207

Longitudinal Evaluation of Energy-saving Effects and Their Implication
Using Electricity Monitoring Data in Shinchi Town, Fukushima
Ayami OTSUKA1* Yujiro HIRANO2 Shogo NAKAMURA2 Tsuyoshi FUJITA3
and Daisuke NARUMI4
Faculty of Social Sciences, Waseda University
1-6-1 Nishi Waseda, Shinjuku-ku, Tokyo 169-8050, Japan2
Regional Environmental Renovation Section, Fukushima Branch, National Institute for Environmental Studies
10-2 Fukasaku, Miharu Town, Tamura-gun, Fukushima, 963-7700, Japan
3
Center for Social and Environmental Systems Research, National Institute for Environmental Studies
16-2 Onogawa, Tsukuba-shi, Ibaraki, 305-8506 Japan
4
Faculty of Environmental and Information Sciences, Yokohama National University
79-7 Tokiwadai, Hodogaya-ku, Yokohama-shi, Kanagawa, 240-8501 Japan*E-mail: aotsuka@aoni.waseda.jp
1

Abstract
This study evaluated the energy-saving effects of electricity consumption monitoring and energy-saving
campaigns in Shinchi Town, Fukushima, Japan, where a community energy management system wasintroduced as part of recovery efforts after the Great East Japan Earthquake of 2011. First, a longitudinalevaluation through all six energy-saving campaigns conducted since 2014 illustrates that the first campaign
showed the highest energy-saving effect, indicating the energy-saving potential brought by visualization of
one’s own energy consumption by home energy management systems (HEMS). Declining trends in energysaving were observed over time. In addition, the correlations between the effects of energy-saving campaigns
and trends in longer-term electricity use were noted to be minimal, indicating that the effects of energy-saving
campaigns did not continue beyond the campaign period. It appears, however, that electricity consumption
rates were restrained during each campaign implementation period. The fact that repeated appeals for energy
savings were met with responses by the study target each time provides good prospects for demand-response
processes that incorporate consideration of renewable energy usage.
Key words :	electricity monitoring, energy-saving campaigns, residential sector, supply-demand balance

1. Introduction
The 2015 Paris Agreement was signed in response to
an increased need to tackle global warming. To
accomplish the aims of the Agreement, saving energy andpromoting a low-carbon society are issues of utmost
urgency. Under the Paris Agreement, Japan pledged toreduce CO2 emissions in the residential and commercial
sectors by 26% compared to 2013 levels. Of this,approximately 22% is to be reduction of CO2 originatingfrom energy use, wherein the residential sector is to
reduce CO2 emissions by approximately 40% (Global
Warming Prevention Headquarters, 2020).
Providing information to energy consumers is
considered essential for promoting energy-saving
activities. One means of doing so is to develop homeenergy management systems (HEMS) that enable“visualization” of energy usage. A variety of efforts,
including energy-use diagnostics, offering advice
Global Environmental Research
24/2020: 207-216
printed in Japan

regarding energy conservation, and various ways ofmaking energy “visible” within the home, often conducted
as part of smart-city empirical social experiments, havebeen performed using HEMS data for promoting energysaving.
The Great East Japan Earthquake (GEJE) of 2011
made Japan’s citizens more inclined to save energy in
general. It also changed the situation surrounding energy
issues in Japan. A fixed-price feed-in-tariff (FIT) system
for renewable energies, for instance, was put in place.
Progress was also made toward liberalizing electrical
power retailing.
Amid these changes, energy demand-and-supply
management on a regional scale is becoming increasingly
important. Indeed, 13% of the targeted 40% reduction forthe household sector under the Paris Agreement concerns
low-carbonizing of electrical power, increasing
anticipations of more aggressive introduction ofrenewable energy (Global Warming Prevention©2020 AIRES

208

A. OTSUKA et al.

Headquarters, 2020). Community energy-management
systems centered on renewable energies are essential toenable active transformation of energy demand to meetthe fluctuating output levels of renewable energy, which
is highly dependent on weather conditions, and this is a
key means of ensuring stability in the power supply-anddemand balance.
This demand-response (DR) method can be defined
as inducing leveling of electricity demand by shifting
electricity consumption patterns via such measures aselectricity price setting or payment of incentives, assummarized under the terms used by the Agency for
Natural Resources and Energy (2020). In other words,
DR is a mechanism by which energy consumers cancontribute to balancing of supply versus demand forelectricity. During “smart city” social demonstration
experiments conducted shortly after the Earthquake, anumber of studies were conducted regarding peak-cut
effects in times of “tight” supply and demand for seasonalreasons, namely air-conditioning and heating. In
Yokohama City, a maximum 15% peak-cut (compared tothe past performance baseline) was reportedly observed
for seasonal demand in the household sector (City of
Yokohama, 2017). A study in Kitakyushu City also
showed 10% –20% peak-cut effects via price setting(New Energy Promotion Council, 2014). On the other
hand, concurrent with the more widespread use ofrenewable energy in the future, increased needs for DR
are likely, particularly for balanced-adjustment effects
with regard to weather-induced fluctuations of supply.
However, there is a lack of knowledge on this issue since
there have been few case studies on regional energy
systems, especially with regard to the establishment ofsmall smart communities in rural areas.
A broadly common issue in promoting energy
conservation to counter global warming and energy
supply/demand (S/D) management is that changes inenergy demand depend on the behavior of energy
consumers, that is, individuals in society. In this regard,
knowledge about ways of inducing energy-saving
behavior can provide cues for evaluating future energydemand flexibility. In fact, Shimoda et al. (2020) statethat there is a need to accumulate more research andknowledge in energy demand science, a newinterdisciplinary science that integrates natural,behavioral and data science, whereby two conflicting
requirements for promoting energy saving while at thesame time seeking to increase energy demand flexibility
should be effectively addressed.
In this regard, the aims of this paper are three-fold.
The first is to present a longitudinal evaluation of theresults of six energy-saving campaigns conducted using
electricity usage data from HEMS in Shinchi Town,
Fukushima Prefecture. This system was established as asmart-community development project, part of the townrevitalization plan aimed at recovery from the damage

caused by the Earthquake. The second is to attempt toevaluate electricity-consumption trends over a longer
term, also using HEMS data. The final aim is, by
combining the results of the above-mentioned two sets oflongitudinal evaluation, to draw implications forcontrolling electricity demand towards introduction of
low-carbon community energy systems.

2. Overview of the Study
2.1 Study Target
In this paper, a case study from Shinchi Town,
Fukushima Prefecture is presented. Shinchi is a small
municipality with a population around 8,000, located at
the northernmost tip of the Fukushima coastline,
bordering with Miyagi Prefecture to the north and west,
Soma City to the south, and the Pacific Ocean to the east
(Fig. 1). It is approximately 300 km north of Tokyo.
In Shinchi, around 20% of the town area wasdamaged or destroyed during the Earthquake of March
2011. In December 2011, Shinchi was selected as an“environmental future city” under the Japanese
government’s “FutureCity” Initiative. Since then, the“Smart Hybrid Town” concept has been applied as part of
the town’s redevelopment efforts to enhance its
environmental, economic and societal values. A local
information network system (regional ICT system) wasestablished to disseminate information about energyconservation activities in the area, provide regional
information and bulletin boards and support the daily
lives of senior persons among other functions. Energy
usage has been made visible from 2014 through smart
meters and electronic display terminals under the“Shinchi Daily Life Assistance System (SDLAS)”
utilizing this ICT system. In addition, with the long-term
vision of meeting a portion of the region’s energy needs
by local energy companies using thermal power andnatural energy, Shinchi Smart Energy, a special purpose
company, was established in February 2018 to supply
energy locally. It began supplying energy to a certain partof the redeveloped areas by the end of the 2018 fiscal
year (FY). Subsequently, it has aimed to perform S/Dadjustment via DR during actual operations (Shinchi

Fig. 1 Location of Shinchi Town.

Longitudinal evaluation of energy-saving effects and its implication using electricity monitoring data

209

Fig. 2 Main demographic attributes of the study targets.

Town, 2018).
The target of this study is the households that havevolunteered to be monitored under the above-stated
SDLAS. Energy monitoring started from June 2014 in theorder of the participants’ selection. While the number ofhouseholds peaked at 75 during FY2015, data from 68
households were used in this study, due to withdrawal
from the program and system connection status. Figure 2
presents the main demographic attributes of thehouseholds, summarized from responses to aquestionnaire survey conducted at the time of volunteer
recruitment. As shown in the figure, characteristics ofhouseholds in Shinchi differed from those of urban area
households, in that many were three-generation
households with a large number of family members, andthat they frequently used kerosene for heating and waterheating purposes. Although not described in the figure
due to sample-number differences, Shinchi residencescharacteristically had large houses with 5 –15 rooms each
(38 of 39 households covered in a lifestyle survey
conducted in December 2014). While it is not clear how
the target households of this study had been affected by
the Earthquake, it is reasonably presumed that physicaldamage to their houses was not so severe, judging fromthe fact that a majority continued to live in houses
constructed before the Earthquake.
2.2 Electricity Monitoring
The HEMS units used in Shinchi were installed in
the monitors’ existing detached homes. Each household
participating as a monitor under SDLAS chose six lines
for monitoring whereby visualization of electricity wasenabled. The six measuring points, therefore, differedaccording to household: some chose individual lines suchas air-conditioning in each room, whereas others chose
six rooms such as kitchen, bedroom, child room, etc. In
this respect, the electricity monitoring system in Shinchi
differed from the standardized HEMS collectively
installed in newly built apartments where what wasmonitored was consistent for all households. Total values
for each monitoring line, as well as the total electricity
consumption for the entire household, all measured
hourly, were stored on a cloud server. A tablet computer

Table 1 Average electricity consumption per household.
SpringSummerFall

Winter

1-2 persons

12.98

14.97

11.47

18.69

3-4 persons

12.30

13.43

11.08

20.05

5-6 persons

16.57

19.19

15.67

24.20

> 7 persons

24.02

27.38

24.30

38.66
(unit: kWh/day)

was distributed to each household to provide information
on energy consumption and other life-related details
described above.
Table 1 summarizes the daily average electricity
consumption per household by number of persons and byseason, according to the HEMS monitoring data.
The seasonal data comprised the following from2015 to 2017: spring and fall, i.e., two-week periods withlow variability in average temperatures; summer, i.e., a
two-week period with higher average temperatures withthe least fluctuation; and winter, i.e., a two-week periodwith lower average temperatures and the least variability.
While demand increased during summer and winter due
to air-conditioning and heating, considering that Shinchi
is located in a coastal district, a comparatively cooler
climate meant that summer peaks were lower comparedto those for the Tokyo Metropolitan Area. Conversely,
while there was comparatively little snowfall in the area
despite its location in the Tohoku region, the winters werecold, leading to high peaks in demand for heating andkerosene. Households having a larger number of
inhabitants (especially for households of seven or more
persons) typically had higher electricity consumption.
The exception was that households with one to two
persons had higher electricity consumption in all seasons
other than winter compared to households with three tofour persons. This was possibly because one-to-two
person households in urban areas often have one or both
adults working and not home during the daytime.
Meanwhile, one-to-two person households in Shinchi
would have higher consumption given the characteristics
of rural towns in Japan with their higher elderly
population ratios, where either one or both adults are
already retired and staying home during daytime, leading

210

A. OTSUKA et al.
Table 2 Energy-saving campaigns conducted in Shinchi.
PeriodNo. of households

Incentives

Information provision

Energy-saving effects*

1

Sept. 201422 / 46

Yes (top 3)

Via tablet PC

0.96%

2

Nov. 201412 / 46

Yes (top 3)

Via tablet PC-5.09%

3

Mar. 201524 / 55

Yes (top 3)

Advisory report (customized)

2.86%

4

Dec. 2015

75

None

Advisory report (customized)

1.58%

5

Mar. 2017

75

None

Advisory report (non-customized)-

6

Nov. 2017

68

None

Advisory report (customized)

2.80%* Energy-saving effects are averaged for all households. Due caution is needed for comparison as the evaluation method for each campaign
differed. Negative figures indicate an increase in electricity use. Details on each evaluation method are available in Shiraki et al. (2016) for
campaigns 1–3, Nakamura et al. (2018) for campaign 4, and Masuda et al. for campaign 6.

Fig. 3 Examples of “Shinchi Energy Savings Newsletter.”to higher electricity consumption. In addition, in terms ofhourly electricity usage, data from the monitoring systemwere characterized by a morning peak at around 6 a.m.
and a nighttime peak from around 6 p.m. and thereafter.
2.3 Energy-saving Campaigns
Since the establishment of the monitoring systemand start of measurement, six different electricity-saving
campaigns have been promoted (Table 2). In campaigns
1–3, the top-ranking households were awarded coupons
for local shopping. However, no such incentives were
offered during subsequent campaigns.
In the first two campaigns (campaigns 1–2), realtime assessment of power consumption was enabled inindividual households as details of power consumption(including rankings that allowed comparisons between
households) were made “visible” to householders(Masuda et al., 2018). In the four subsequent campaigns(campaigns 3–6), in addition to the above information,
households were provided paper-based reports titled“Shinchi Energy Savings Newsletter (kawara-ban)”containing advice and suggestions (Fig. 3). Information
was provided in Japanese and customized for eachhousehold, including consumption percentages by thehour of day and system type. Moreover, other general
information on energy saving tips was provided, such as
the amount of energy-conservation expected from
replacing existing electrical devices with more energyefficient equivalents.
With the exception of Campaign 5, where noverification was made of per-household energy-savings
effects, in each campaign, the average energy-savings
effects of participating households ranged from –5.0% to+2.9% (Shiraki et al., 2016; Nakamura et al., 2017;
Masuda et al., 2018). The negative figure of –5.0%
indicating an increase in electricity use for Campaign 2
was reportedly due to “wear-out” effects, according to
Shiraki et al. (2016), as the first and the second campaign
took place only one month apart from each other. It
should be noted, however, that as evaluation methods
differed by campaign, no overall comparisons across the
six campaigns were possible.
2.4 Methods of Longitudinal Evaluation for Electricity
Consumption and Energy-savings Effects
A suitable method was needed to evaluate energy-Longitudinal evaluation of energy-saving effects and its implication using electricity monitoring data

211

Fig. 4 An example of scatterplots and prediction formulas.

savings effects longitudinally across the past campaigns
as well as changes in energy consumption over time after
introduction of the electricity monitoring system. For
this, following the method used in the most recent
campaign (Campaign 6), a prediction formula wasemployed that was derived from temperatures and actual
electricity consumption of each household.
The daily electricity consumption of each household
against the daily average outside temperature for Shinchi
Town as published by the Japan Meteorological Agency(2017) was plotted for the period from June 2014 throughAugust 2017. Quadratic polynomial approximation lines
were then drawn on the scatterplot for each fiscal year,
and the derived equation was used as the prediction
formula for per-household electricity consumption foreach fiscal year (Fig. 4). Hence, scatterplots andprediction formulas were created for all the selected 68
households.
To assess electricity consumption over time, year-toyear changes were first computed for three values oftemperature representative of each season, viz. 5°C(winter), 15°C (spring and fall) and 25°C (summer). For
assessing change rates over time, the change rates foreach fiscal year, and change rate between FY2015 andFY2017 were calculated to assess the changes over alonger period of time. Then, to assess the effectiveness ofpast energy-saving campaigns, actual daily averagetemperatures were included in the prediction formulas forthe fiscal years wherein the campaign was conducted.
Subsequently, the differences between the predictedvalues (from which temperature effects were excluded)
and actual measured values within the campaign period
were considered “energy-saving effects” (or, vice versa,
energy-increase effects).
It is noteworthy that for evaluating energy-saving
campaigns, the use of randomized controlled trials(RCTs, which involve the random allocation of test
subjects to the control group) is recommended for greater

accuracy. However, it is also often considered difficult toperform strict RCTs (Bhushan et al., 2018) for any
experiment involving the household sector, largely
because of costs, and secondarily because it often requires
voluntary participation in questionnaire surveys to enable
researchers to understand background factors forbehavioral changes. This conflicts with the nature of
RCTs. There is a commonly recognized need for more
appropriate evaluation methods in cases where RCTs
cannot be performed (Bhushan et al., 2018; Vine, 2014).
The same issue was shared in this study: the participatinghouseholds were originally voluntary, and it was difficult
to include non-monitored households to serve as acontrol. Hence, in the present study the method used wasbased on actual data from each household and predictedvalues estimated using the formula.
2
However, the prediction accuracy (i.e. R value of theprediction equation) varied by fiscal year. In the case ofhouseholds in Fig. 4, the R2 value for the same householdvaried from 0.47 to 0.79. This variation was considered
due to limited data availability: for FY 2014 and FY
2017, the data used for analysis included only specificperiods, possibly lowering R2. Moreover, factors that
could potentially explain increases in electricity
consumption in particular fiscal years (e.g., FY 2014) are
unknown. Although questionnaire surveys aimed at
identifying factors that could potentially influenceelectricity consumption (e.g., purchase of energy-saving
household electrical appliances, or changes in family
composition) were conducted, but it proved difficult toascertain all factors. These limitations were taken into
consideration during the analysis and attempts were madeto estimate energy-saving and increase effects fromelectricity monitoring data and the results of the energysavings campaigns.

212

A. OTSUKA et al.

3. Results and Discussions
3.1 
Longitudinal Evaluation of the Energy-saving
Campaigns
Results of longitudinal evaluations of the past six
energy-saving campaigns are indicated in Fig. 5. The
horizontal axis depicts the campaign period and thepreceding and succeeding two-week periods. However, as
noted in the discussion regarding Table 2, the horizontal
axis is not continuous, as there was more than one year’s
gap between campaigns 4 and 5.
It is evident that the effect of the energy-saving
campaign was up to 7.7% for subject household averages
during one campaign period (Campaign 1). The
corresponding values for campaigns 2, 3, and 4 were,respectively, 6.8%, 0.8%, and –3.9%, i.e., a continuous
decline in its energy-saving effects. (Notably, in
Campaign 3, since there were no data available to identify
the participating households and their energy data, thefigure indicates the average measurement values for all
households). With respect to the above declining trend,and considering that only volunteer households
participated in campaigns 1 and 2 during the early periodof the electricity monitoring system, the results suggestthat the effects of making energy usage “visible” to users
were large, as these were new and of novel interest. As
for Campaign 4, the large consumption increase was
likely due to the fact that the campaign period covered
the year end and New Year’s period. This season isassociated with substantial changes in normal daily-life
patterns (e.g., household members spending a longer time
at home, and relatives and guests coming to visit fromoutside). For campaigns 5 and 6 these figures were 0.1%
and –1.3%, respectively. While these indicate improved
energy-savings rates compared to those for Campaign 4,these figures showed a slight increase vis-à-vis predictedvalues used for longitudinal evaluation, i.e., prediction
values from which atmospheric temperature effects were
excluded. Energy-savings effects were particularly
evident during the period following the end of Campaign
6. This campaign was designed such that upon its
conclusion the participants were provided individualized
information concerning results from the campaign period(Masuda et al., 2018). It is possible that the effects ofpresenting this information were thus delayed until after
the campaign.
As mentioned earlier, repeated requests to the same
study subjects to save energy showed a trend of
progressively weaker responses to such appeals. Yet, wefound that electricity consumption was lower during
campaigns compared to the periods before and after eachcampaign.
3.2 Trends in Electricity Consumption over Time
During the energy-saving campaigns, households
were provided with various information to promote
behavioral changes. One example of such information isadvice on replacing electricity-consuming household
appliances with energy-efficient ones. With the purposeof measuring the rate of application of information in thedaily life of householders, changes in electricity
consumption after the introduction of the monitoring
system were analyzed.
The frequency distributions of change rates of eachhousehold at each of the three air temperature values: 5°C(winter), 15°C (spring and fall), and 25°C (summer) arepresented in Fig. 6. Here, negative figures in change rates
indicate increases in electricity use. According to thefigure, the most frequent change rates were in the range
of 0% –5% decreases for winter, at 5°C, for all FY

Fig. 5 Results of longitudinal evaluation.

Longitudinal evaluation of energy-saving effects and its implication using electricity monitoring data

213

Fig. 8 Correlation between the two energy-saving effects.

Fig. 6 Frequency distributions of change rates.

Fig. 7 Average change rates in electricity consumption.

periods. The mode value for spring and fall, at 15°C,
changed from a 0% –5% increase for FY 2014–2015 to0% –5% decrease for FY2015-2016, which again shiftedto a 5% –10% increase in FY2016-2017. As for summer,
mode values showed divergent results in the early period
after system introduction (FYs 2014–2015), i.e., a0% –5% decline followed by a 5% –10% increase. Morerecent trends in FY2015-2016, and FY2016-2017 showeven more divergence among the households, where themode is within the 0% –5% increase range while thenumber of households with decreasing trends have also
increased.
The average electricity consumption change overtime for all households at the same air temperature of
5°C, 15°C, or 25°C, presented in Fig. 7, indicates thesetrends more clearly. Considering the comparably longer
period from FY2015 through FY2017, average change
rates showed a slight increase in electricity use by
1.2% –2.3% for all three temperatures. The trends inchange overtime varied by season, however. In winter,they started with a decline in the FY2014 and FY2015
periods, which turned into an increase subsequently. That
increase was especially large in the more recent period
from FY2016 to FY2017. The trends in spring and fall
were opposite: starting with a large increase in the
beginning which then turned into a gradual decrease. The
trend in summer indicated mixed results: an initial large
increase turning into a decrease, as in spring and fall, but
it shifted to an increase again more recently in the
FY2016-FY2017 period, yet the increase is somewhat
restrained. An overall increasing trend on average, withparticularly large increases during winters, suggests thatthe average values were impacted by households thatshowed large increases, despite there being a number ofhouseholds showing a decreasing trend, as discussed
regarding Fig. 6 above.
3.3 Correlation between the Two Energy-saving
Effects
From these results, the relationships between
previous energy-saving campaigns and changes in overtime electricity consumption rates were explored by aseries of correlation analyses. For instance, in Fig. 8, thevertical axis represents change over time rates (at 5°C)
from FY 2015 to FY 2017, while the horizontal axis
shows average energy-saving effects for each household
during campaigns 1 through 6. This figure suggests thatthe energy-saving effects (reduction rates) of campaigns
and electricity consumption change rates over time were
mostly uncorrelated. The same was observed for othertemperatures (at 15 °C and 25 °C), or for individual
analyses by each campaign. In other words, the effects ofeach energy-saving campaign did not translate to steady,long-term energy-saving effects.
The probable reasons for this are as follows.
Reduction effects from energy-saving campaigns
appeared to be due, in a relatively pure fashion, tochanges (reductions) arising from engagements during
the campaign period. Meanwhile, rates of changes overtime are likely to have been more strongly impacted byfactors other than purely energy-saving effects, such as,changes in household member configurations. This is214

A. OTSUKA et al.

Table 3 Factors influencing rate of change over time.
Name of variables

Standardized β
5℃

Family size
Family size (increased)
Family size (decreased)
No. of persons at home (weekday)
No. of persons at home (weekend)
Residence area
Age (at application)Age of family (infants)
Age of family (juniors)
Age of family (>65)
Occupation
Family income
Year built
Solar panel installed
All electric
Air-conditioning typeNo. of air conditioners
Heating typeNo. of heating units
No. of elec. appliances
No. of lights
Active in energy-saving behavior
Life satisfaction
Prospect for Shinchi Town
Adjusted R2

15℃

25℃

0.328

-0.205-0.260
0.356
0.320
0.281

0.383

-0.729

0.366
0.507

-0.212-0.342-0.4520.396-0.299
0.432

0.171

0.515

p < 1%, 5%, 10%; Step-wise regression analysis was used: empty cells in
the Table therefore mean that these were not identified as influencing
factors although these were also included in the analysis. There are no VIF
figures, suggesting multicollinearity among the variables.

indeed indicated in the results of a series of step-wise
regression analyses (Table 3). Here, the rates of changeover FY2015 to FY2017 at each temperature were usedas dependent variables, whereas households’ demographic
and housing attributes, partly presented in Fig. 2, wereused as independent variables. People’s subjective
cognition on energy issues as well as general well-being
obtained from a survey conducted during one of theenergy-saving campaigns, namely Campaign 4, were also
included in the analysis (though limiting the sample size,i.e., 35). Cognition of energy-issues, such as societal
responsibility and efficacy, is known to have certain
influences on energy-saving behaviors and thus energy
consumption (e.g., Otsuka et al., 2017), so it wasconsidered important to include similar cognitive
variables as this study directly addresses the effect ofenergy-saving campaigns the purpose of which is tochange people’s cognition and/or awareness andbehaviors. The variable “actively taking energy-saving
behavior” was used here to represent such conventional
cognition, whereas variables “life satisfaction” and“prospects for Shinchi Town” were something unique but
important for this study, which looks at redevelopmentfrom the aftermath of the Earthquake.
The results show that changes (increase/decrease) infamily size as well as age of the house, normally known

to be factors affecting energy consumption, are found tobe have an influence with statistical significance. The
number of electric appliances was associated with anegative influence (i.e., decrease in change rate) – theopposite of what is normally expected. One of the reasonsfor this could be that it gave opportunity for people toreplace these appliances with more efficient ones
according to the energy advice given during the energysaving campaigns. Although there were no data available
which could be used as a variable indicating practices in
replacing appliances to back-up this reasoning, the factthat this variable was included as an influencing factor
for spring and fall (at 15°C), when the increased sample
number (n=68) was used without the inclusion of
cognitive variables, may reasonably imply such a
possibility, especially given the overall trend in thedecreasing change rate at 15°C as discussed earlier.
It is also noteworthy that cognitive variables such as“actively conducting energy-saving behavior” and“having good prospects for town redevelopment in the
future” contribute to energy-saving. This is especiallytrue for winter, when the cognitive variable “actively
taking energy-saving behavior” shows the strongest
influence on the change (decrease, in this case) rate. The
fact that the age of the house is the second mostinfluential factor (in the increasing direction) in winterhighlights the need for promoting better insulation. This
is in line with the current energy-saving promotion policy
in Japan, and will be an important agenda to raise, given
the location of Shinchi in the Tohoku region with a cold
winter climate. Implementation of such measures forexisting houses poses a challenge, however, as it issomething not simply achievable through behavioral
changes in everyday life.
On the other hand, “installation of solar panels” hasbeen one of the factors pointing toward increased
electricity consumption over time in summer. The same
trend for having solar panels was observed in otherseasons, at both 5°C and 15°C, when the analysis was
conducted with a larger sample number (n=68) without
cognitive variables. The link between the increase inelectricity consumption and having solar panels per se is
not necessarily a problem as it represents a low-carbon
energy source, especially if the solar-powered electricity
is used towards enabling net-zero electricity in a house. If
the increase in electricity consumption is indeed related
to the in-situ (i.e., self-produced) use of solar power, it
then makes the challenge of effective S/D management a
reality. There is a need to investigate this relationshipfurther with increased samples, though it was not possible
in this study due to limited availability of data. It is also
noted that a new awareness arising for DR in terms ofclimatic fluctuation is necessary. Again, that “actively
taking energy-saving behavior” was also associated withan increase, rather than decrease, in electricity
consumption in summer poses a challenge, but the fact

Longitudinal evaluation of energy-saving effects and its implication using electricity monitoring datathat people’s attachment to locality as shown in theprospects for the future of the town contribute to anenergy-saving effect gives hope in terms of community
energy-management systems.

4. Conclusion
This study used electricity monitoring data for
independent households within Shinchi Town, Fukushima
Prefecture to investigate: 1) energy-(electricity)-saving
effects during energy-saving campaigns and, 2) long-termchanges in electrical power consumption, in an aim tolearn from them and assess the implications of S/Dbalancing effects in the future. The conclusions drawn
from the results are summarized as follows.
The energy-saving effects due to the implementationof each energy-saving campaign showed declining trends
over time. It appears, however, that electricity
consumption rates were restrained during each campaign
implementation period.
With regard to changes related to over-timeelectricity consumption tendencies beyond the period of
the energy-saving campaigns, while there was anincreasing tendency overall, there were also numerous
households that showed a declining tendency. This wasapparent when considering changes during each fiscalyear period and changes within individual households.
There were also households that showed restraint in theirincrease of electricity consumption. Notably, however,
the effects of energy-saving campaigns were limited andmostly did not continue beyond the campaign period.
Hence, it follows that measures are required, especiallyregarding seasonally increased electricity consumption
trends, to promote and stabilize further energy savings.
To summarize the study’s findings, considering thatthe effects produced during the energy-saving campaigns
did not translate into long-term effects, it can be said thateven stronger engagement and educational/awarenessraising efforts are needed. In doing so, new issues such asbuilding insulation, noted to be quite influential during
wintertime, may need to be addressed. Moreover, given
the fact that households responded to repeated appeals forenergy savings, it sheds light on future prospects thatrequests made to consumers via processes such as
demand-response (DR), that are likely to be long-term,
continuous and more frequent within community energy
management systems than in the energy-saving
campaigns used as the case study in this paper will also
be responded to by the targeted consumers, implying that
consumers are ready to make changes when so requested.
This gives further positive prospects that the results of theenergy-saving campaigns would have been more positive
if evaluation by randomized control tests had been
available.
With regard to further work in the target area of thepresent study, this study was limited to covering only the215

electricity usage in the households. Given the climaticconditions, where heating is often conducted using energy
sources other than electricity as well as warmer summer
temperatures in the most recent years, it would be
worthwhile to study people’s behavioral responses tosimilar requests regarding heating as well as in hotter
summers.
Further, similarly to energy-saving campaigns
conducted in the past, it is also important to investigate
and verify the people’s responses and behavioral changes
in settings whereby actual demand-and-supply balanceadjustment requests, although virtual, are given thatincorporate considerations for renewable energy usage.
References
Agency for Natural Resources and Energy (2020) What is Virtual
Power Plant : Demand Response. Retrieved from https://www.
enecho.meti.go.jp/category/saving_and_new/advanced_systems/
vpp_dr/meaning.html (Accessed 25 August 2020)
Bhushan, N., Steg, L. and Albers, C. (2018) Studying the effects ofintervention programmes on household energy saving behaviours
using graphical causal models. Energy Research & Social
Science, 45, 75–80. Retrieved from https://doi.org/10.1016/
j.erss.2018.07.027
City of Yokohama (2017) Yokohama Smart City Project. Retrieved
from https://www.city.yokohama.lg.jp/kurashi/machizukurikankyo/ondanka/etc/yscp/yscp02.files/0003_20190312.pdf(Accessed 25 August 2020)
Global Warming Prevention Headquarters (2020) Submission ofJapan’s Nationally Determined Contribution. Retrieved fromhttps://www.env.go.jp/press/files/jp/113664.pdf (Accessed 30
June 2020)
Japan Metrological Agency (2017) Past Climatic Data. Retrieved
from http://www.data.jma.go.jp/obd/stats/etrn/index.php(Accessed 8 October 2017)
Masuda, T., Otsuka, A., Nakamura, S., Hirano, Y., Gomi, K., Fujita,
T. and Narumi, D. (2018) Energy-saving effects by information
provision of consumers’ choice. Report on Meeting of AIJ Kanto
Branch 2017, 191–194, Architectural Institute of Japan, Tokyo.
Nakamura, S., Hirano, Y. and Fujita, T. (2017) Results of research
by energy-saving campaign in Shinchi Town. Report on the 6 th
Meeting of Tohoku Branch of Air-conditioning and Hygiene
Association (presentation material) [obtained from the author].
New Energy Promotion Council (2014) Report on Next Generation
Energy and Social System Demonstration project: Kitakyusyu.
Retrieved from http://www.nepc.or.jp/topics/pdf/150330/150330_67.pdf (Accessed 25 August 2020)
Otsuka, A., Hirano, Y. and Narumi, D. (2017) People’s values andenergy cognition behind energy-saving behavior. Journalof Environmental Engineering (Japan), 82(739), 811–820,
Architectural Institute of Japan, Tokyo.
Shimoda, Y., Yamaguchi, Y., Iwafune, Y., Hidaka, K., Meier, A.,
Yagita, Y., Kawamoto, H. and Nishikiori. S. (2020) Energy
demand science for a decarbonized society in the context of theresidential sector. Renewable and Sustainable Energy Reviews,
132. Retrieved from https://doi.org/10.1016/j.rser.2020.110051
Shinchi Town (2018) Press Release on Establishment of Shinchi
Smart Energy Company. Retrieved from http://www.shinchitown.
jp/soshiki/2/smartenergyseturitu.html (Accessed 20 May 2018)
Shiraki, H., Tanaka, H. and Nakamura, S. (2016) Effect of energy
saving campaign in Fukushima. Booklet Series of Environmental
Emergency Research, 6–10, National Institute for Environmental

216

A. OTSUKA et al.

Studies, Tsukuba.
Vine, E., Sullivan, E., Lutzenhiser, L., Blumstein, C. and Miller, B.
(2014) Experimentation and the evaluation of energy efficiency
programs. Energy Efficiency, 7, 627–640. Retrieved fromhttps://doi.org/10.1007/s12053-013-9244-4

Ayami OTSUKA
Ayami Otsuka is an assistant professor at theFaculty of Social Sciences, Waseda University.
Holding a Ph.D. in environmental studies, she
served as a visiting research fellow at the National
Institute for Environmental Studies from October
2017 to March 2019. Her main research interest
lies in integrating energy research and social
sciences, with a particular focus on citizens’ roles in realizing a sustainable
society.

Yujiro HIRANO
Yujiro Hirano is a senior researcher at theRegional Environmental Renovation Section of
the National Institute for Environmental Studies,
Fukushima Branch. He holds a doctorate ofengineering and has long been engaged in researchon low-carbon lifestyles remote sensing of urban
surfaces, urban heat island countermeasures andurban energy system. His recent research interests are in modeling andanalysis of regional energy use for environmental renovation plans.

Shogo NAKAMURA
Shogo Nakamura is a researcher in the Regional
Environmental Renovation Section of the
National Institute for Environmental Studies,
Fukushima Branch.
He holds a Ph.D. inagricultural science. He has played a central rolein developing the energy monitoring systems in
Shinchi as well as many other areas in
Fukushima. Recently his interests have led him to participate in theEnvironment-Economy-Society Integration Research program.

Tsuyoshi FUJITA
Tsuyoshi Fujita is a principal researcher at theCenter for Social and Environmental Systems
Research, National Institute for Environmental
Studies. He is concurrently a professor in the
Department of Urban Engineering, University ofTokyo. His main fields of interest are in regional
SDGs, regional circular and ecological systems,
and urban industrial symbiosis. He is also a registered professional engineer
in the field of Civil Engineering and Architecture.

Daisuke NARUMI
Daisuke Narumi is a professor at Yokohama
National University. He received his PhD inengineering from Osaka University in 2003. His
doctoral thesis was titled, "Study on the Planning
Methods of Urban and Architectural Environment
in Harmony with Surrounding Climate
Characteristics." His specialty is urban andarchitectural environmental engineering, and he has been working inresearch on a wide range of subjects, from energy consumption in cities andbuildings to analysis of the environmental impacts associated with energy
consumption. His major awards include the Encouragement Prize from AIJ
and Paper Award from the Society of Heating, Air-Conditioning andSanitary Engineers of Japan.
(Received 23 September 2020, Accepted 25 December 2020)