Embed Size (px)
DESCRIPTION
When it comes to energy business, and especially electricity, things can get a little odd sometimes. Higher temperatures mean people are going to need more power. Lower temperatures, less power. Right. Usually. But not always. Sometimes you might need an expert to make sense of your data.
Citation preview
The Case of the Curious Correlations
Is this what you would expect?
R²#=#0.88355#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
The miracle of air conditioning
R²#=#0.88355#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
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 ?
R²#=#0.88355#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
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.
R²#=#0.88355#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
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#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#
Q2?2012)#
R²#=#0.90115#
0#
5,000#
10,000#
15,000#
20,000#
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 tight, positively correlated data is concentrated in Q2 and Q3
R²#=#0.88355#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
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#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#
Q2?2012)#
R²#=#0.90115#
0#
5,000#
10,000#
15,000#
20,000#
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
R²#=#0.88355#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
R²#=#0.20403#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
January#
R²#=#0.55973#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
February#
R²#=#0.39612#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
March#
R²#=#0.83112#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
April#
R²#=#0.55973#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
May#R²#=#0.90612#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
June#R²#=#0.76431#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
July#R²#=#0.93766#
0#
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#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
October#
R²#=#0.25539#
0#
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
R²#=#0.88355#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
R²#=#0.20403#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
January#
R²#=#0.55973#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
February#
R²#=#0.39612#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
March#
R²#=#0.83112#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
April#
R²#=#0.55973#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
May#R²#=#0.90612#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
June#R²#=#0.76431#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
July#R²#=#0.93766#
0#
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#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
October#
R²#=#0.25539#
0#
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
April looks odd, compared to March and May. Investigate further by looking at 2011 and 2013 data.
R²#=#0.88355#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
R²#=#0.20403#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
January#
R²#=#0.55973#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
February#
R²#=#0.39612#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
March#
R²#=#0.83112#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
April#
R²#=#0.55973#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
May#R²#=#0.90612#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
June#R²#=#0.76431#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
July#R²#=#0.93766#
0#
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#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
October#
R²#=#0.25539#
0#
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
Temperature is dominant driver of electric load in some months . . .
R²#=#0.88355#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
R²#=#0.20403#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
January#
R²#=#0.55973#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
February#
R²#=#0.39612#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
March#
R²#=#0.83112#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
April#
R²#=#0.55973#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
May#R²#=#0.90612#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
June#R²#=#0.76431#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
July#R²#=#0.93766#
0#
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#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
October#
R²#=#0.25539#
0#
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
But understanding what drives loads in other months requires more sophisticated models . . .
R²#=#0.88355#
0#
5,000#
10,000#
15,000#
20,000#
25,000#
30,000#
20# 40# 60# 80# 100#Temperature)@)DFW)(degrees)F))
2012)Daily)Peak)Electric)Demand)(ERCOT#North#Central,#MW)#
R²#=#0.20403#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
January#
R²#=#0.55973#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
February#
R²#=#0.39612#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
March#
R²#=#0.83112#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
April#
R²#=#0.55973#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
May#R²#=#0.90612#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
June#R²#=#0.76431#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
July#R²#=#0.93766#
0#
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#
0#
10,000#
20,000#
30,000#
20# 40# 60# 80# 100#
October#
R²#=#0.25539#
0#
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 ?
Looking for answers about energy and markets ? [email protected]
AnalyticsEnergy Risk
Uncertainty: measured, modeled, managed
PHILIP DIPASTENA(972) [email protected]
