Field Trial toward Low-carbon Society inIndonesia

 Kenji Uchida  Minoru Fujii  Shuichi Ashina  Seiya Maki
 Toshinori Hanya  Kengo Aizawa  Tsuyoshi Fujita
Developing economies are rapidly pursuing urbanization and industrialization, and the consumption of fossil-fuel-based energy is increasing. Thus, the greenhouse gas emissionsof these countries are also expected to increase. Fujitsu is collaborating with the National
Institute for Environmental Studies of Japan, Bogor Agricultural University, and the Bandung
Institute of Technology of Indonesia, working toward the application of continuous evaluationand field trial of energy-saving measures to model urban/industrial areas in Indonesia. Thisinitiative is unique in that it leverages the technologies and expertise of Fuji Electric Co., Ltd.,a company that has a high reputation in Indonesia for data measurement, as well as a local
maintenance team. This initiative is also noteworthy for the use of big data analysis and highly
reliable energy-saving assessment technology provided by Fujitsu Semiconductor Ltd. These
strengths make it possible to select the energy-saving measures best suited for the culture andcustoms of Indonesia, and to quantify the effectiveness of evaluations using information andcommunications technology (ICT). We have high hopes that this initiative will lead to the application of large-scale energy-saving measures to urban areas and/or industrial parks as aninspection and field trial. It could thus make significant contributions to achieving a low-carbon society in developing countries. This paper discusses the role of ICT in this field trial.

1. Introduction
The Republic of Indonesia, which is an emerging
Asian country, has the world’s fourth largest population of 252 million as of 2014, and has been steadily
achieving a high level of economic growth of around
5% per annum in recent years.1) However, owing to
the increase in domestic energy consumption accompanying economic growth, domestic energy demandis expected to exceed supply in 2020, and Indonesia
faces the urgent task of changing over to an industrial
and social structure with high economic efficiency andenvironmental efficiency.2) In transforming the energy
systems of industry and society, energy-saving measures and the introduction of renewable energy will be
required, necessitating the quantitative and continuous
monitoring of actual energy consumption conditions
to obtain the data required for analysis, assessment,
forecasting, and decision-making. To this end, theGovernment of Indonesia decided to measure andgrasp energy consumption using state-of-the-art digitaltechnology in collaboration with universities, research
62organizations, related ministries, and municipalities
in the country. Fujitsu and the National Institute forEnvironmental Studies of Japan are participating in a
research project to build a system to measure energy
consumption (hereafter, an energy monitoring system)
in Bogor city, Indonesia, in collaboration with suchbodies as Bogor Agricultural University and the Bogor
Municipal Government, and the system has been in fulloperation since March 2015.
This paper describes the development of anenergy monitoring system using Fujitsu’s digital technology, for use especially in developing countries. The
establishing of following items was required in the research project.
1) The technologies of the energy monitoring system
and network consumption. (Technical elements:
Realization and implementation of Internet ofThings [IoT] and energy consumption visualization function)2) A process for the utilization of the measured databy Indonesian and Japanese affiliated entities
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K. Uchida et al.: Field Trial toward Low-carbon Society in Indonesia

3)

on a collaborative basis. (Technical elements:
Establishment of big data analysis method andintroduction of energy-saving diagnosis)
A method that can be applied deployment overa wide area of the above process flow basedon implementation and verification in Bogor.
(Technical element: Establishment of an estimation method that extrapolates measurement
results and standardizes them)

2. Realization of IoT and energy
consumption visualization function
Figure 1 (a) shows the concept of the energy
monitoring system, and Figure 1 (b) shows the work
flow for analyzing and using the measured data.
The measurement targets of energy consumption
are electricity and fossil fuel such as gas and heavy oil.
In view of the fact that Indonesia has a tropical climate
with an average temperature of 26°C or higher throughout the year, air conditioning, OA equipment, andhome appliances are assumed to account for the majorpart of energy consumption, and energy consumption
measurement is given priority for such equipment overother types of equipment.
Indonesia has a variety of electric facilities, andthus it is necessary to support two types of power
distribution boards for measurement, namely thesingle-phase 2-wire 220 V type for home use, and thethree-phase 4-wire 380 V type for industrial use. A
measurement system designed using products and solutions3) from Fuji Electric Co., Ltd. The company has a
track record of data measurement at Indonesian plants
as well as a maintenance system. The local operationof the measurement system is being implemented
with the support of the company. The measured datais aggregated over Fujitsu’s cloud server in Japan via
a LAN-to-LAN VPN (virtual private network) over theInternet. Researchers, local governments, citizens, andothers involved in this project can check the energy
consumption situation in real time by connecting tothis cloud server through a smartphone or tablet.

3. Establishment of big data analysis
methodAnalyses of the daily measurement data are carried out using statistical and engineering methods.
Figure 2 shows the analysis flow.
FUJITSU Sci. Tech. J., Vol. 53, No. 3 (April 2017)

3.1 Statistical methods
The statistical methods consist in the use of two
methods of analysis, “time series analysis” for grasping periodicity and seasonality in energy consumption
data, and “regression analysis” for estimating the factors of change in energy consumption and creating
models based on these factors.
1) Time series analysis
A time series whose statistical properties (average, variance) are independent of time is called astationary time series, and a time series whose statistical properties are dependent on time is calleda non-stationary times series. The autoregressive(AR) model and moving average (MA) model can beapplied to stationary time series, and the autoregressive moving average (ARMA) model, which combines
the two above models, is described with the following
equation.4)
p

q

i=1

i=1

Xt = εt + ∑ ψi Xt‒i + ∑ θi εt‒iThis model combines an AR model of order p andan MA model of order q, where X is power consumption, ψ and θ are parameters, and εt is a residual error
term that constitutes a random number term that follows a normal distribution with zero mean.
The autoregressive integrated moving average(ARIMA) model, which approximates a stationary
time series by removing differences with previous values because the previous values affect the value at agiven point in time, is applied to the analysis of nonstationary time series. In this paper, we decided tocarry out an analysis using a seasonal autoregressive
integrated moving average (SARIMA) model that adds
a seasonal variation component to ARIMA.
Figure 3 shows the results of SARIMA analysisof the power consumption measurement results foroffices in Bogor, Indonesia. From the top, the results
are classified as measurement data, seasonality, periodicity, and residual error, as obtained for power
consumption in 1-hour units from January 1, 2015 toJune 30, 2016. It should be noted that measurement
started on January 19, 2015. With regard to seasonality, Indonesia has a tropical climate with an averagetemperature of 26°C or higher throughout the year, soenergy consumption tends to be constant throughout
the year at around 1.4 kWh. The decrease in power
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K. Uchida et al.: Field Trial toward Low-carbon Society in Indonesia

Indonesian city/industry
Malls
Plants

Offices

Homes

Measurement ofenergy consumption

Support forenergy-saving measures

Measurement of energy
consumption in Indonesia

Big data analysis atdata center in Japan

Data transfer

F-MPC, Green Terminal
manufactured by
Fuji Electric Co., Ltd.

Energy diagnosis and supportof measures in Indonesia

Visualization

Fujitsu data center

Local support forvisualization in real time(a) Energy monitoring system concept

Big data analysis
at data center in Japan

Facilities in Indonesia

1) Datameasurement
Support for
maintenance andoperation of
measuring
instruments

KPI2) Time series analysis
Quantitativeevaluation

KPI3) Regression analysis

Factor analysis
Parameter input

Modelevaluation

Qualitativeevaluation

4) Neural network

Implementationofenergy-saving
measures

Use of know-how ofengineers and researchers

KPI

Machine learning
using past data

Gaining ofawareness
5) Thermal load calculation
Energy-saving
performance evaluation

Input of building
structure andequipment
information

6)
Planningof energysaving
measures

Optimum energy-saving measures
best suited to local culture and customs
KPI: Key performance indicator(b) Work flow

Figure 1
Continuous evaluation and field trial of the effect of energy-saving measures.

consumption in July 2015 is due to the influence ofa long vacation peculiar to the Muslim faith called
Lebaran, which is celebrated after Ramadan (fasting
month). In terms of periodicity, a constant cycle with64

fluctuation range of about 3.0 kWh tends to be seen.
This result indicates that model evaluation using only
seasonality and periodicity is not possible owing to thelarge influence of the residual error term because the
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K. Uchida et al.: Field Trial toward Low-carbon Society in Indonesia

KPI
Phillips-Perron
test P value ≤ 0.01

1) Datameasurement

KPI

Quantitative evaluation
2) Time series analysis

Yes

Breusch-Pagan
test P value ≤ 0.01

KPI

No

Yes

Durbin-Watson
Ratio ≈ 2

Yes

No

Qualitative evaluation

3) Regression analysis
Unequalerror distribution

No3) Regression analysis with corrected
T value and significance from
Newey-West standard errors
Serialcorrelation
3) Regression analysis with corrected
T value and significance fromWhite standard errors
4) Neural network

KPI
Load factor > 60%

Yes

Energy saving performance evaluation
5) Thermal load calculation

Figure 2
Analysis flow.

Power consumption (kWh)Measurement 7.0
6.0
data

Power consumption (kWh)Seasonality

Power consumption (kWh)Periodicity

Power consumption (kWh)Residual error

5.0
4.0
3.0
2.0
1.0
0.0
2.5
2.0
1.5
1.0
0.5

Power consumption decrease during Lebaran period

0.0
–0.5
2.5
2.0
1.5
1.0
0.5
0.0
–0.5
–1.0
–1.5
4.0
3.0
2.0
1.0
0.0
–1.0
–2.0
–3.0
–4.0

January

April

July
2015

OctoberJanuary

April
2016

Figure 3
Evaluation with SARIMA.

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K. Uchida et al.: Field Trial toward Low-carbon Society in Indonesia

P value is 0.01 in the Phillips-Perron test. The factorsof change of seasonality, periodicity, and residual error
are described in the next section.
2) Regression analysis
The factors of the seasonality and periodicity ofenergy consumption described in the previous section
were analyzed using regression analysis. In this section, we describe the results of applying a multiple
regression analysis model, which is a linear regression
analysis that describes multiple factors of change.
The multiple regression analysis model, which isused for listing the factors of change from the on-sitesituation and grasping the influence of each factor, canbe described by the following equation.5)my = β0 + ∑ βi xi + ε
i=1This is a multiple regression model of order m,where y is the power consumption, x is the activity
time, β is a parameter, and ε is a residual error term.
In this project, of the measurement points of the data
used in time series analysis, for the quantitative data,
we selected the temperature as a factor of change, andfor the qualitative data, we selected the activity timeof facilities (9:00 to 18:00 on business days) as a factor of change, using the Akaike’s Information Criteria(AIC).4), 5) For the temperature data, we used the 1-hourinterval values for Bogor provided by the Open Weather

Map.
Table 1 shows the analysis results for the degreeof contribution of each multiple regression analysis
factor according to the Newey-West standard errors.
Figure 4 shows the results predicted using the model
based on the above for the one-month period fromMay 17, 2016 to June 16, 2016. The vertical axis of
the graph represents the electric power consumption(kWh). When fitting constants by the multiple regression analysis model with the range of temperaturesfrom 0°C to 50°C as normal values, power consumption
is described as follows.
Power consumption (kWh)= 2.57 × activity coefficient (0 or 1)+ 0.09 × temperature (°C) − 1.88
From the results in Table 1, the facts that the absolute value of value T is 2 or greater and value P is 0.01
or lower indicate that periodicity of power consumption
depends on human activity and seasonality depends
on temperature as factors of change. However, analysis
applying the flow shown in Figure 2, using the BreuschPagan test (P value: 2.2×10−16) and Durbin-Watson test
(DW ratio: 0.93), showed unequal error distribution
and serial correlation. The fact that coefficient of determination R2 is 0.72, thus smaller than 1, shows thatthe variables of human activity (periodicity) and temperature (seasonality) on their own are insufficient formodel evaluation. In the future, more accurate model

Table 1
Multiple regression analysis model (coefficient of determination R2 = 0.72).
β valueStandard error

T value

P value

Intercept

−1.88

0.51

−3.67

2.6 × 10−4

Activity time

2.57

0.16

16.33

< 2.2 × 10−16

Temperature

0.09

0.02

4.69

3.3 × 10−6

Power consumption (kWh)Factor of change

6

Measurement value

5

Predicted value

4
3
2
1
0

May 17

May 23

May 30June 6

June 13

June 16

Day (May 17, 2016 - June 16, 2016)

Figure 4
Predictive model evaluation.
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K. Uchida et al.: Field Trial toward Low-carbon Society in Indonesia

evaluation is expected through measurement of qualitative data (residual error) of social elements such as
the number of people staying in the office.
3) Machine learning technology
Machine learning is a method to predict the
future from past time series data. In this section, we
describe the results of applying machine learning using
a recursive neural network. A general neural network is
described by the following equation.6)()

z = f ∑ωi xi(t) ‒ h , f (v) =i=1function that mimics the behavior of neurons. In thisproject, long short-term memory (LSTM)7) was adopted
for the storage of data input in the past (Figure 5).
Figure 5 (a) shows the concept of a recursive
neural network using the LSTM created in this project.
This is a model that predicts power consumption for thefollowing week based on the power consumption anddate and time data of the past two weeks, assumingthat the date and time data (day of the week, humanactivity, etc.) are known.
Learning was conducted using the power consumption between the data measurement points usedin the time series analysis: January 19, 2015 and May
31, 2016. Figure 5 (b) shows an example in which

1
1+e‒v

where z is the output of the neural network, x is
the input, ω is a parameter, and f(v) is the activation

Past (2 weeks)

Present

Predicted value

Future (1 week)+1 hour+2 hours+3 hours+168 hours

Power
data

Power
data

Power
data

Power
data

Power
data

Power
data

Power
data

FC

FC

FC

FC

FC

FC

FC

LSTM

LSTM

LSTM

LSTM

LSTM

Date and time
data

Date and time
data

Date and time
data

Date and time
data

Date and time
data

Date and time
data

Date and time
data

Power
data

Power
data

Power
data

Power
data

Power
data

Power
data

Power
data

-336 hours-2 hours-1 hour+1 hour+2 hours+167 hours

LSTM

...

Present

LSTM...

LSTM: Long short-term memory
FC: Fully connected layer

Power consumption (kWh)(a) Prediction concept of future power demand
6

Measurement value

5 Predicted value
4

Predicted value

3
2
1
0

June 19

June 20

June 21

June 22

June 23

June 24

June 25

Day (June 19, 2016 - June 25, 2016)(b) Predictive evaluation of future power demand

Figure 5
Machine learning using a recursive neural network.
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K. Uchida et al.: Field Trial toward Low-carbon Society in Indonesia

data for the two weeks from June 5 to June 18, 2016
are input and the week from June 19 is predicted. The
vertical axis of the graph represents the power consumption per hour. Looking at the prediction results,
we see that although regular changes are captured between measured values and predicted values, there aredifferences in demand peaks. These are the differences
between past behavioral patterns referenced for prediction and actual behavior. They show how awarenesscan be achieved among users by showing them in realtime the changes due to forgetting to turn lights off
and to equipment abnormality, and the effects of theimplementation of energy-saving measures.

QL: Amount of heat required to lower indoor humidity(latent heat) (W)
QIN: Amount of heat from indoors (W)QEX: Amount of heat from outdoors (W)EAC: Power consumption for cooling by air-conditioning(W)
COP: Air-conditioning performance value (The higher
the value, the higher the efficiency)
Figure 6 shows these relationships as a concept.
In this project, first, under the premise that when
the air conditioner load factor is high, it is close to the
COP catalogs value9), cooling load QAC was obtained
from the power consumption of the air conditioner
and the COP catalog value. Then, from the power consumption of the household electrical appliances in theroom and the human body load, amount of heat fromindoors QIN was obtained, and the amount of heat fromoutdoors QEX was evaluated. For the office where thedata shown in Figure 3 was measured, thermal load
calculation was performed for 1 hour from 14:00 to
15:00, and Table 2 compares the thermal load calculation results and power consumption ratio. The heatamount was taken to be the value divided by the total
floor area, the air temperature 31.6°C, and the humidity 52%. Further, the load factor of the air conditioning
indicates the ratio between the power consumption of

3.2 Engineering methods
As the engineering method, quantitative analysisof the heat balance of buildings was carried out basedon the measured power data. The heat balance duringcooling can be described by the following equation.8)

QAC = QS + QL = QIN + QEX
EAC =

QAC
COP

QAC: Cooling load (W)QS: Amount of heat required to lower indoor temperature (sensible heat) (W)

Flow-through load

QEX

QS Cooling sensible heat load
(Amount of heat required to lower
indoor temperature)

QL

QEX

Cooling latent heat load

(Amount of heat required to lower
indoor humidity)

Indoor unit of air
conditioner

QIN
Lighting load

Human body load

QIN

QIN

Outdoor unit of
air conditioner
Cooling load

QAC

Outside air load

QEX

QIN
Equipment heat load

QEX

Solar radiation load

QEX

Flow-through load

QEX

Flow-through load

QEX

Draft load

QEX

Figure 6
Conceptual diagram of cooling load.
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K. Uchida et al.: Field Trial toward Low-carbon Society in Indonesia

Table 2
Thermal load calculation (97% air conditioning load factor) and power consumption ratio.
Load
Cooling load QAC
Amount of heat fromindoors QIN
Amount of heat fromoutdoors QEXHeating value(W/m2)

Ratio (%)

55

Ratio (%)

Air conditioner

87

8

15

Electric outlet

10

47

85

Lighting3

the catalog value of the air conditioner and the actual
power consumption.
From the results, the load factor of the air conditioning being close to 100%, it is understood that
inverter control is not effective because the air conditioner always operates at the maximum cooling load.
Moreover, the ratio of power consumption for air conditioning to total power consumption is high, at 87%.
Considering that the amount of heat from outdoors is
as high as 85% or more, reduction in power consumption is expected to be achievable by switching to a high
COP air conditioner and increasing the thermal insulation and air tightness of the building itself. The results
show that the energy-saving performance (heat insulation, air tightness) of buildings can be evaluated alongwith the energy-saving performance (COP) of home
appliances.

4. Introduction of energy-saving
diagnosis
The energy-saving diagnosis carried out at an
office in addition to data measurement is described
below. The thermal environment of the office as surveyed by thermography is shown in Figure 7. Shownon the left side of Figure 7 are photos of the office,and on the right side are photos taken by a thermographic camera. The diagnosis was carried out by
Fujitsu Semiconductor Ltd., which has plant facilities
know-how. Although the window of the office can be
opened and closed, it can be seen that there is a gap
even when it is closed as shown in the above figure,and outside air heat penetrates into the room. The
figure below shows how external heat penetrates fromthe ceiling. In this office, the doors of unused rooms
are left open, letting warm air flow in from non-air
conditioned spaces and causing unnecessary air conditioning load, resulting in insufficient cooling capacity of
the air conditioning equipment.
FUJITSU Sci. Tech. J., Vol. 53, No. 3 (April 2017)

Equipment

Air outlet: 20°C

Suction port: 30°C

Air conditioner

Window gap: 34°C

Ceiling: 35°C

Figure 7
State of energy loss at office.

Reducing power consumption requires the installation of heat insulation on the ceiling and roof, lightshielding curtains and blinds, and double windows toprevent heat from entering from the outside, and taking measures to ensure that the doors of unused rooms
are closed to prevent cooling of spaces that are notneeded. Implementing these measures and thereby
being able to change the set temperature of the air conditioners from 25°C to 27°C is expected to reduce power
consumption by about 20%.10) On top of that, further
reduction of power consumption can be expected byreplacing air conditioners with high-performance ones.
By implementing energy-saving measures appropriate to the climate of Indonesia described in thispaper and continuously implementing the work flow
shown in Figure 1 (b), it is possible to quantitatively
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K. Uchida et al.: Field Trial toward Low-carbon Society in Indonesia

The method introduced so far is based on measurement data of a specific facility (point), but in order
to disseminate energy-saving measures across a wholecountry or region, it is necessary to also expand its application to a wider area such as a city or an industrial
park. In this paper, we describe an initiative that utilizes open data to estimate power consumption acrossa wider area. Figure 8 shows the positioning of power
consumption in Bogor. It shows the features of electric
power demand in Bogor, as obtained from the compilation of information published by the Statistics Bureau11)
and information from power companies.12)
Figure 8 (a) shows a comparison of average annual power consumption per capita in the
country of Indonesia, Java island, and the city ofBogor. In 2014, Bogor’s population was 1.01 million11), and average per capita power consumption
was nearly the same value as that for Indonesia asa whole. As can be seen from the density distribution of the distribution transformers per city block in
Figure 8 (b), the density is higher in the central city blockareas. Looking at the relationship between population
density and power density for each city block of Bogor[Figure 8 (c)], one can see that power density is morea function of facility location than population density.
This paper has presented data and analysis
results, taking an office as an example. It has demonstrated the possibility of applying this approach toenergy-saving measures across a wider area by selecting and analyzing model facilities from commercial
facilities such as eating and drinking, industrial facilities such as plants, and entertainment facilities such as
hotels and shopping malls, and applying the results toblock districts with similar characteristics and carryingout continuous estimation.

6. Future initiatives
Going forward, using satellite images and deeplearning technology,13) we plan to classify woods, rivers,
roads, cars, residences, commercial facilities, industrial
facilities, offices, and so on, based on urban images,
70

Average power consumption (kWh/person)

5. Establishment of estimation method
for extrapolation to a whole area

and obtain data on the number of facilities and theirsurface area to further improve the accuracy of estimation of energy demand per city block area. We will also
expand application of the data collection system andanalytical method described in this paper horizontallyto a larger scale, and based on the environmental parameters of temperature, humidity, and wind speed,
and the location conditions for each city block, selectthe optimum energy-saving measures for each city and

1,200
1,000
800

Government

600

Offices & stores

400

Industry

200

Residential

0Indonesia
as a wholeJava island

Bogor(a) Power consumption in 2014

Low power density

4,000 — 5,000 kVA/km2
3,000 — 4,000 kVA/km2
2,000 — 3,000 kVA/km2
1,000 — 2,000 kVA/km2
0 — 1,000 kVA/km2

High power density

1 km
Low power density(b) Distribution of power density of distribution transformers
in BogorPopulation density (people/km2)

grasp the actual state of energy consumption, and todemonstrate and verify continuously the energy-saving
effect that is achieved.

100,000
10,000
1,000
100
10
1

Homes

0

Offices
Stores

1,000

Industry
Hotels2,000

Shopping
malls

3,000

Stations

4,000

5,000

Power density of distribution transformers (kVA/km )2(c) Power density distribution of distribution transformers
per block of Bogor

Figure 8
Characteristics of electricity consumption of Bogor.
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K. Uchida et al.: Field Trial toward Low-carbon Society in Indonesia

consider the best path for social implementation.

8)

7. Conclusion
This paper introduced a work flow that combines
data analysis and other techniques for the diagnosis and implementation of energy-saving measures,using energy consumption measurement data as thestarting point. This content is based on a case study
conducted in collaboration with Fujitsu as part of a
research project carried out by the Center for Socialand Environmental Systems Research of the National
Institute for Environmental Studies of Japan,note )1)forthe Research and Information Office, Policy Planning
Division, Global Environment Bureau, Ministry of theEnvironment of Japan.
In the future, we plan to contribute to the realization of a low-carbon society in developing countries
by expanding the scope of activities of this field trial,
including the proposed estimation method, for extrapolation to a wide area.
In closing, we would like to express our sincere
gratitude to all the people at the Centre for Climate
Risk and Opportunity Management in Southeast Asiaand the Pacific of Bogor Agricultural University andthe Overseas Energy Management Department, Sales
Division, Fuji Electric Co., Ltd. for their cooperation in
introducing this system.
note)1) Technology advancement project commission inIndonesia in relation to MRV, etc., for FY2014, 2015,2016 Joint Credit Mechanism (JCM) promotion.

References
1)
2)
3)

4)
5)
6)
7)

World Economic Outlook Database. International
Monetary Fund, April (2016).
Intellectual asset creation. Nomura Research Institute,
October issue (2014) (in Japanese).
Fuji Electric FA Components & Systems: Energy Control
Equipment.
http://www.fujielectric.com/products/pm.html
G. Kitagawa: An introduction to time series analysis.
11th edition, Iwanami Shoten, (2014) (in Japanese).
M. Kin: Data Science by R. Ninth edition, Morikita
Publishing, (2014) (in Japanese).
T. Yamashita: Learning Deep Learning with Pictures.
Third edition, Kodansha, (2016) (in Japanese).
F. A. Gers et al.: Learning to forget: Continual prediction
with LSTM. In Artificial Neural Networks, 1999. ICANN
99. Ninth International Conference on (Conf. Publ. No.

FUJITSU Sci. Tech. J., Vol. 53, No. 3 (April 2017)

9)

10)

11)

12)
13)

470), volume 2, pp. 850–855 (1999).
I. Doi: Perfect manual for air-conditioning equipment
practice. Second edition, Shuwa System (2014) (inJapanese).
M. Miyata: On the actual performance of air conditioning and heating equipment [Reference 5]. Building
Research Institute (in Japanese).
http://www.kenken.go.jp/japanese/information/
information/press/20120611/20120611-5.pdf
Ministry of the Environment: Energy saving actions thatcan be done in the office (in Japanese).
https://funtoshare.env.go.jp/setsuden/office/saving01.html
Kota Bogor Dalam Angka Bogor City in Figures 2015,
ISSN 0215.59.31, Publication Number: 32.710.15.01(2015).
Statistik PLN, ISSN 0852-8179, Publication Number:
31:621.3 (2014).
J. Sherrah: Fully Convolutional Networks for Dense
Semantic Labelling of High-Resolution Aerial Imagery.
arXiv: 1606.02585v1. [cs.CV] (2016).

Kenji Uchida
Fujitsu Ltd.
Mr. Uchida is currently engaged in the development of business systems focusing
on data analysis.

Minoru Fujii
National Institute for Environmental
Studies
Center for Social and Environmental
Systems Research
Dr. Fujii is a Senior Researcher at the Ecosociety Innovation Section.

Shuichi Ashina
National Institute for Environmental
Studies
Center for Social and Environmental
Systems Research
Dr. Ashina is a Senior Researcher at theTrans-boundary Impacts & Mitigation
Modeling Section.

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K. Uchida et al.: Field Trial toward Low-carbon Society in Indonesia

Seiya Maki
National Institute for Environmental
Studies
Center for Social and Environmental
Systems Research
Dr. Maki is a Research Associate at the
Eco-society Innovation Section.

Toshinori Hanya
Fujitsu Ltd.
Mr. Hayama is currently engaged in thedevelopment of business systems focusing on data analysis.

Kengo Aizawa
National Institute for Environmental
Studies
Center for Social and Environmental
Systems Research
Mr. Aizawa is an Advanced Skill Specialistat the Eco-society Innovation Section.

Tsuyoshi Fujita
National Institute for Environmental
Studies
Center for Social and Environmental
Systems Research
Dr. Fujita is Director of the Center for Socialand Environmental Systems Research.

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FUJITSU Sci. Tech. J., Vol. 53, No. 3 (April 2017)