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
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20# 40# 60# 80# 100#
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10,000#
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20# 40# 60# 80# 100#
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10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
August#
R²#=#0.96721#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
September#
R²#=#0.72922#
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10,000#
20,000#
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20# 40# 60# 80# 100#
October#
R²#=#0.25539#
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10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
November#
R²#=#0.58202#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
December#
Digging deeper into the data
July correlation significantly weaker than other summer months. Could it be due to Independence day falling on a Wednesday in 2012 ?
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