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Business Statistics- Disaggregation of energy saving

action -

2012. 12. 10

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1. Objectives2. Data3. Analysis process4. Result5. Conclusion

0. Outline

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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

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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

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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

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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

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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.

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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.

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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

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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%

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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%

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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

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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

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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

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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.

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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