ARTICLE IN PRESS

0360-5442/$ - se

doi:10.1016/j.en

�CorrespondE-mail addr

Energy 32 (2007) 1617–1633

www.elsevier.com/locate/energy

Evaluation of city-scale impact of residential energy conservationmeasures using the detailed end-use simulation model

Yoshiyuki Shimoda�, Takahiro Asahi, Ayako Taniguchi, Minoru Mizuno

Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, 2-1, Yamada-oka,

Suita, Osaka 565-0871, Japan

Received 6 October 2006

Abstract

Energy conservation policies for the residential sector are evaluated by a model that simulates city-scale energy consumption in the

residential sector by considering the diversity of household and building types. In this model, all the households in the city are classified

into 380 categories based on the household and building type. The energy consumption for each household category is simulated by the

dynamic energy simulation model, which includes an energy use schedule model and a heating and cooling load calculation model. Since

the energy usage of each appliance is simulated for every 5min according to the occupants’ energy usage activity, this model can evaluate

not only the energy conservation measures by improving the buildings and appliances but also the measures that involve changing the

occupants’ activities. The accuracy of the model is verified by comparing its results with the statistical and the measured data on Osaka

City, Japan. Various types of energy conservation measures planned by the Japanese government for the residential sector are simulated

and their effects on Osaka City are evaluated quantitatively. The future effects of these combined measures on the energy consumption

are also predicted.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: City scale evaluation; Energy conservation measures; Household type distribution; Occupants’ behavior

1. Introduction

In Japan, energy consumption in the residential sectorhas been increasing continuously due to improvements inthe standard of living, such as enlargement of houses,popular use of various types of home electric appliances,and an increase in the number of small families. In the last25 years, the energy consumption of the residential sectorhas doubled while the population has increased by only10% [1].

To achieve the 6% greenhouse gas reduction commit-ment in keeping with the Kyoto Protocol, various kinds ofmeasures were proposed for the residential sector in Japan.The ‘‘New Climate Change Policy Program,’’ which wasadopted in March 2002, aims to maintain the CO2 emissionfrom energy usage at its levels in 1990. However, CO2

emission from energy use in the residential sector had

e front matter r 2007 Elsevier Ltd. All rights reserved.

ergy.2007.01.007

ing author. Fax: +81668797665.

ess: shimoda@see.eng.osaka-u.ac.jp (Y. Shimoda).

increased from its level in 1990 by 28.8% in 2002 due to anincrease in the number of household appliances. Accordingto this increase, the ‘‘Kyoto Protocol Target AchievementPlan,’’ which was adopted in April 2005, aims to reduceCO2 emission from the residential sector such that it is 6%greater than its level in 1990 by strengthening the energyefficiency measures for buildings and appliances.As a part of these programs, the revised version of the

Law Concerning Rational Use of Energy established one ofthe highest energy efficiency standards—commonly knownas the ‘‘Top-runner Standard’’—in the world for homeelectric appliances. According to this standard, an appli-ance manufacturer’s average energy efficiency in 2004 mustbe higher than that of the most efficient model in 1999.This standard was revised in 2005 to extend it to moretypes of appliances.Besides the top-runner standard, the New Climate

Change Policy Program specifies various kinds of energyconservation measures such as increase in energy effi-cient residential buildings, reduction in standby power,

ARTICLE IN PRESSY. Shimoda et al. / Energy 32 (2007) 1617–16331618

promotion of high-efficiency water heaters. The programalso specifies the change in occupants’ behavior such as‘‘easing set room air temperature,’’ ‘‘family membersstaying together in the living room and not in theirindividual rooms,’’ and ‘‘reducing the number of hoursspent watching TV.’’

The effects of these measures are interactive with othermeasures. For example, energy efficient appliances andchange in the occupants’ schedule affects the cooling andheating load. Sezgan and Koomey [2] estimated theinteraction between the lighting and space conditioningenergy use in commercial buildings. Therefore, thesemeasures should be evaluated by a model that can treatthe combination of these measures simultaneously.

Generally, it is very difficult to quantitatively estimatethe city or national scale effects of these measures. Thequantitative evaluation of energy conservation measureshas thus far been based on the simulation results for ‘‘astandard household,’’ which implies a family comprisingtwo adults and two children. However, the energyconsumption of each household differs considerablydepending on the household type (number and age ofmembers), building type, the number and efficiency ofappliances, the occupants’ activity, and other factors.Ultimately, to quantify the city-scale effect of the variousenergy conservation measures, which include the dissemi-nation of energy efficient appliances and buildings andchange in the occupants’ behavior correctly, a ‘‘virtual citymodel’’ must be developed. This model should be capableof simultaneously simulating the operation of all appli-ances and the occupants’ behavior in all households withinthe objective region without using ‘‘unit energy consump-tion per household/person/floor area.’’

Clarke et al. [3] applied a building simulation programfor estimating the effect of the improvement in athermodynamic class such as window size and insulationlevel by considering the present distribution of house typesand thermodynamic classes in Scotland. Jones et al. [4]developed a model that estimates the residential energyused in a city by considering the distribution of the buildingenergy used based on Geographical Information Systemtechniques. Brownsword et al. [5] developed the urbanenergy model which simulates spatial and diurnal varia-tions of energy demand based on diurnal demand profile ofeach consumer type. However, these models could notconsider the energy use by each appliance. Michalik et al.[6] developed a structure model of electricity demand in theresidential sector of a region based on a bottom-upapproach that sums up each appliance’s operation schedulefor improving the electricity load curve by demand sidemanagement. However, this method does not include theheat load calculation and the fuel consumption.

The authors [7] have developed a bottom-up simulationmodel that simulates the city-scale energy consumption inthe residential sector by considering the diversity ofhousehold and building types. In this model, the energyconsumption for each household category was simulated

by the appliance energy use model, hot water supplymodel, and heating and cooling model. In the applianceenergy use model, the energy use of each appliance wassimulated individually based on the schedule data of theoccupants’ behavior. In the heating and cooling model, thecooling and heating load was simulated from the buildingdata and weather data. The internal heat gain, which wascalculated by the appliance energy use model, and theoccupants’ behavior schedule were also used in this model.Since rooms in Japan are commonly equipped with roomair conditioners and heaters, which are intermittently usedfor air-conditioning, consideration of the occupants’behavior schedule is necessary to correctly estimate theenergy consumption for heating, cooling, and lighting aswell as the energy consumption of appliances such astelevisions. In Japan, the time allocation of living activitiesis surveyed every 5 years by Broadcasting Culture ResearchInstitute [8]. These results can be used for modeling theoccupants’ energy use schedule [9].In this paper, our previous model was improved in terms

of the heat load calculation, simulation of the occupants’behavior schedule, and so on. The new model has beenapplied to Osaka City (population: 2598 thousand, house-holds: 1044 thousand). The present amount of energyconsumption in the residential sector is estimated andcompared with the statistical data. In the final part of thispaper, the energy conservation effects of the various kindsof measures are evaluated quantitatively.

2. Simulation model

2.1. Structure of the simulation model

Fig. 1 shows the structure of the simulation model. Inthis simulation, the annual energy consumption of onehousehold is calculated iteratively for 19 householdcategories and 20 building categories—10 categories fordetached houses and 10 categories for apartment housesare set depending on the floor area. In addition, five typesof building insulation levels are assumed. Each occupant’stime allocation for living activities, amount and tempera-ture of hot water supply, weather data, and appliance’senergy efficiency properties are provided as input data. Thesimulation of heat load and energy use is conducted in timesteps of 5min. The total energy consumption by theresidential sector in the object region can be estimated bymultiplying the simulated energy consumption and thenumber of households in each category and then summingthe products.The authors have also developed a ‘‘stock model,’’ which

estimates the distribution of an appliance’s energy effi-ciency and a building’s insulation levels in the object regionand the object year. Furthermore, this model also estimatesthe input data for the simulation model, such as theappliance’s average energy efficiency ratio, which meansthe weighted average of the energy efficiencies of theexisting appliances determined by the year of manufacture.

ARTICLE IN PRESS

Fig. 1. Flowchart of the simulation.

Y. Shimoda et al. / Energy 32 (2007) 1617–1633 1619

2.2. Appliance energy use model

2.2.1. Schedule of living activities

To determine the occupants’ schedule of livingactivities, the results of the time allocation survey of livingactivities, which was performed by the Broadcasting

Culture Research Institute, NHK (Japan Broadcast-ing Corporation), have been used. In the formermodel [7], the living activity of an occupant at eachtime step was expressed as a percentage of probability. Inthe new model, the living activity of each householdmember of each household category over 1 year is

ARTICLE IN PRESS

Table 1

Power consumption of home electric appliances used in this simulation

Y. Shimoda et al. / Energy 32 (2007) 1617–16331620

determined by the occupants’ behavior simulation asfollows:

Appliances Room Number of Power consumption (W)

� holdings

(per 100

households)

Operating

mode

Standby

All household members are classified into 8 occupantcategories corresponding to the time allocation survey:employed male, employed female, housewife, elemen-tary school student, junior high school student, highschool student, aged male, aged female.

�

Rice cooker Kitchen 88.1 1250.0 35.0

Dishwasher Kitchen 22.2 1000.0 3.0

Thermos Kitchen 72.1 1000.0 45.0

Microwave Kitchen 100.6 200.0 3.4

Toaster Kitchen 79.0 500.0 2.8

For each occupant category, the behavior schedule issimulated in time steps of five minutes for 500 days ofweekdays and holidays from the result of the time allocationsurvey. In this simulation, simultaneous activities such aswatching television while having a meal can be considered.

TV Living and 238.1 114.0 2.4

� bedroom

Refrigeratora Kitchen 122.8 600.0 No standby

Fan Kitchen 100.0 20.0 0.0

Washing

Machine

Bathroom 109.3 126.0 0.7

Tumble dryer Bathroom 26.4 1300.0 0.2

Hair dryer Bathroom 133.9 450.0 0.0

Desk lamp Bedroom 100.0 30.0 0.0

Vacuum Living room 148.3 200.0 0.0

Iron Living room 102.5 500.0 0.0

VCR Living and

bedroom

127.4 21.0 3.7

Radio Living and

bedroom

88.0 100.0 14.0

CD player Bedroom 86.2 100.0 14.0

PC Bedroom 47.6 62.7 3.3

PC accessories Bedroom 47.6 20.0 34.8

BS tuner Living and

bedroom

43.1 b 13.9

Fax Living room 39.1 b 20.0

Telephone Living room 138.2 b 5.0

Shower toilet Toilet 53.4 b 35.0

Kotatsu (foot

warmer)

Living room 116.3 500.0 0.0

Electric carpet Living room 116.1 580.0 0.1

aPower consumption of refrigerator is modeled as a function of outdoor

air temperature.bOperating mode is not considered since operation time is quite small.

For each household category, the behavior schedule ofeach household member for a day is selected at randomfrom the abovementioned 500-day schedules on a dailybasis. This procedure enables the expression of thedistribution of activities among occupants in the samecategory and avoids the occurrence of unrealistic peaksof simulated electricity load curves for the entire city.

2.2.2. Link between living activities and energy use

Each living activity calculated by this simulation is linkedwith the energy use of appliances and hot water use. Theprobability of appliance use is also considered for each timeperiod. From the links between each family member and aroom, the room where each living activity occurs is alsoidentified to determine the energy use for heating, cooling,and lighting. Table 1 shows the power consumption and thestandby power of the home electric appliances used in thissimulation. The dissemination ratios of these appliancesshown in the Table 1 are also considered in this model.

2.3. Lighting energy use schedule model

It is assumed that all the occupied rooms (except whenoccupant is asleep) and corridors are illuminated at night. Inthe daytime, occupied rooms are classified as rooms that arealways illuminated, rooms that are never illuminated, androoms where the lighting is dependent on the brightness ofdaylight. The ratios of these categories are determined fromthe results of the questionnaire survey. Energy consumptiondue to lighting is set as 5W/m2 for all rooms. The brightnessof daylight is calculated from the weather data.

2.4. Hot water energy use model

Energy consumption due to hot water use is calculatedfrom the amount of hot water used and its temperature andthe city water temperature. The city water temperature isconsidered to be a function of the outdoor air temperature.

2.5. Heating and cooling model

2.5.1. Heat load simulation

Using the Standard Weather Data of Osaka City,dynamic heat load simulation is carried out. In the former

model [7], the heat conduction and ventilation betweenrooms was not considered and only one insulation level(the average insulation level) was considered. In the newmodel, the ventilation and heat conduction between roomsis considered by a thermal circuit network method. Asshown in Fig. 2, room air, a thin (interior) wall, or awindow is expressed as one node and a thick (exterior) wallis expressed as two nodes (inside and outside) in thethermal circuit network. The heat transfer between twosuch nodes is calculated from the thermal resistance andtemperature difference. Incident solar radiation on wallsand windows is also considered. In this model, all detachedhouses are assumed to be wooden buildings and allapartment houses are assumed to be reinforced concretebuildings. To simulate the heating and cooling load foreach building category, the floor plan of houses for each of

ARTICLE IN PRESSY. Shimoda et al. / Energy 32 (2007) 1617–1633 1621

the 20 building categories is assumed, as shown in Fig. 3.The heating and cooling load and energy consumption issimulated for five types of insulation: no insulation,insulation below the 1980 standard, insulation up to the1980 standard, insulation up to the 1992 standard, andinsulation up to the 1999 standard. The heating and cool-ing energy consumption of each household and buildingcategory is obtained from the weighted average of theseresults with the share of each insulation level estimatedfrom our stock model. The room air temperature ofcooling is 27 1C. The ventilation rate (including infiltration)

Fig. 2. Thermal circuit network model.

Bed room

9.96 m2

Bed room

9.96 m2

Bed room

Balcony

11.6 m2

Living room

14.9 m2

Liv

1

Kitchen4.98 m2

9100

7280

Apartment house 68.7m2

Fig. 3. Example of the plan of t

and room air temperature of heating are set differentlydepending on the building insulation category in order toconsider the air tightness and the rebound effect ofinsulation [10]: 3.0 ac/h and 18 1C for no insulation,2.0 ac/h and 20 1C for insulation below the 1980 standard,1.0 ac/h and 21 1C for insulation up to the 1980 standard,0.5 ac/h and 22 1C for insulation up to the 1992 and 1999standards.

2.5.2. Room air conditioner model

The coefficient of performance (COP) of a room airconditioner is modeled as a function of the outdoor airtemperature and part load ratio (compressor speed); itvaries with each time step. This function is derived from thecalculation method of the Seasonal Energy EfficiencyRatio (SEER) for a room air conditioner by JapanRefrigeration and Air Conditioning Industry Association(JRAIA) [11]. Fig. 4 shows the COP of the room airconditioner used in the base case (averaged value in 2000)and that which conformed to the 1999 Law ConcerningRational Use of Energy (top-runner standard). The statetransition probability function, which decides whether theroom air conditioner is powered based on the room airtemperature and time [9], is also considered in this model[12]. In Japan, most room air conditioners have a heatpump mode. The energy consumption for heating iscalculated from the estimated share and efficiency of theroom air conditioner, electric heater, city gas heater, andkerosene heater.

Bed room

9.96 m2

Bed room

9.96 m2

Bed room

11.6 m2

Balcony

ing room

1F

9.9 m2

Kitchen8.3 m2

8190

8190

Detached house 87.2m2

5460

he house used in this model.

ARTICLE IN PRESS

0

2

4

6

8

10

12

20 22 36343230282624

Outdoor Air Temperature [°C]

CO

P

Cooling

Base case

Top runner

0

2

4

6

8

10

-10 -5 0 5 10 15

Outdoor Air Temperature [°C]

CO

P

Heating

Base

(Minimum speed)

(Minimum speed)

(Minimum speed)

(Minimum speed)

(Maximum speed)

(Maximum speed)

(Maximum speed)

(Maximum speed)

Top

1

2

Fig. 4. Relationship between the outdoor air temperature and COP.

(Cooling and heating mode, 2.8 kW cooling capacity).

Table 2

Household types and number of households in Osaka City

No. of household members Family type of household No. of wo

1 Male 1

Female 1

Aged male 0

Aged female 0

2 Couple 2

1

Aged couple 0

Mother and a childb 1

0

3 Couple and a child 2

1

Mother and children 1

0

4 Couple and children 2

1

5 Couple and children 2

1

More than 5 Couple, children, and parents 2

1

Total

a‘‘Working person’’ means a family member who is out on business on a wbAll children are defined as students.

Y. Shimoda et al. / Energy 32 (2007) 1617–16331622

2.6. Classification of households

Using the results of the National Population Census [13],all households in Osaka City are classified into 19household categories, 2 building categories (detachedhouse or apartment house), and 10 floor area categoriesfor each building category. Table 2 shows the householdcategories and number of households in Osaka City used inthis study.

2.7. Identification of the parameters

In addition to the settings shown in the previoussections, there are some parameters that are necessary torun this simulation model, such as the share of heatingequipment, frequency of bathing, dissemination ratio ofappliances, and usage ratio of lighting in daytime. Theseparameters are determined from the results of the ques-tionnaire survey for the residents of Osaka City [14].

3. Simulation results for present condition and verification of

the model

3.1. Simulation results for each household

In this paper, energy consumption is indicated as the‘‘primary energy consumption,’’ and electricity consump-tion is calculated by the following relation: 1 kWh ofelectricity ¼ 9830 kJ of primary energy consumption.

rking personsa Category Detached house Apartment house

1a 22,442 178,867

1b 15,670 125,233

1c 10,490 22,323

1d 39,513 40,278

2a 22,847 43,432

2b 21,259 40,414

2c 58,887 38,968

2d 16,438 25,833

2e 9739 15,304

3a 25,279 31,942

3b 40,808 51,565

3c 9139 11,146

3d 5414 6603

4a 30,665 35,816

4b 39,909 46,612

5a 10,571 10,595

5b 13,757 13,788

6a 4833 2639

6b 6487 3543

404,146 744,901

eekday.

ARTICLE IN PRESSY. Shimoda et al. / Energy 32 (2007) 1617–1633 1623

Fig. 5 shows the distribution of the simulated annualprimary energy consumption of one household in a detachedhouse and an apartment house. These figures show that thetotal energy consumption depends more on the number offamily members than the total floor area. The influence of thetotal floor area on the energy consumption is stronger in alarge family since the number of occupied rooms and energyuse for lighting, heating, and cooling increases with thenumber of family members. In addition, energy consumptionfor TVs increases with the number of rooms occupied since aTV is assumed to be available in the living room and all of thebedrooms. By this assumption, the total number of TVsowned approaches the statistical value shown in Table 1.

160.0

140.0

120.0

100.0

80.0

60.0

40.0

20.0

0.0

160.0

140.0

120.0

100.0

80.0

60.0

40.0

20.0

0.0

10806040200

Floor area [

10806040200

Floor area [m

Annual prim

ary

energ

y

consum

ption [G

J/y

ear/

household

]

Annual prim

ary

energ

y

consum

ption [G

J/y

ear/

household

]

Fig. 5. Distribution of the total energy consumption for an apartment hous

correspond to the ‘‘category’’ column in Table 2).

For the same floor area and household category, theenergy consumption in a detached house is greater thanthat in an apartment house. Most of this difference is dueto the amount of heating energy. The difference increasesin a large family’s house, as the number of rooms heatedis greater.

3.2. Total energy consumption in the city and comparison

with statistical data

Fig. 6 shows the comparison between the simulatedannual primary energy consumption and actual energy

1801601401200

m2]

1801601401200

2]

1a

1b

1c

1d

2a

2b

2c

2d

2e

3a

3b

3c

3d

4a

4b

5a

5b

6a

6b

1a

1b

1c

1d

2a

2b

2c

2d

2e

3a

3b

3c

3d

4a

4b

5a

5b

6a

6b

e [top] and detached house [bottom]. (Symbols ‘‘1a’’-‘‘6b’’ in the legend

ARTICLE IN PRESS

Fig. 6. Comparison between the simulated energy consumption in Osaka

City and the statistical value (in primary energy [1 kWh of electricity

¼ 9830kJ]).

3.0

2.5

2.0

1.5

1.0

0.5

0.0Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ga

s C

on

su

mp

tio

n[P

J/m

on

th]

Simulated Actual supply data

Fig. 7. Comparison between the simulated monthly gas consumption and

the actual value.

Y. Shimoda et al. / Energy 32 (2007) 1617–16331624

supply in the residential sector in Osaka City in 2000, whenthe weather data used in the model is observed. The actualsupply data was estimated by utility companies. Theamount of kerosene supplied is estimated from the unitsper household obtained from the statistical data. Thesimulated total energy consumption agrees with the actualprimary energy supply within 1.0%. The errors inelectricity, city gas and kerosene supply are 1.3%,

�10.2%, and 30.3%, respectively. Monthly data of citygas supply is compared in Fig. 7. The annual and monthlysimulated gas consumption have good agreement with theactual data.Fig. 8 shows the electricity load curve of Osaka city’s

residential sector. An improvement of the energy use schedulemodel (introduction of occupants’ behavior simulation) makesthis load curve more realistic than that of the previous model[7]. Even at midnight, the electricity consumption by appliancesdoes not reduce significantly since most of it is in the form ofstandby energy use. Since city’s level load curve for theresidential sector alone cannot be measured, the measuredelectricity load curve of the residential district in the vicinity ofOsaka City in July 2001 and sensitivity of electricityconsumption to outdoor air temperature derived from theload curve [15] are used for verification of the model. In thisdistrict, total floor areas of residential buildings and non-residential buildings are 1,144,000m2, and 184,000m2, respec-tively. Simulation was done for Osaka City area using weatherdata of the same period as measurement. The differences ofhousehold and building type distribution between Osaka Cityand the measurement district are not considered. Fig. 9(a)shows the comparison of the normalized electricity load curve.The simulated load curve on holiday and the daily peak valueshave a good agreement with the actual value. On the otherhand, the simulated electricity consumption at midnight and inthe weekday’s daytime is smaller than the actual value. Thereasons for these results are supposed that the actual valuecontains the electricity use by end-uses other than residentialbuildings, and that the ratio of people at home in the daytimein the measurement district is larger than Osaka City as theproportion of single family in the district is small. Fig. 9(b)shows the percentage of increase in electricity consumption bya 1 1C increase of outdoor air temperature. Simulatedsensitivity shows good agreement with the value estimatedfrom measured data. Since sensitivity of electricity consump-tion to air temperature in summer has a close relationship withcooling, it is clear that the model can simulate cooling energyconsumption with sufficient accuracy.

4. Evaluation of various energy-saving measures

One significant advantage of this model is its simulation ofthe heating and cooling loads precisely by coupling thedynamic heat load simulation and energy use schedule model.Further, it enables to consider the change in the energyconsumption due to heat insulation of buildings, climateconditions, and schedules of living activities. Accordingly, theeffect of a heat insulation standard, an energy efficiencystandard for room air conditioners and an introduction ofdaylight saving time are predicted as an example of the energyconservation policy evaluation by this simulation model.

4.1. Heat insulation of a building

The annual energy consumption for heating andcooling for each household and building category

ARTICLE IN PRESS

Fig. 8. Simulated electricity load curve of Osaka City’s residential sector (August 6 [Sunday] to August 7 [Monday]). (�‘‘Kitchen’’ means five appliances

from the top of Table 1; ��Energy use of refrigerator is modeled as a function of the daily average outdoor air temperature. Hourly change is not

modeled).

Fig. 9. Comparison between the simulated electricity load curve and the measured value.

Y. Shimoda et al. / Energy 32 (2007) 1617–1633 1625

is simulated under three heat insulation conditions asfollows:

(1)

Base case: The share of each insulation level in 2000 isassumed as shown in Table 3. Since an insulation

standard for houses is not mandatory in Japan, theshares of the 1992 and 1999 standards are very small.

(2)

1992 Standard case: All houses in the city conformto the 1992 standard (New Energy ConservationStandard).

ARTICLE IN PRESS

Table 3

Heat insulation conditions used in this study

Standard Exterior wall Window Share in the base case (%)

Detached house

No insulation None Single glazing (6.51W/m2K) with lace curtain 47.6

Below 1980 Glass wool (0.050W/mK) 15.0mm Single glazing (6.51W/m2K) with lace curtain 16.1

1980 Standard Glass wool (0.050W/mK) 30.0mm Single glazing (6.51W/m2K) with lace curtain 31.7

1992 Standard Glass wool (0.038W/mK) 38.0mm Single glazing (6.51W/m2K) with lace curtain 4.4

1999 Standard Glass wool (0.036W/mK) 79.2mm Double glazing (4.65W/m2K) with blind 0.2

Apartment house

No insulation None Single glazing (6.51W/m2K) with lace curtain 20.2

Below 1980 Polystyrene foam (0.040W/mK) 10.0mm Single glazing (6.51W/m2K) with lace curtain 24.2

1980 Standard Polystyrene foam (0.040W/mK) 20.0mm Single glazing (6.51W/m2K) with lace curtain 50.6

1992 Standard Polystyrene foam (0.040W/mK) 36.0mm Single glazing (6.51W/m2K) with lace curtain 4.8

1999 Standard Polystyrene foam (0.028W/mK) 30.8mm Double glazing (4.65W/m2K) with blind 0.2

35.0

30.0

25.0

20.0

10.0

15.0

5.0

0.00 20 40 60 80 180160140120100

Floor area [m2]

An

nu

al p

rim

ary

en

erg

y r

ed

uctio

n

[GJ/y

ea

r/h

ou

se

ho

ld]

1a

1b

1c

1d

2a

2b

2c

2d

2e

3a

3b

3c

3d

4a

4b

5a

5b

6a

6b

Fig. 10. Distribution of the primary energy saving from the base case by conforming to the 1999 insulation standard (detached house). (Symbols ‘‘1a’’-

‘‘6b’’ in the legend correspond to the ‘‘category’’ column in Table 2).

Y. Shimoda et al. / Energy 32 (2007) 1617–16331626

(3)

1999 Standard case: All houses in the city conform tothe 1999 Standard (Next Generation Energy Conserva-tion Standard).

Fig. 11. The effect of heat insulation on the total energy consumption for

heating and cooling in Osaka City.

Fig. 10 shows the distribution of the simulated annualprimary energy reduction in the 1999 standard case incomparison with the base case for detached houses. Even inthe same house, the amount of energy conservationincreases with the number of household members. Thedifference of energy saving between a one-person house-hold and a six-person household is 5–6 times for a largehouse. The energy saving in a detached house is 1–2 timesgreater than that in an apartment house with the same floorarea.

Fig. 11 shows the total energy consumption for heatingand cooling in the Osaka City area under the three cases. Ifall buildings conform to the 1999 standard, the heatingenergy consumption is reduced by 9.1 PJ/year from thebase case. The cooling energy consumption is also reduced,

but the difference is small. The total amount of energysaved is equal to 13.2% of the total primary energyconsumption in the residential sector in Osaka City.

ARTICLE IN PRESSY. Shimoda et al. / Energy 32 (2007) 1617–1633 1627

4.2. Energy efficiency standard for room air conditioner

Fig. 12 shows the distribution of the simulated pri-mary annual energy reduction by substituting all theroom air conditioners with those that conform to the1999 Law Concerning Rational Use of Energy (top-runnerstandard) in comparison with the base case for a de-tached house. Since the difference of the COP in cooling islarger than that in heating, as shown in Fig. 4, and otherheating equipment such as the city gas heater is usedfor heating, the energy saving in cooling is larger than thatin heating. Even in the same building and room airconditioner setting, the amount of energy saving variesconsiderably depending on the occupants’ behavior.

Fig. 13 shows the comparison of the primary energyconsumption in Osaka City for cooling and heating underthe present energy efficiency and the efficiency conformingto the top-runner standard. From the simulation results

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

Annual coolin

g e

nerg

y r

eduction

[G

J/y

ear/

household

]

Cooling

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 20 40 60 80 100

Floor area [m

0 20 40 60 80 100

Floor area [m

Annual heating e

nerg

y r

eduction

[G

J/y

ea

r/h

ou

se

ho

ld]

Heating

1

2

Fig. 12. Distribution of the primary energy saving in room air conditioner fro

(Symbols ‘‘1a’’-‘‘6b’’ in the legend correspond to the ‘‘category’’ column in Ta

shown in the center of the figure, the primary energyconsumption for cooling and heating in Osaka City isreduced by 45% (2.63 PJ/year) and 6% (0.69 PJ/year),respectively. The sum of both these reductions is equal to4.6% of the total primary energy consumption in OsakaCity.The left side of Fig. 13 shows the energy consumption

estimated by the JRAIA’s SEER calculation method [11].In our simulation model, room air conditioners areassumed to be installed in living rooms and all bedroomsfor each of the 20 building categories, and the capacity ofeach room air conditioner is selected from five categories(2.2, 2.5, 2.8, 3.5, and 4.0 kW of cooling capacity) based onthe room area. By multiplying the total number ofhouseholds for each building category and summing upthe products, the total number of room air conditionersunits in Osaka City is obtained for the respective capa-cities. Therefore, by multiplying the seasonal electricity

1a

1b

1c

1d

2a

2b

2c

2d

2e

3a

3b

3c

3d

4a

4b

5a

5b

6a

6b

180160140120

2]

180160140120

2]

1a

1b

1c

1d

2a

2b

2c

2d

2e

3a

3b

3c

3d

4a

4b

5a

5b

6a

6b

m base case by conforming to the top-runner standard (detached house).

ble 2).

ARTICLE IN PRESS

60

50

40

30

20

10

Base Top-runner Base Top-runner Top-runner

JRAIA Simulation Simulation

(All RAC

Heating)

Prim

ary

En

erg

y C

on

su

mp

tio

n [

PJ]

Heating

Cooling

0

Fig. 13. Change in primary energy consumption of room air conditioner

by conforming to the top-runner standard.

Y. Shimoda et al. / Energy 32 (2007) 1617–16331628

consumption for each capacity estimated by the JRAIAmethod and the total number of units, the annual energyconsumption by room air conditioners in Osaka City isestimated with the same unit number condition as that inthe simulation. Both the total energy consumption andenergy-saving effect of the top-runner standard estimatedby the JRAIA method are much larger than the simulationresults shown in the center of the Fig. 13. One of thereasons is the overestimation of the operation hours by theJRAIA method. Although 18 h operation (from 6AM to0AM) per day is assumed in the JRAIA SEER calculationmethod, room air conditioners are not operated oftenduring the daytime in Japanese residences. As shown inFig. 8, the result of the simulation shows that the operationof room air conditioners during the daytime on weekdays isrestrained. As previously mentioned, the other reason isthat other heating appliances are used in the simulationmodel.

To make the best use of the energy efficiency of the top-runner standard, a case in which all heating is provided bythe room air conditioner is also simulated. The result isshown on the right-hand side of Fig. 13. In this case, theprimary energy consumption is reduced by 40% (8.29 PJ)from the ordinary top-runner standard case. In this case,the total primary energy saving by conforming to the top-runner standard and changing the heating equipment is8.8 PJ from the base case, and it is equivalent to 12.4% ofthe total primary energy consumption in Osaka City.

4.3. Introduction of daylight saving time

Daylight saving time, which has not been adopted inJapan, intends to reduce the lighting energy by setting theclocks 1 h later than the normal local time. However, inJapan, the daylight saving time may potentially increasethe cooling load in the evening.

In the simulation, the term representing the daylightsaving time is assumed to have limits from the first Sunday

in April to the last Sunday in October. The daylight savingtime is modeled by shifting the weather data from the otherschedule data by 1 h.The electricity load curve for lighting and cooling in

Osaka City’s residential sector on August 1 and 2 is shownin Fig. 14. In late afternoon, the lighting energy is reducedby the daylight saving time due to the difference ofsunlight. The energy use for cooling in the morning is alsoslightly reduced since the outdoor air temperature becomeslower than that in the base case. On the other hand, theenergy use for cooling in the evening becomes greater thanthat in the base case. This is because the cooling loadbecomes higher due to the higher outdoor air temperatureand solar radiation, and the frequency of cooling use ishigher in the evening than in the morning. In the seasonaltotal, the energy reduction of lighting becomes 0.02% ofthe total annual primary energy consumption in theresidential sector in Osaka City. The energy use for coolingincreases by 0.15% of the total energy use. Therefore, bythe adoption of daylight saving time, the primary energyuse is increased by 0.13% of the total primary energyconsumption in Osaka City’s residential sector.This result depends on the magnitude of the cooling load

and the occupants’ cooling behavior in Japan. In order toexamine the energy-saving effect of daylight saving time, asimultaneous evaluation in non-residential buildings isnecessary.

4.4. Evaluation of the other energy conservation measures

In addition to the results mentioned above, major energyconservation measures in the residential sector, as stated inthe New Climate Change Policy Program, were evaluatedquantitatively. The energy conservation measures consid-ered in this paper and these simulation methods are asfollows:

�

Easing set room temperature. Set room temperature ischanged from base case to 28 1C in cooling and 19 1C inheating. � Conforming to top-runner standard for refrigerators.

Annual energy consumption is reduced from 600 to400 kWh.

� Conforming to top-runner standard for televisions. Energy

consumption in the operation mode is reduced from 120to 103W, while standby power is reduced from 2.0 to0.6W.

� Conforming to top-runner standard for all other home

appliances. All lighting devices, VCRs, shower toilets,gas water heaters, and oil water heaters are made toconform to their corresponding top-runner standard.

� Introduction of water saving shower head. Hot water used

for showers is reduced by 50%.

� All the members of family watching TV together. In the

energy use schedule model, the room where the activityof ‘‘watching TV’’ and ‘‘resting’’ occurs is changed fromeach member’s bedroom to the living room in order to

ARTICLE IN PRESS

Fig. 14. Change in the electricity load curve by adopting daylight saving time.

Fig. 15. Energy conservation effects of each measure in Osaka City.

Y. Shimoda et al. / Energy 32 (2007) 1617–1633 1629

ARTICLE IN PRESSY. Shimoda et al. / Energy 32 (2007) 1617–16331630

reduce the energy consumption for television, lighting,heating, and cooling in the bedroom.

Fig. 15 shows the energy conservation effects in OsakaCity by all measures including the heat insulation ofbuildings and conforming to the top-runner standards forall room air conditioners in order of the amount of theenergy conservation effect. The result shows that theenergy conservation standards for heat insulation, refrig-erator, and room air conditioner are effective for city-scaleenergy conservation. On the other hand, occupants’behavioral changes such as ‘‘watching TV together’’ and‘‘easing set room temperature’’ are less effective. Fig. 16shows the peak electricity reduction effects for variousmeasures that are identical to those in Fig. 15. In OsakaCity’s residential sector, peak electricity occurred at 18:00in July. The top-runner standard for room air conditionersshows the maximum peak value for the electricity reduc-tion effect. Although the annual cooling energy does notdecrease significantly, peak electricity decreases consider-ably due to the heat insulation standard.

4.5. The total effect by combining multiple measures

Since the effect of each energy efficiency measure interactswith the other measures, the combined effect of some measurescannot be estimated by summing each effect, which is shownin Fig. 15. Therefore, the simulations on the energy-savingeffect of multiple measures are performed. These simulationsare performed according to the following three steps:

(1)

Step 1: Energy efficiency of the appliances: Conformingto the top-runner standard for room air conditioner,refrigerator, television, and the other home appliances.

Fig. 16. Peak electricity reduction effec

(2)

ts o

Step 2: Step 1+Energy-saving occupants’ behavior:Combination of ‘‘easing set room temperature’’, ‘‘in-troduction of water-saving shower head’’ and ‘‘allfamily members watching TV together’’.

(3)

Step 3: Step 2+All houses conforming to the 1999insulation standard.

Results of these simulations are shown in Fig. 17. InStep 1, the energy consumption is reduced by 13.7% fromthe base case value. This value is lower than 14.2%, whichis the sum of each measure’s effect, as shown in Fig. 15.One of the reasons for this is that the energy used forheating has not reduced, as shown in Fig. 15, since theinternal heat gain is reduced by a decrease in the energy useof appliances. Since the top-runner standard has becomemandatory, this reduction can be expected in the nearfuture.In Step 2, the energy consumption is reduced by 5.1%

from that in Step 1 due to the reduction in heating, cooling,hot water, television, and lighting.The effect of building insulation in Step 3 is very

large. The total energy consumption for cooling andheating becomes 37.5% of that in the base case. Inthis case, since the set room air temperature is loweredto 19 1C, the energy reduction by building insulation isgreater than that shown in Figs. 11 and 15, whichconsider the rebound effect of the building insulation(increase in set room temperature with the insulationlevel). On the other hand, the substitution of all otherheating equipment with room air conditioner is notconsidered in this simulation. By Step 3, the totalenergy reduction becomes 28.5% of the base case.However, a substantial amount of time will be requiredto achieve this situation since the building insulation

f each measure in Osaka City.

ARTICLE IN PRESS

Fig. 17. Combined effects of all the measures.

a

b

Fig. 18. Distribution of the total energy consumption for apartment

house [top] and detached house [bottom] in the combination case of

Step 3. (Symbols ‘‘1a’’-‘‘6b’’ in the legend correspond to the ‘‘category’’

column in Table 2).

Y. Shimoda et al. / Energy 32 (2007) 1617–1633 1631

standard is currently not mandatory and a long time willbe required to substitute the present building stock with anew one.

Fig. 18 shows the distribution of the annual primaryenergy consumption of one household in the case of Step 3.In general, the energy-saving effect of the measuresincreases with the floor area of the building and thenumber of household members. Consequently, as is evidentby comparing Fig. 18 with Fig. 5, the difference betweenthe annual primary energy consumption in an apart-ment house and a detached house, the difference due to thefloor area of the building, and the difference betweenhousehold categories, all become smaller than that in thebase case.

5. Conclusion

In this paper, a new ‘‘virtual city’’ end-use model, whichsimultaneously simulates the operation of all appliancesand occupants’ behavior in all households in the city, isdeveloped. This model can quantitatively estimate the city-scale effect of various types of energy conservationmeasures such as energy efficient appliances, insulation ofbuildings, and change in occupants’ behavior.Using this model, various types of energy conservation

measures in the residential sector proposed by the Japanesegovernment are evaluated quantitatively for the residentialsector in Osaka City. The results of the simulation are asfollows:

�

The effect of measures with regard to heating andcooling energy use such as building insulation andintroduction of energy efficient room air conditioners islarge. In particular, if all the residential buildingsconform to the 1999 insulation standard, the primaryenergy consumption in Osaka City will decrease by13.2% even if the rebound effect on the set airtemperature is considered. � For estimating the total annual energy consumption of

appliances in the city, the conventional estimationmethod, which multiplies the standard annual energyconsumption of an appliance with the total number ofappliances, results in an overestimation in case of room

ARTICLE IN PRESSY. Shimoda et al. / Energy 32 (2007) 1617–16331632

air conditioners. One of the reasons for this is thatintermittent heating and cooling is common in Japaneseresidential buildings.

� Changes in the occupants’ behavior such as all family

members watching television together and easing roomair temperature are less effective.

� In this model, the combined effect of the measures can

be evaluated by considering the interaction between themeasures such as the heating load increase by areduction in the energy used by appliances in the room.The simulation result shows that the top-runnerstandard for home electric appliances reduces theprimary energy consumption in Osaka City by 13.7%.

� If all the measures for building insulation and occu-

pants’ behavior considered in this study are adopted andthe appliances conform to the top-runner standard, theprimary energy consumption in Osaka City can bereduced by 28.5% of its present value. On this occasion,the difference in the primary energy consumption due tothe difference in building and household types reduces.

� Although the authors had not adjusted the parameters

in accordance with the difference between the simulationresults and the measured data, a good agreement wasobserved between the results and the statistical data inFigs. 6 and 7. However, it is considered that many errorsmay be included in each household level. For example,the rebound effect of insulation (e.g., room airtemperature in heating) must vary with the household,and it is difficult to define this effect. If there is norebound effect and the room air temperature of theentire room during heating is 18 1C, the energyconsumption for heating decreases by 12.5% from thebase case, and the total energy consumption decreasesby 2.4%. In addition, this model does not considered theshading effect of neighboring buildings, the position ofthe room in the apartment house, and the buildingorientation since the distributions of these parametersare difficult to estimate from statistical data and theinclusion of these parameters in the model requiresconsiderable computation time. Further, validation ofthe model with various types of measured data isnecessary to improve it.

When estimating the national-scale effect of thesemeasures from the results of this paper, it must be noticedthat the proportion of single families and small buildings inOsaka City is large in comparison to that in many othercities. For example, the proportion of single families inOsaka City is 40.3%, while the Japanese average is 27.4%.Nonetheless, the authors feel that this model can be appliedto many other locations and various types of measures. Forevaluating a micro-CHP system, which requires matchingof electricity demand and supply, the detailed electricityload curve is indispensable. Therefore, it is assumed thatthis model, which uses a 5min time step, will guaranteebeneficial effects for such a purpose. This model alsopermits the application of various types of conditions such

as weather conditions and demographic conditions. Forexample, this model can evaluate the change in energy usedue to an increase in air temperature on account of anurban heat island effect [7,12]. In the future, this can beapplied to predict the impact of climate change on theenergy efficiency measures [16].

Acknowledgment

This work is supported by Grants-in-Aid for ScientificResearch, Japan Society for the Promotion of Science,Nos. 15360310 and 18360273.

References

[1] The Energy Data and Modeling Center, The Institute of Energy

Economics, Japan. EDMC handbook of energy and economic

statistics in Japan 2002. Tokyo: The Energy Conservation Center,

Japan; 2002.

[2] Sezgan O, Koomey JG. Interactions between lighting and space

conditioning energy use in US commercial buildings. Energy

2000;25:793–805.

[3] Clarke JA, Johnstone CM, Kondratenko M, Lever M, McElroy LB,

Prazeres L, et al. Using simulation to formulate domestic sector

upgrading strategies for Scotland. Energy Build 2004;36:759–70.

[4] Jones PJ, Lannon S, Williams J. Modeling building energy use at

urban scale. In: Lamberts R, Negrao C, Hensen J, editors.

Proceedings of the seventh international IBPSA conference. Rio de

Janeiro, Brazil: IBPSA; 2001. p. 175–80.

[5] Brownsword RA, Fleming PD, Powell JC, Pearsall N. Sustainable-

cities—modelling urban energy supply and demand. Appl Energy

2005;82:167–80.

[6] Michalik G, Khan ME, Bonwick WJ, Mielczarski W. Structural

modeling of energy demand in the residential sector: 1. Development

of Structural Models. Energy 1997;22(10):937–47.

[7] Shimoda Y, Fujii T, Morikawa T, Mizuno M. Residential end-use

energy simulation at city scale. Build Environ 2004;39(8):959–67.

[8] Broadcasting Culture Research Institute, Japan Broadcasting Cor-

poration. NHK data book 2000 national time use survey, National,

Prefectural (Kokumin Seikatsu Jikan Chosa). Tokyo: Japan Broadcast

Publishing; 2001 [in Japanese].

[9] Tanimoto J, Hagishima A, Sagara H. A methodology for peak energy

requirement considering actual variation of occupants’ behavior

schedules. Build Environ, in press, doi:10.1016/j.buildenv.2006.

06.034.

[10] Mizuishi T, Murakami S, Ikaga T. LCCO2 assessment on thermal

insulation of residential buildings including fluorocarbon leakage.

J Environ Eng (Transactions of Architectural Institute of Japan,

5-26-20 Shiba, Munato-ku, Tokyo 108-8414) 2004;579:89–96 [In

Japanese].

[11] Japan Refrigeration and Air Conditioning Industry Association.

Calculating method of annual power consumption for room air

conditioners, JRA4046. Tokyo, Japan: Refrigeration and Air

Conditioning Industry Association (3-5-8, Shibakoen, Minatoku,

Tokyo 105-0011); 1999 [in Japanese].

[12] Shimoda Y, Nakao T, Narumi D, Habara H, Mizuno M, Tsuji K.

Verification of residential energy end-use model at city scale and

effect of outdoor air temperature. Energy Resour (Enerugi- Shigen,

1-9-10 Kyomachibori, Nishi-ku, Osaka 550-0003) 2006;27(6):431–7

[in Japanese].

[13] Statistics Bureau and Statistics Center. 2000 Population Census of

Japan, Tokyo, Japan Statistical Association (2-4-6 Hyakunin-cho,

Shinjukuku, Tokyo 169-0073), 2000 [in Japanese].

[14] Morikawa T, Shimoda Y, Mizuno M. Estimation of residential

energy end-use in Osaka city considering the distribution of residence

ARTICLE IN PRESSY. Shimoda et al. / Energy 32 (2007) 1617–1633 1633

and appliances. J Environ Eng (Transactions of Architectural

Institute of Japan, 5-26-20 Shiba, Munato-ku, Tokyo 108-8414)

2005;595:97–104 [in Japanese].

[15] Narumi D, Niurao Y, Shimoda Y, Mizuno M. Effects of temperature

change upon the regional electric power consumption, energy and

resources (Enerugi-Shigen, 1-9-10 Kyomachibori, Nishiku, Osaka

550-0003) 2007;28(3), in press [in Japanese].

[16] Gaterell MR, McEvoy ME. The impact of climate change

uncertainties on the performance of energy efficiency measures

applied to dwellings. Energy Build 2005;37:982–95.