A Social experiment for controllingelectricity demand by visualization- A case of Nushima Island
Dec. 13, 2013
Koji Shimada (Ritsumeikan Univ., Japan)
Yuki Ochi (E-konzal, Japan)

Introduction
Background Distributed autonomous energy systems have received remarkableattention.
– as a measure for climate change and natural hazards mitigation
 Especially for remote islands, such systems are expected to be a
powerful solution for disaster preparedness.
 In the system, power demand management system will play asignificant role.

Objective This study puts its focus on developing an effective methodology ofcontrolling electricity demand suited for regional conditions.
– Conducting a social experiment of visualizing real-time electricity
consumptions in Nushima Island, one of remote islands in Japan– Estimating an effect of visualization to reduce power demand bypanel data analysis
1Where is Nushima Island?
Special zone
“Awaji Environmental Future Island”

NushimaIslandNIES

2About Nushima IslandArea

2.71km2
A little larger than Tokyo Disney Resort

Population

514 persons

Number ofhouseholds

231 households

Main industries Fishery
Famous for “hamo”– A kind of eels– Popular food in summer in Kyoto

Features Electricity and water aresupplied from Awaji Island
through submarine cables.
 There are no high school orpolice stations.
3Project design
Outline of the social experiment
Place

Nushima IslandPeriod

3 years (2012-2014)

Number of participants 51 households
Objective

Developing an effective methodology ofmanaging electricity demand Several projects are implemented in Nushima Island as a part of thevision for Awaji Environment Future Island.
– Example: Photovoltaic and DC power system– This experiment of visualization is one of them.
 In 2012, smart meters were installed in 51 houses.
 In May 2013, tablet PCs are distributed to the participants and theelectricity consumptions has been presented through them.
 In this study, power consumptions measured by the smart meters during
this summer are analyzed.

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Smart meter Smart meters were installed to 51houses in 2012.

 Equipment is covered by a white box inorder to prevent damage from salt
breeze.

Smart meter Smart meters measure electricity
consumptions continually.

 Consumption data are sent to a server.

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Visualization
 Tablet PCs are distributed tothe participants in May 2013.

Electricity consumptions in your house
Now
Today
Yesterday
This month(W/person) (Wh/person) (Wh/person) (Wh/person)
450

3,000

7,500

27,000Per-capita electricity consumptions

Only in pattern 2 and 3

 Contents of visualization arerotated monthly from pattern 1
to pattern 3.
Only in pattern 3

Now
Today
Yesterday
This month(W/person) (Wh/person) (Wh/person) (Wh/person)

Your house

150

1,000

Average ofall houses

2,500

9,000

3,625

10,756Per-capita electricity consumptions(Wh/person)

 Each family can view their realtime electricity consumptions
through a tablet PC.

Time

Least Electricity consumption ranking
Your rank: 14th (yesterday)

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Methodology
Panel data analysis– Sample: 51 households×183 days

Apr. 1

Cross-section data
[Household number]

Analyzing factors that may have an
influence on electricity consumptions by
panel data analysis– Formulating an estimating equation that
explain electricity consumptions– Estimating coefficients α by using panel
dataTime-series data
[Date]
Apr. 2…

Sep. 30

12…

Panel data

5051

(Apr. 1 – Sep. 30)(Per-capita electricity consumptions)= (Constant) + α1 × (Temperature) + α2 × (Wind speed)+ α3 × (Number of refrigerators)+ α4 × (Number of commercial freezers)+ α5 × (Frequency of viewing consumptions by a tablet PC [Pattern1])+ α6 × (Frequency of viewing consumptions by a tablet PC [Pattern2])+ α7 × (Frequency of viewing consumptions by a tablet PC [Pattern3])+ α8 × (Dummy variables for households where all energy is supplied with electricity)+ α9 × (Dummy variables for timber frame houses)+ α10 × (Dummy variables for summer vacation)

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Data collectionVariables

References

Remarks

Per-capita electricity
consumption

Smart meters

Household size byquestionnaire

Daily mean temperature

Japan Meteorological
AgencyDaily mean wind speed

Japan Meteorological
AgencyNumber of refrigerators

Questionnaire

Number of commercial
freezers

Questionnaire

Frequency of viewing
consumptions by a tablet PC

Tablet PC access log

Dummy variables forhouseholds where all energy
is supplied with electricity

Kansai Electric Power Co.,Inc.

Yes: “1”
No: “0”

Dummy variables for timber
frame houses

Questionnaire

Yes: “1”
No: “0”

Dummy variables for summer
vacation-Aug. 8 – Aug. 18: “1”The rest: “0”8Results
Estimation
Results
Constant

Coefficient
α1,565***

Daily mean temperature

31.21

***

Daily mean wind speed

31.45

Number of refrigerators

2,249

***

Number of commercial freezers

3,298

***

Pattern 1-31.23***

Pattern 2-18.30***

Pattern 3

77.82

***

Dummy variable for households
where all energy is suppliedwith electricity

215.4

***

Dummy variable fortimber frame houses-752.4***

Dummy variable forsummer vacation

2,347

***

Frequency ofviewing
consumptions
by a tablet PCAdjusted R-squared

0.9656

Durbin-Watson stat1.392

Model*** indicates statistical significance at the 1% level.

Cross-section
SUR

The larger the value is, the larger an
impact on power demand is.
 Positive value→ Electricity consumptions increase.
 Negative value→ Electricity consumptions decrease.

*** indicates statistical significance atthe 1% level.

Adjusted R-squared is an indicator forthe estimating equation.
The closer to 1, the better.

9Results
Estimation
Results
Constant

Coefficient
α1,565***

Daily mean temperature

31.21***Daily mean wind speed

31.45

Number of refrigerators

2,249

***

Number of commercial freezers

3,298

***

Pattern 1-31.23***

Pattern 2-18.30***

Pattern 377.82***

Dummy variable for households
where all energy is suppliedwith electricity

215.4

***

Dummy variable fortimber frame houses-752.4***

Dummy variable forsummer vacation

2,347

***

Frequency ofviewing
consumptions
by a tablet PC

Adjusted R-squared

0.9656

Durbin-Watson stat

1.392

Model
*** indicates statistical significance at the 1% level.

Cross-section
SUR The estimating equation explains
electricity consumptions by 97%.
 Factors other than wind speed
are considered statisticalsignificant at the 1% level.
 As a tablet PC is viewed every
time, per-capita electricity
consumptions– decrease by 31 Wh/day (Pattern 1)– decrease by 18 Wh/day (Pattern 2)– increase by 78 Wh/day (Pattern 3)

 A family who checks theirconsumptions 5 times a day
saves electricity by 2.6%
compared with a family whodoes not.
Visualization has some effect toreduce power demand, but it isinsufficient.
10Conclusions
 We are conducting a social experiment of visualizing
real-time electricity consumptions in Nushima Island,
one of remote islands in Japan.
 Factors that may have an impact on electricity
consumptions were analyzed by panel data analysis.
 Although visualization has some effect to reduce power
demand, a more influential instrument is needed.

 In this experimental project, dynamic pricing will be
designed and introduced in 2014 based on the present
study. Economic incentive with visualization is assumed
to be more effective to manage electricity demand.
 These kinds of consumption behavioral change had
better be taken into account when projecting futureenergy demand in smart-energy communities.
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Thank you for your kind attention.