3. 1. Objectives
Background
Highly interested in human behavior concerning energy saving
since the disaster on 311, 2011 in Japan.
Data analysis on amount and actions for energy saving is
important to understand this condition.
Problems
It is not still unclear how the relationship between amount of
energy saving and energy saving actions was explained.
It is not still unclear how much energy saving is and which
actions can contribute to energy saving.
3
4. 1. Objectives
Objectives
To understand the energy saving actions
To disaggregate the amount of energy saving by each
action through multi-regression model
Energy
Saving
Action
Owned
Applian
ces
Easy to act
4
Amount of
Energy Saving
Temp
rature
Time
Spendi
ng at
home
High effect
5. 2. Data
Overview of questionnaire
The questionnaire investigation conducted for 20’s to
60’s on the web
Period
Conducted in May. 2012
Area
Around Tokyo (Tokyo, Kanagawa, Chiba, Saitama)
Screening condition
1) No moved since Dec. 2010
2) No changed family structure since Dec. 2010
3) Sample having electric and gas meter receipts on Dec.
to Mar. 2010 and 2011
# of samples
5,892 ss
【Age - Sex
sample rate】
20s
30s
40s
50s
60s
Male
1,100
1,094
813
1,176
Female
5
274
248
296
302
238
351
6. 2. Data
Making data
To sum up the
amount of electricity
• To extract from meter
receipt of electricity
from Dec. to Mar. on
2010 and 2011
To calculate the
amount of energy
saving
• To calculate the amount of
energy saving on 2011 winter
compared to 2010 based on the
corrected amount of electricity
6
To correct for the
influence of the
date of metering
To correct for the
influence of
temperature
difference
The effect of each
actions for energy
saving
• To conduct multi regression
analysis with the amount of
energy saving as objective variable
and each action as explanatory
variables
7. 2. Data
To correct for the influence of the date of metering
Corrected the amount of electricity because inputted
data by sample through questionnaire is based on
each date of metering
For instance,
（The amount of ele. on Jan.）＝
（date of metering）×（The amount of ele. on Jan. / 31）＋
（31-date of metering）×（The amount of ele. on Dec. / 28）
Dec. of metering
Date
Jan. of metering
Date
Meter receipt on Jan.
Feb. of metering
Date
Meter receipt on Feb.
Real amount of electricity on Jan.
7
8. 2. Data
To correct for the influence of temperature difference
The amount of electricity
[kWh/month/house]
To make single regression model using the amount of electricity per a
house on Dec., Jan. and Feb. from 1998 to 2010 in TEPCO area and
temperature in Tokyo.
To definite this coefficient as a temperature corrected coefficient
(decrease electricity of 11.1kWh(Dec.), 11.5kWh(Jan.) and 9.0kWh(Feb.)
per 1 degree Celsius.)
Decreased electricity to correct -2.5 degree C(Dec.), -0.5 degree C(Jan.)
and -1.7400.0
degree C(Feb.) on 2011 compared to 2010.
390.0
380.0
y = -11.11x + 438.1
R² = 0.641
370.0
360.0
y = -11.53x + 439.5
R² = 0.530
350.0
1月
Jan.
340.0
330.0
320.0
2月
Feb.
y = -8.980x + 387.7
R² = 0.455
310.0
300.0
4.0
8
12月
Dec.
5.0
6.0
7.0
8.0
9.0
10.0
Temperature [degree C]
9. 3. Analysis process
To know deeply the objective data and
find the correlation with various data
9
1.
Overview
ing the
objectiv
e data
2. Making
the
correlatio
n matrix
3. Picking
up
explanatory
variables
4.
Developing
the multi
regression
model
5.
Improving
the multi
regression
model
10. 4-1. Overviewing the objective data
Average energy saving
Calculated the amount (82.9kWh down) and the ratio
(7.1% down) of energy saving by using the corrected
electricity by date of metering and temperature from
Dec. to Feb. on 2011
Dec.
Electricity
Jan.
Dec.
Dec. to Feb.
2010
368 kWh
444 kWh
364 kWh
1,175 kWh
2011
333 kWh
415 kWh
343 kWh
1,092 kWh
34.4
28.2
20.3
82.9
kWh
kWh
kWh
kWh
9.4
6.4
5.6
7.1
%
%
%
%
The amount of energy
saving [kWh]
The ratio of energy saving
[%]
10
11. 4-1. Overviewing the objective data
The overview of the amount of electricity saving
The amount of electricity conservation is normal
distributed with the center of 82.9kWh on average
70% of sample could accomplish energy saving
70%
11
12. 4-2. Making the correlation matrix
The definition of explanatory variables
Heard the degree of 3 segments of energy saving actions based
on questionnaire survey
Explanatory variables are defined by the difference of the degree
of all the actions between 2010 and 2011
Saving
Action
The degree of
electricity saving
actions based on 7point scale
Detail setting
temperature and
using hour of
heaters
12
Owned
Appliances
Dispose, purcha
se and
replacement of
each appliance
Hour at
Home
Hour with
nobody at home
each weekday
and holiday
13. 4-2. Making the correlation matrix
The overview of explanatory variables
Calculated the increasing ratio of energy saving actions on 2011
compared to 2010
Some results are shown here, totally all of energy saving actions
were increased than 2010
Close the door
when heaters are
working
13
Unplug the
appliances
Set refrigerator
low level
Boil water by
gas cooking
stove
Stop rice cooker
to keep warm
14. 4-2. Making the correlation matrix
The overview of explanatory variables
Investigated the time of use and set of temperature for appliances,
especially heaters and lights, on 2011 compared to 2010
Some results are shown here, totally all of appliances were not
used a lot
Lengthen
14
Shorten
Lengthen
Shorten
15. 4-2. Making the correlation matrix
The amount of
electricity
saving
The amount of electricity
saving
Time of
Light in
Living
room
Time of
AC
Unplug the
appliances
Time of Light
in Bed room
…
1
0.21
0.15
0.15
0.13 …
Time of AC
0.21
1
0.17
0.11
0.12
Time of Light in Living
room
0.15
0.17
1
0.14
0.16 …
Unplug the appliances
0.15
0.11
0.14
1
0.07 …
Time of Light in Bed room
0.13
0.12
0.16
…
…
…
0.07
…
1 …
…
…
To find the explanatory variables which have the strong relationship with the
amount of electricity saving.
To categorize the similar explanatory variables not to include
multicollinearity.
15
16. 4-3. Picking up explanatory variables
Top variables which have strong relationship with
The amount of electricity saving
Time of AC
-0.208
Set
refrigerator
low level
-0.145
Close the door when Unplug the
heaters are working appliances
-0.149
Boil water by gas
cooking stove
-0.140
-0.153
Stop rice cooker
from keeping
warm
-0.146
Time of Light Time of Light in Bed
Time of TV
in Living room room
-0.153
-0.131
-0.125
16
17. 4-4. Developing the multi regression
model
Based on hypothesis and statistical approach, I
developed the multi regression model.
Hypothesis is the most important because model
must be easy to explain and be accepted to audience.
Then I tried to find the optimal explanatory variables
without decreasing p-value, AIC and R^2
Hypothesis
Statistics
A variable
E variable
B variable
C variable
D variable
17
Objective
variable
F variable
G variable
.
.
.
.
18. 4-5. Improving the multi regression
model
Step up explanatory variables
Step up explanatory variables from the fundamental
factor influenced to electricity
Made three models by adding variables in turn with
checking AIC and p-value
Electricity saving
Purchasing appliances
Model 1
Set temperature and Used hours
The degree of use for appliances
Used hours of TV and lights
Model 2
Electricity saving actions
Model 3
18
Hours at home
19. 5. Result
Result of model 1
Model 1 is the simple model based on variables
showing the purchase of appliances
Coefficient
P-value
(Intercept)
-59.858
P<0001
Oil stove
-61.884
P<0.001
Electric stove
27.921
P<0.05
Gas fan heater
-97.654
P<0.001
LED
-42.272
P<0.001
Television
-25.383
P<0.001
Humidifier
29.148
P<0.01
-33.323
P<0.05
39.872
p<0.05
Refrigerator
-43.375
P<0.001
Dish washing machine
-94.446
P<0.01
Hot watering toilet seat
Washing and drying machine
AIC = 73,886
19
20. 5. Result
Result of model 2
Model 2 has used hours and set temperature of appliances
Purchasing appliances
Coefficient
Pvalue
(Intercept)
-52.227
Oil stove
-36.630 P<0.001
Electric stove
25.229
P<0001
P<0.05
Used hours
AC
Electric carpet
Pvalue
-16.123 P<0.001
-7.581 P<0.001
Gas stove
9.405
P<0.1
4.429
P<0.1
Gas fan heater
-75.162 P<0.001
Gas fan heater
LED
-36.574 P<0.001
Oil stove
Television
-20.834 P<0.001
Oil fan heater
Humidifier
Coefficient
13.932 P<0.001
4.743
P<0.1
28.834
Hot watering toilet seat
Washing&drying machine
P<0.01
Electric stove
-12.388 P<0.001
-37.420
P<0.05
Halogen heater
-10.119
36.334
p<0.05
Electric fan heater
-17.945 p<0.001
P<0.01
Refrigerator
-45.922 P<0.001
Oil heater
-21.057 P<0.001
Dish washing machine
-81.189
Lights in living room
-15.646 P<0.001
Lights in bed room
-17.005 P<0.001
TV in living ronnm
-8.956 P<0.001
Water server
20
P<0.01
43.134= 73,377
P<0.05
AIC
21. 5. Result
Result of model 3
Model 3 involves energy saving actions
Purchasing appliances
Coefficient
Pvalue
Used hours
Coefficient
Pvalue
(Intercept)
-42.499
P<0001
AC
Oil stove
-30.476
P<0.01
Electric carpet
-6.009 P<0.001
26.950
P<0.05
Gas stove
11.628
P<0.05
6.751
P<0.01
Electric stove
-13.662 P<0.001
Gas fan heater
-65.947 P<0.001
Gas fan heater
LED
-27.793 P<0.001
Oil stove
Television
-15.110
P<0.05
Oil fan heater
6.726
P<0.01
Humidifier
20.130
P<0.05
Electric stove
-10.542
P<0.01
-38.548
P<0.01
Halogen heater
-8.973
P<0.05
31.711
p<0.05
Electric fan heater
-15.898
p<0.01
Hot watering toilet seat
Washing&drying machine
13.870 P<0.001
Refrigerator
-49.654 P<0.001
Oil heater
-19.404 P<0.001
Dish washing machine
-92.589
P<0.01
Lights in living room
-11.019 P<0.001
49.583
P<0.05
Lights in bed room
-13.907 P<0.001
Water server
21
TV in living ronnm
-5.084
P<0.05
22. 5. Result
Result of model 3
Model 3 involves energy saving actions
Energy saving actions
Set low temperature of electric floor heater
Coefficient
P-value
-132.945
P<0001
-13.962
P<0.01
-3.267
P<0.1
Close the door when heaters are working
-20.267
P<0.001
Unplug appliances
-12.435
P<0.05
Set refrigerator low level
-16.082
P<0.01
Boil water by gas cooking stove
-16.636
P<0.01
Stop rice cooker from keeping warm
-20.869
P<0.001
31.711
p<0.05
Used degree of Hot watering toilet seat
-49.654
P<0.001
Used degree of washing & drying machine
-92.589
P<0.01
49.583
P<0.05
Set low temperature of gas fan heater
Hours at home
Used degree of humidifier
Used degree of electric pot
AIC = 73,190
22
23. 5. Result
The improvement of models
The improvement of R^2 is shown
Still low but can extract the effective energy saving actions
which have high t-value
It means that results show the disaggregated electricity
saving amount by effective and important actions
Model 3
Model 1
Adjusted R^2 = 0.0358
23
Model 2
Adjusted R^2 = 0.1185
Adjusted R^2 = 0.1493
24. 5. Result
The residual analysis
The big difference between active
and passive energy savers is
whether they purchased new
appliances
Positive energy saver
Tends to purchase much new
appliances such as LED, TV
and oil stove
Passive energy saver
Tends to purchase much new
appliances such as fumidifier,
electric stove and water server
Passive
+3σ
-3σ
Positive
24
25. 6. Conclusion
The feature of effective energy saving actions
Summarize the feature of effective energy saving actions
from the result of Model 3
• The effect of purchase of new appliances, especially electric
heater, LED, is highest
• Reducing used hours of appliances is more effective for saving
energy than setting low temperature
• Switching to gas and oil heaters contributes to saving energy
due to the avoidance of electric heaters including AC
• Reducing the use of electric heat generator such as
humidifier, Hot watering toilet seat, electric pot, drying
machine and rice cooker is definitely important to save energy
25
