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.

1. The Case of the
Curious
Correlations

2. 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)#

3. 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

4. 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.

5. 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

6. 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

7. 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

8. 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.

9. 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 . . .

10. 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 . . .

11. 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 ?

12. Looking for answers about
energy and markets ?
info@eRiskAnalytics.com
Analytics
Energy Risk
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