Business statistics final
-
Upload
goshi-fujimoto -
Category
Business
-
view
324 -
download
2
description
Transcript of Business statistics final
1
Business Statistics- Disaggregation of energy saving
action -
2012. 12. 10
2
1. Objectives2. Data3. Analysis process4. Result5. Conclusion
0. Outline
3
BackgroundHighly 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.
1. Objectives
4
ObjectivesTo understand the energy saving actionsTo disaggregate the amount of energy saving by each
action through multi-regression model
1. Objectives
Amount of Energy Saving
Energy Saving Action
Owned Applian
ces
Temprature
Time Spending at home
Easy to act High effect
5
Overview of questionnaireThe questionnaire investigation conducted for 20’s to
60’s on the web
2. Data
Period Conducted in May. 2012Area Around Tokyo (Tokyo, Kanagawa, Chiba, Saitama)Screening condition 1) No moved since Dec. 2010
2) No changed family structure since Dec. 20103) 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 274 1,100 1,094 813 1,176Female 248 296 302 238 351
6
Making data2. Data
To sum up the amount of electricity• To extract from meter
receipt of electricity from Dec. to Mar. on 2010 and 2011
To correct for the influence of the date of metering
To correct for the influence of temperature
difference
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
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
To correct for the influence of the date of meteringCorrected 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 )
2. Data
Jan. Feb.Date of metering Date of meteringDec.Date of metering
Meter receipt on Jan. Meter receipt on Feb.
Real amount of electricity on Jan.
8
To correct for the influence of temperature difference 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 -1.7 degree C(Feb.) on 2011 compared to 2010.
2. Data
Temperature [degree C]
The
amou
nt o
f ele
ctric
ity
[kW
h/m
onth
/hou
se]
4.0 5.0 6.0 7.0 8.0 9.0 10.0 300.0
310.0
320.0
330.0
340.0
350.0
360.0
370.0
380.0
390.0
400.0
f(x) = − 8.98053001941919 x + 387.756905167774R² = 0.455390866426225
f(x) = − 11.5392746545442 x + 439.543241541095R² = 0.530129668403322
f(x) = − 11.1115945921918 x + 438.142961406614R² = 0.641650406669905
12月Linear (12月 )
1月
Dec.
Jan.
Feb.
9
3. Analysis process To know deeply the objective data and
find the correlation with various data
1. Overviewing the objective data
2. Making the correlation 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. Jan. Dec. Dec. to Feb.
Electricity 2010 368 kWh 444 kWh 364 kWh 1,175 kWh
2011 333 kWh 415 kWh 343 kWh 1,092 kWh
The amount of energy saving [kWh]
34.4kWh
28.2kWh
20.3kWh
82.9kWh
The ratio of energy saving [%]
9.4%
6.4%
5.6%
7.1%
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%
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
The degree of electricity saving actions based on 7-point scale
Detail setting temperature and using hour of heaters
Saving Action
Dispose, purchase and replacement of each appliance
Owned Appliances
Hour with nobody at home each weekday and holiday
Hour at Home
13
The overview of explanatory variablesCalculated the increasing ratio of energy saving actions on 2011
compared to 2010Some results are shown here, totally all of energy saving actions
were increased than 2010
4-2. Making the correlation matrix
Close the door when heaters are
working
Unplug the appliances
Set refrigerator low level
Boil water by gas cooking
stove
Stop rice cooker to keep warm
14
The overview of explanatory variables Investigated the time of use and set of temperature for appliances,
especially heaters and lights, on 2011 compared to 2010Some results are shown here, totally all of appliances were not
used a lot
4-2. Making the correlation matrix
Lengthen Shorten Lengthen Shorten
15
4-2. Making the correlation matrixThe amount of electricity saving
Time of AC
Time of Light in Living room
Unplug the appliances
Time of Light in Bed room …
The amount of electricity saving 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.
16
4-3. Picking up explanatory variables Top variables which have strong relationship with The
amount of electricity saving
Time of AC Close the door when heaters are working
Unplug the appliances
-0.208 -0.149 -0.153Set refrigerator low level
Boil water by gas cooking stove
Stop rice cooker from keeping warm
-0.145 -0.140 -0.146Time of Light in Living room
Time of Light in Bed room Time of TV
-0.153 -0.131 -0.125
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
HypothesisA variable
B variable
C variable
D variable
Objective variable
StatisticsE 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
Set temperature and Used hours
The degree of use for appliances
Used hours of TV and lights
Electricity saving actions
Hours at home
Model 1
Model 2
Model 3
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<0001Oil stove -61.884 P<0.001Electric stove 27.921 P<0.05Gas fan heater -97.654 P<0.001LED -42.272 P<0.001Television -25.383 P<0.001Humidifier 29.148 P<0.01Hot watering toilet seat -33.323 P<0.05Washing and drying machine 39.872 p<0.05Refrigerator -43.375 P<0.001Dish washing machine -94.446 P<0.01
AIC = 73,886
20
5. Result Result of model 2
Model 2 has used hours and set temperature of appliancesPurchasing appliances Coefficient P-value(Intercept) -52.227 P<0001Oil stove -36.630 P<0.001Electric stove 25.229 P<0.05Gas fan heater -75.162 P<0.001LED -36.574 P<0.001Television -20.834 P<0.001Humidifier 28.834 P<0.01Hot watering toilet seat -37.420 P<0.05Washing&drying machine 36.334 p<0.05Refrigerator -45.922 P<0.001Dish washing machine -81.189 P<0.01Water server 43.134 P<0.05
Used hours Coefficient P-valueAC -16.123 P<0.001Electric carpet -7.581 P<0.001Gas stove 9.405 P<0.1Gas fan heater 4.429 P<0.1Oil stove 13.932 P<0.001Oil fan heater 4.743 P<0.1Electric stove -12.388 P<0.001Halogen heater -10.119 P<0.01Electric fan heater -17.945 p<0.001Oil heater -21.057 P<0.001Lights in living room -15.646 P<0.001Lights in bed room -17.005 P<0.001TV in living ronnm -8.956 P<0.001
AIC = 73,377
21
5. Result Result of model 3
Model 3 involves energy saving actionsPurchasing appliances Coefficient P-value(Intercept) -42.499 P<0001Oil stove -30.476 P<0.01Electric stove 26.950 P<0.05Gas fan heater -65.947 P<0.001LED -27.793 P<0.001Television -15.110 P<0.05Humidifier 20.130 P<0.05Hot watering toilet seat -38.548 P<0.01Washing&drying machine 31.711 p<0.05Refrigerator -49.654 P<0.001Dish washing machine -92.589 P<0.01Water server 49.583 P<0.05
Used hours Coefficient P-valueAC -13.662 P<0.001Electric carpet -6.009 P<0.001Gas stove 11.628 P<0.05Gas fan heater 6.751 P<0.01Oil stove 13.870 P<0.001Oil fan heater 6.726 P<0.01Electric stove -10.542 P<0.01Halogen heater -8.973 P<0.05Electric fan heater -15.898 p<0.01Oil heater -19.404 P<0.001Lights in living room -11.019 P<0.001Lights in bed room -13.907 P<0.001TV in living ronnm -5.084 P<0.05
22
5. Result Result of model 3
Model 3 involves energy saving actionsEnergy saving actions Coefficient P-valueSet low temperature of electric floor heater -132.945 P<0001Set low temperature of gas fan heater -13.962 P<0.01Hours at home -3.267 P<0.1Close the door when heaters are working -20.267 P<0.001Unplug appliances -12.435 P<0.05Set refrigerator low level -16.082 P<0.01Boil water by gas cooking stove -16.636 P<0.01Stop rice cooker from keeping warm -20.869 P<0.001Used degree of humidifier 31.711 p<0.05Used degree of Hot watering toilet seat -49.654 P<0.001Used degree of washing & drying machine -92.589 P<0.01Used degree of electric pot 49.583 P<0.05
AIC = 73,190
23
5. Result The improvement of models
The improvement of R^2 is shownStill low but can extract the effective energy saving actions
which have high t-valueIt means that results show the disaggregated electricity
saving amount by effective and important actions
Model 1 Model 2
Model 3
Adjusted R^2 = 0.0358 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 saverTends to purchase much new
appliances such as LED, TV and oil stove
Passive energy saverTends to purchase much new
appliances such as fumidifier, electric stove and water server
Passive
Positive
+3σ
-3σ
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