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Citation: Nam, A.; Kim, Y.I.
Prioritizing Energy Performance
Improvement Factors for Senior
Centers Based on Building Energy
Simulation and Economic Feasibility.
Energies 2024,17, 5576. hps://
doi.org/10.3390/en17225576
Academic Editors: Joanna
Ferdyn‑Grygierek, Krzysztof
Grygierek and Agnes Psikuta
Received: 26 September 2024
Revised: 30 October 2024
Accepted: 30 October 2024
Published: 8 November 2024
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4.0/).
Article
Prioritizing Energy Performance Improvement Factors for
Senior Centers Based on Building Energy Simulation and
Economic Feasibility
Arisae Nam 1and Young Il Kim 2, *
1Department of Architectural Engineering, Graduate School, Seoul National University of Science and
Technology, Seoul 01811, Republic of Korea; nalgae1505@naver.com
2School of Architecture, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
*Correspondence: yikim@seoultech.ac.kr
Abstract: This study examined energy performance improvement factors by analyzing both energy
performance and the economic impacts to reduce energy costs for senior centers. A fact‑nding
survey was conducted on 20 senior centers in a metropolitan area, identifying key energy improve‑
ment factors. Energy simulations of the buildings were performed using ECO2, an ocially certied
energy assessment program in Korea, comparing the energy requirements before and after the im‑
provements. The energy demand, energy consumption, and oor area were analyzed, with the J, K,
and S standard models selected based on the median values of these factors. To assess the impact of
the improvements, blower door tests were conducted on two senior centers before and after window
upgrades. Based on the ECO2 simulations and the blower door test results, improvement priorities
were identied in the following order: windows, exterior walls, boilers, roofs, and doors. Finally, an
economic feasibility analysis applied the construction and heating costs to the standard models. Over
a 40‑year period, only boiler improvements generated a net prot. Without government support, this
study recommends prioritizing boiler upgrades when selecting energy performance improvements.
Keywords: senior center; building energy simulation; primary energy; life cycle cost; blower door test
1. Introduction
Amid the global phenomena of climate change and abnormal warming, energy de‑
mand for buildings has been increasing. Following the rise in international oil prices due to
the Russia–Ukraine war, issues regarding energy vulnerability among low‑income groups
have emerged in South Korea [1,2].
In addition, international oil prices have increased due to the Russia–Ukraine war, and
in Korea, the issues of energy‑vulnerable groups emerged due to the burden of heating en‑
ergy costs [1,2]. The number of patients aected by hypothermia and frostbite, particularly
elderly patients in their 60s and older, increased compared to 2021 due to the cost burden
of heating energy consumption and abnormal temperature phenomena [3,4].
Senior centers, which are spaces for the elderly (over the age of 60), are often estab‑
lished in renovated buildings. Old senior centers have high air inltration due to leakage
areas in the building, which increases the heating load and increases energy consumption.
In order to achieve energy welfare for vulnerable groups through the improvement of
energy performance in senior centers, the Seoul City implemented energy performance im‑
provement projects at 15 locations in 2020, 7 locations in 2021, and 12 locations in 2022. This
initiative also contributed to the reduction of greenhouse gas emissions from buildings
by encouraging private sector participation. By prioritizing energy performance improve‑
ments when specifying the scope of renovations, the eectiveness of the projects was en‑
hanced, further contributing to the reduction of greenhouse gas emissions
from buildings.
Energies 2024,17, 5576. https://doi.org/10.3390/en17225576 https://www.mdpi.com/journal/energies
Energies 2024,17, 5576 2 of 29
This study conrmed the need to improve the energy performance of senior centers,
which are mainly used by elderly people over 60 years of age. Among the energy per‑
formance improvement projects to be activated to achieve the domestic greenhouse gas
reduction target, this study intends to present the eects of each energy performance im‑
provement factor of the senior centers. In addition, an economic feasibility analysis was
conducted for each energy performance improvement factor to reduce the energy con‑
sumption burden of these centers and their users.
2. Objective
As mentioned in the introduction, it is necessary to improve the energy performance
of senior centers, which are mainly used by elderly people in their 60s or older. Prior to
an analysis, this study selected the target area by checking the regional status of old senior
centers. Presently, the “2nd Green Building Basic Plan” categorizes buildings that have
aged more than 20 years since the building completion as old buildings [5]. As shown in
Figure 1, the statuses of facilities for the elderly and children, built more than 20 years ago,
were analyzed for each region in Korea. [6].
Energies 2024, 17, x FOR PEER REVIEW 2 of 30
buildings by encouraging private sector participation. By prioritizing energy performance
improvements when specifying the scope of renovations, the effectiveness of the projects
was enhanced, further contributing to the reduction of greenhouse gas emissions from
buildings.
This study confirmed the need to improve the energy performance of senior centers,
which are mainly used by elderly people over 60 years of age. Among the energy perfor-
mance improvement projects to be activated to achieve the domestic greenhouse gas re-
duction target, this study intends to present the effects of each energy performance im-
provement factor of the senior centers. In addition, an economic feasibility analysis was
conducted for each energy performance improvement factor to reduce the energy con-
sumption burden of these centers and their users.
2. Objective
As mentioned in the introduction, it is necessary to improve the energy performance
of senior centers, which are mainly used by elderly people in their 60s or older. Prior to
an analysis, this study selected the target area by checking the regional status of old senior
centers. Presently, the “2nd Green Building Basic Plan” categorizes buildings that have
aged more than 20 years since the building completion as old buildings [5]. As shown in
Figure 1, the statuses of facilities for the elderly and children, built more than 20 years ago,
were analyzed for each region in Korea. [6].
An analysis of the current status of the facilities for the elderly as of 2021 revealed
that old buildings aged 20 to 35 years after completion were most concentrated in the
Gyeonggi-do province. In addition, it was confirmed that there are many old buildings in
Seoul that are over 35 years old.
In Korea, Seoul, Gyeonggi-do, and Incheon are categorized as metropolitan areas.
Therefore, this study investigated the effects of enhancing the energy performance of sen-
ior centers, specifically targeting the metropolitan areas where buildings for the elderly
and children, aged over 20 years, are concentrated. In addition, we conducted an economic
feasibility analysis of the improvement factors, and the effect of reducing heating energy
charges on the improvement factors was presented. Hence, the objective of this study is
to delineate the hierarchical prioritization of the improvement factors aimed at enhancing
the energy efficiency of senior centers. This will be accomplished through a comprehen-
sive analysis of the energy performance impact and economic viability associated with
each factor.
(a) (b)
Energies 2024, 17, x FOR PEER REVIEW 3 of 30
(c) (d)
Figure 1. Number of years since completion of the buildings for the elderly and children in Korea:
(a) number of buildings aged 20–25 years since completion; (b) number of buildings aged 25–30
years since completion; (c) number of buildings aged 30–35 years since completion; and (d) number
of buildings aged > 35 years since completion.
3. Research Process
To analyze senior center energy performance improvement effects and conduct an
economic analysis, we surveyed the energy performance improvement status of the senior
centers in the metropolitan area, conducted an ECO2 simulation to diagnose their energy
performance, selected standard models, performed an economic analysis, and conducted
blower door tests before and after window improvement. In addition, an economic feasi-
bility analysis was performed to analyze the energy cost reduction effect of implementing
various building energy performance improvement factors. The research process is de-
picted in Figure 2.
Initially, a comprehensive field survey encompassing 20 senior centers, subject to
governmental support aimed at enhancing energy performance within the metropolitan
region, was undertaken. We collected the existing design documents to analyze energy
consumption and other factors, followed by an on-site survey. The on-site survey included
user questionnaires, an assessment of the condition and aging status of each building com-
ponent, an identification of the areas and scopes for improvement, an asbestos inspection,
and a visual structural safety assessment. In addition, the energy performance improve-
ment factors were selected via a literature review. Secondly, the energy efficiency of these
buildings was evaluated through ECO2 simulations, wherein each selected factor contrib-
uting to energy performance enhancement was analyzed. Thirdly, the standard model
was chosen by assessing key parameters including energy demand, consumption, pri-
mary energy consumption, and the building area derived from the prior ECO2 simula-
tions, with the selection criterion being the median value.
Figure 1. Number of years since completion of the buildings for the elderly and children in Korea:
(a) number of buildings aged 20–25 years since completion; (b) number of buildings aged 25–30 years
since completion; (c) number of buildings aged 30–35 years since completion; and (d) number of
buildings aged > 35 years since completion.
Energies 2024,17, 5576 3 of 29
An analysis of the current status of the facilities for the elderly as of 2021 revealed that
old buildings aged 20 to 35 years after completion were most concentrated in the Gyeonggi‑
do province. In addition, it was conrmed that there are many old buildings in Seoul that
are over 35 years old.
In Korea, Seoul, Gyeonggi‑do, and Incheon are categorized as metropolitan areas.
Therefore, this study investigated the eects of enhancing the energy performance of se‑
nior centers, specically targeting the metropolitan areas where buildings for the elderly
and children, aged over 20 years, are concentrated. In addition, we conducted an economic
feasibility analysis of the improvement factors, and the eect of reducing heating energy
charges on the improvement factors was presented. Hence, the objective of this study is
to delineate the hierarchical prioritization of the improvement factors aimed at enhancing
the energy eciency of senior centers. This will be accomplished through a comprehen‑
sive analysis of the energy performance impact and economic viability associated with
each factor.
3. Research Process
To analyze senior center energy performance improvement eects and conduct an
economic analysis, we surveyed the energy performance improvement status of the senior
centers in the metropolitan area, conducted an ECO2 simulation to diagnose their energy
performance, selected standard models, performed an economic analysis, and conducted
blower door tests before and after window improvement. In addition, an economic feasi‑
bility analysis was performed to analyze the energy cost reduction eect of implementing
various building energy performance improvement factors. The research process is de‑
picted in Figure 2.
Energies 2024, 17, x FOR PEER REVIEW 4 of 30
Figure 2. Research process for analyzing the effects and economics of improving the energy perfor-
mance of senior centers.
Fourthly, to confirm the improvement in energy performance, constructions aimed
at enhancing energy efficiency were carried out in selected older facilities serving the el-
derly and children in the Seoul metropolitan area. Blower door tests were conducted be-
fore and after the improvements to verify the effectiveness of the energy enhancement
measures. Based on the results of the ECO2 simulation and blower door test conducted in
the third and fourth stages, a method for selecting improvement factors was proposed.
Fifthly, employing a standard model as the framework, this study conducted a de-
tailed analysis of the impact of specific elements on the energy performance improvement
of senior centers. This analysis aims to delineate a prioritization process for selecting im-
provement elements and to evaluate their economic implications.
Finally, this study analyzed the energy performance and economic effect of each im-
provement element and suggested a method for selecting senior center energy perfor-
mance improvement elements.
4. Senior Center Energy Performance Diagnosis
To assess the effects of the energy performance improvement measures implemented
in the senior centers, an architectural survey of 20 buildings was conducted. Additionally,
to evaluate the effectiveness of each energy performance improvement measure, the an-
nual primary energy consumption per unit area was determined by conducting ECO2
simulations.
4.1. Senior Center Status
To examine the conditions of the existing senior centers, the architectural and equip-
ment statuses of 20 buildings were surveyed via their drawings. For the building factors
that could not be assessed solely through a visual inspection, such as walls and roofs, the
applicable materials were estimated by referring to the relevant insulation application
standards based on the year of completion.
Figure 2. Research process for analyzing the eects and economics of improving the energy perfor‑
mance of senior centers.
Initially, a comprehensive eld survey encompassing 20 senior centers, subject to gov‑
ernmental support aimed at enhancing energy performance within the metropolitan re‑
gion, was undertaken. We collected the existing design documents to analyze energy con‑
Energies 2024,17, 5576 4 of 29
sumption and other factors, followed by an on‑site survey. The on‑site survey included
user questionnaires, an assessment of the condition and aging status of each building com‑
ponent, an identication of the areas and scopes for improvement, an asbestos inspec‑
tion, and a visual structural safety assessment. In addition, the energy performance im‑
provement factors were selected via a literature review. Secondly, the energy eciency
of these buildings was evaluated through ECO2 simulations, wherein each selected fac‑
tor contributing to energy performance enhancement was analyzed. Thirdly, the standard
model was chosen by assessing key parameters including energy demand, consumption,
primary energy consumption, and the building area derived from the prior ECO2 simula‑
tions, with the selection criterion being the median value.
Fourthly, to conrm the improvement in energy performance, constructions aimed at
enhancing energy eciency were carried out in selected older facilities serving the elderly
and children in the Seoul metropolitan area. Blower door tests were conducted before and
after the improvements to verify the eectiveness of the energy enhancement measures.
Based on the results of the ECO2 simulation and blower door test conducted in the third
and fourth stages, a method for selecting improvement factors was proposed.
Fifthly, employing a standard model as the framework, this study conducted a de‑
tailed analysis of the impact of specic elements on the energy performance improvement
of senior centers. This analysis aims to delineate a prioritization process for selecting im‑
provement elements and to evaluate their economic implications.
Finally, this study analyzed the energy performance and economic eect of each im‑
provement element and suggested a method for selecting senior center energy perfor‑
mance improvement elements.
4. Senior Center Energy Performance Diagnosis
To assess the eects of the energy performance improvement measures implemented
in the senior centers, an architectural survey of 20 buildings was conducted. Addition‑
ally, to evaluate the eectiveness of each energy performance improvement measure, the
annual primary energy consumption per unit area was determined by conducting ECO2
simulations.
4.1. Senior Center Status
To examine the conditions of the existing senior centers, the architectural and equip‑
ment statuses of 20 buildings were surveyed via their drawings. For the building factors
that could not be assessed solely through a visual inspection, such as walls and roofs, the
applicable materials were estimated by referring to the relevant insulation application stan‑
dards based on the year of completion.
4.1.1. Architectural Status
The architectural status of 20 senior centers in the metropolitan area was surveyed
based on the year of building completion, building standards at the time of completion,
and the visual inspection results. First, in terms of building age, as shown in Table 1, the
A‑senior center, completed in 1963, is the oldest, while the T‑senior center, completed in
1998, is the newest. Most of the buildings were completed between 1980 and 1989.
Table 1. Result of survey on completion year of 20 senior centers in Seoul metropolitan area.
Construction Completion Year Senior Center
Before 1970 A, B
1970–1979 C, D, E, F, G
1980–1989 H, I, J, K, L, M, N, O
1990–2000 Q, R, S, T
In addition, the external walls and roofs were estimated by analyzing design books,
performing a visual inspection, and evaluating the building standards for the year of com‑
Energies 2024,17, 5576 5 of 29
pletion. Through a visual inspection, the shape, area, and space of the buildings were iden‑
tied, and the materials used for the windows and doors, as well as the energy sources and
capacities of the boilers, were analyzed. For the buildings for which it was not possible to
obtain the architectural drawings because a long time had elapsed since their completion
date, the insulation measures applied to the outer walls and roof were estimated by refer‑
ring to the building information display and the year of completion [7,8].
The insulation composition and heat transfer coecient of the outer walls were inves‑
tigated. As shown in Table 2, the measures to prevent heat loss were initially implemented
in 1979 under Article 25 of the Enforcement Rules of the Building Act [8,9]. In addition,
in 1992, the measures to prevent heat loss were implemented under the “Rules on Equip‑
ment Standards for Buildings” and in 2008, under the “Criteria for Energy Saving Design
of Buildings” [10,11]
Table 2. External wall insulation standards in Seoul metropolitan area by revision year.
Revision Year External Wall Insulation Standard
(Heat Transfer Coecient U: W/m2K) Building Code Standard
1979 2.093 or 50 mm‑thick insulation Article 25 of Building Act Enforcement Rules
1980 0.581 Article 25 of Building Act Enforcement Rules
1984 0.581 or 50 mm‑thick insulation Article 19 of Building Act Enforcement Rules
1987 0.581 or 50 mm‑thick insulation Article 19 of Building Act Enforcement Rules—Central region
1992 0.581 or 50 mm‑thick insulation Article 21 of Regulations on Equipment Standards for
Buildings—Central region
Regarding total oor area, as outlined in Table 3, the highest number of senior centers
was observed within the range of 50 m2to 150 m2.
Table 3. Total oor area survey results of 20 senior centers in Seoul metropolitan area.
Total Floor Area (m2)Senior Center
Less than 50 P
50–100 or lower H, I, K, L, M, N, R
100–150 or lower B, D, E, F, G, J, O, S
More than 150 A, C, Q, T
Therefore, the heat transfer coecients of the outer walls were estimated by checking
the age since the completion year of the senior centers and the implementation date of
the standard for the outer wall insulations. According to the results, the estimated heat
transfer coecients of the outer walls before the improvements were 2.093 W/m2K and
0.581 W/m2K, as listed in Table 4. The external nishing materials were veried through
a site survey, and the insulation for the exterior walls was assumed to be PF boards, with
the insulation thickness calculated in reverse to meet the required overall heat transfer
coecient.
Table 4. Estimated heat transfer coecients of the exterior walls of 20 senior centers in Seoul
metropolitan area.
External Wall Insulation Standard
(Heat Transfer Coecient U: W/m2K) Senior Center
0.581 H, I, J, K, L, M, N, O, P, Q, R, S, T
2.093 A, B, C, D, E, F, G
The roof heat transfer coecients were estimated by checking the roof insulation stan‑
dards enforced in the Seoul metropolitan area by revision year and building age since the
year of completion, as shown in Table 5.
Energies 2024,17, 5576 6 of 29
Table 5. Roof insulation standards in Seoul metropolitan area by revision year.
Revision Year Roof Insulation Standard
(Heat Transfer Coecient U: W/m2K) Building Code Standard
1979 1.047 or 50 mm‑thick insulation Article 25 of Building Act Enforcement Rules
1980 0.581 Article 25 of Building Act Enforcement Rules
1984 0.581 or 50 mm‑thick insulation Article 19 of Building Act Enforcement Rules
1987 0.407 or 50 mm‑thick insulation Article 19 of Building Act Enforcement Rules—Central region
1992 0.407 or 80 mm‑thick insulation Article 21 of Regulations on Equipment Standards for
Buildings—Central region
The roof’s heat transfer coecient was estimated based on the completion time of the
senior center and the building standards code at that time.
According to the estimates, the roof heat transfer coecients before the improvements
were 0.407 W/m2K, 0.581 W/m2K, and 1.047 W/m2K, as shown in Table 6.
Table 6. Estimated roof heat transfer coecient values of 20 senior centers in Seoul metropolitan
area.
Roof Insulation Standard
(Heat Transfer Coecient U: W/m2K) Senior Center
0.407 N, O, P, Q, R, S, T
0.581 H, I, J, K, L, M
1.047 A, B, C, D, E, F, G
Regarding windows, this study identied the distinguishable materials, assessed the
number of glass layers, and measured the glass thickness. In addition, to understand the
heat transfer coecients and compositions of the windows built in each year, we referred to
the Korea Land and Housing Corporation’s report “Research on Sound Insulation Design
of External Windows”, the literature, and the energy‑saving design standards [8,10–13].
Table 7summarizes the compositions and heat transfer coecients of the windows
estimated based on the visual inspection results and the information contained in the ref‑
erence materials.
Table 7. Results of a survey of major window compositions and heat transfer coecients of 20 senior
centers in Seoul metropolitan area.
Window Insulation Standard
(Heat Transfer Coecient U: W/m2K) Window Composition Senior Center
1.8 22 mm (5 mm + 12 mm (Air) + 5 mm), PVC E, F, R
2.1 16 mm (5 mm + 6 mm (Low‑E) + 5 mm), PVC C, H
2.4 16 mm (5 mm + 6 mm (Air) + 5 mm), PVC A, B, G, I, J, M, O, P
10 mm (5 mm + 5 mm), PVC D, K, L
4 16 mm (5 mm + 6 mm (Air) + 5 mm), AL N, Q, S
6.6 5 mm, AL T
A visual inspection conrmed that among the 20 senior centers, with the exception of
the D, J, K, L, N, Q, S, and T senior centers, the other buildings had undergone window
improvement work at least once in their lifetimes. It was conrmed that the improved
polyvinyl chloride (PVC)‑framed windows were superior to the aluminum or wooden win‑
dows that were typically installed at the time of building completion.
The heat transfer coecients and compositions of the doors in these buildings were
checked via a visual inspection and by referring to the “Energy saving plan design criteria”,
as listed in Table 8.
Hence, the architectural factors related to the energy improvement of the senior center
were identied through eld surveys, the literature review, and the legal history.
Energies 2024,17, 5576 7 of 29
Table 8. Results of an investigation of major door compositions and heat transfer coecients of
20 senior centers in Seoul metropolitan area.
Door Insulation Standard
(Heat Transfer Coecient U:
W/m2K)
Door Composition Senior Center
2.4 Wood J
2.7 Steel H, K, L, M, N, O, P, Q, R, S
5.5 Single glaze A, B, C, D, E, F, G, I, T
4.1.2. Building Equipment Status
The equipment installed in the senior centers was investigated. The boilers which af‑
fect heating energy consumption, were analyzed, and it was found that new and renewable
equipment was not installed in all 20 senior centers.
Table 9lists the main boiler specications determined via the visual inspections and
the aached boiler construction signs. In addition, the visual inspections conrmed that
electric boilers were installed as auxiliary equipment in addition to the main boilers.
Table 9. Major boilers installed in 20 senior centers in Seoul metropolitan area.
Boiler Type Eciency (%) Senior Center
Gas 80–83 A, B, C, D, E, F, H, I, J, K, M, N, O, Q, R, S, T
Condensing 86–87 G, L, P
A thorough examination was conducted on the equipment installed within the senior
centers. This investigation specically focused on boilers, which are known to signicantly
impact heating energy consumption. The ndings revealed that none of the 20 senior cen‑
ters had incorporated new and renewable energy systems. Table 9lists the boiler specica‑
tions determined via the visual inspections and the aached boiler construction signs. In
addition, the visual inspections conrmed that electric boilers were installed as auxiliary
equipment in addition to the main boilers.
4.2. Energy Performance Diagnosis—Method
To conrm the changes in building energy performance after the improvements to
the architectural and facility factors of the 20 senior centers, the dierences in the primary
energy consumption per unit area of these buildings were analyzed before and after the
implementation of the improvements to each building. To assess the variations in primary
energy consumption, ECO2s, a building energy simulation program extensively utilized
in Korea, were conducted.
ECO2 is a window‑based program that calculates building energy requirements and
consumption using monthly average weather data. When inputs such as building ori‑
entation, architectural features, mechanical systems, renewable energy, building usage,
operating hours, and management methods are provided, the program can estimate the
building’s monthly energy requirements and predict its energy consumption. Energy con‑
sumption is categorized into heating, cooling, hot water, lighting, and ventilation. These
calculations can then be used to project the building’s primary energy consumption and
carbon dioxide emissions [14].
4.2.1. Simulation Methods
ECO2 is a building energy consumption assessment program based on ISO 13790 [15]
and DIN 18599 [14,16–20]. The ECO2 program is mainly used to perform building energy
and zero‑energy building assessments [21]. It has also been used for comparative analyses
of actual energy consumption and other program results in several studies [22–25]. Thus,
this study examined the pre‑ and post‑renovation energy demand, energy consumption,
Energies 2024,17, 5576 8 of 29
and primary energy consumption of the exterior walls, roofs, windows, and boilers in
20 senior centers that were the recipients of domestic support projects.
4.2.2. Selection of Improvement Factors—Methods
The selection of improvement factors, namely exterior wall insulation, roof insula‑
tion, windows, doors, and boilers for the senior centers, was based on prior identications
within this study. The selection of improvement factors was conducted via a literature re‑
view to conrm the eects before and after energy performance improvement, as shown
in Table 10.
Table 10. Comparison of the literature review—selection of improvement factors for energy perfor‑
mance improvement.
Reference Contents
[26–28] Energy improvement eect of PF (phenolic foam) board exterior wall insulation
[29] A study on materials for improved energy conservation in buildings—PF (phenolic foam) board
Major topic Improvement of exterior wall insulation
[30,31] Energy improvement eect of PF (phenolic foam) board roof insulation
[29] A study on materials for improved energy conservation in buildings—PF (phenolic foam) board
Major topic Improvement of roof insulation
[24] Thermal transmiance rate by window type
[32–34] A study on windows for improved energy conservation in buildings—low‑e, double‑glazed glass
Major topic Improvement of window
[24] Thermal transmiance rate by door type
Major topic Improvement of door
[35–37] A study on boilers for improved energy conservation in buildings—condensing boiler
Major topic Improvement of boiler
This work Selection of improvement factors for energy performance improvement
To improve energy performance, options such as reducing the window area, installing
shading devices, adding windproof structures, installing nighime insulation devices for
the windows, implementing inverter control for the rotating equipment, applying LED
lighting, and using standby power cut‑o outlets could be considered. However, in this
study, these measures were not considered due to the diculty of applying them to small,
aging buildings.
4.3. Energy Consumption by Improvement Factor
The improvement factors selected through the literature review were applied to the
ECO2 simulations to compare the energy demand, energy consumption, and primary en‑
ergy consumption before and after the improvement of the exterior walls, roofs, windows,
and boilers.
4.3.1. Wall Insulation
The changes in building energy performance after the application of the exterior wall
improvements to the 20 senior centers were analyzed. The exterior wall congurations
utilized in the ECO2 simulation input were based on the exterior wall conditions outlined
in Table 2. The conditions for post‑improvement of the exterior walls involved the appli‑
cation of widely used PF boards, ensuring re resistance performance as required by the
relevant regulations [38–40]. Table 11 lists the analysis results of the energy demand, en‑
ergy consumption, and primary energy consumption before and after the application of PF
boards for the improving the external walls. The average decrease in primary energy con‑
sumption after the application of the external wall improvements was found to be 25.9%.
The senior center with the least improvement eect was analyzed as O. The median value
of the primary energy consumption reduction was 14.6% and was with the K‑senior center.
Energies 2024,17, 5576 9 of 29
Table 11. ECO2 simulation results for energy demand, energy consumption, and primary energy
consumption before and after the application of external wall improvements.
Improvement
Eect
(Analysis of 20 Models)
Demand or Consumption per Unit Area per Year (Unit: kWh/m2y)
Energy Demand Energy Consumption Primary Energy Consumption
Before After Before After Before After Improvement Rate (%)
Lowest O 114.4 104.7 143.5 129.0 201.9 186.4 8.7
Median K 127.3 110.4 174.3 148.5 222.5 194.5 14.6
Highest D 207.8 126.4 296.9 176.7 357.6 225.9 59.3
Average 150.8 116.7 205.3 155.8 260.5 205.6 25.9
Figure 3shows the ECO2 simulation input screen for the K‑senior center, the improve‑
ment rate of which was found to be the median value. The heat transfer coecients of the
exterior walls of the K‑senior center before and after the application of the exterior wall
improvements were 0.581 W/m2K and 0.340 W/m2K, respectively. Therefore, based on
the simulation results, the predicted primary energy consumption per unit area per year
would decrease by 28 kWh/m2y after the application of PF boards for the exterior wall
improvements (Table 11).
Energies 2024, 17, x FOR PEER REVIEW 10 of 30
Figure 3. ECO2 simulation input screen before and after wall improvement at K-senior center.
Table 11. ECO2 simulation results for energy demand, energy consumption, and primary energy
consumption before and after the application of external wall improvements.
Improvement
Effect
(Analysis of 20 Models)
Demand or Consumption Per Unit Area Per Year (Unit: kWh/m
2
y)
Energy Demand Energy Consumption Primary Energy Consumption
Before After Before After Before After Improvement Rate (%)
Lowest O 114.4 104.7 143.5 129.0 201.9 186.4 8.7
Median K 127.3 110.4 174.3 148.5 222.5 194.5 14.6
Highest D 207.8 126.4 296.9 176.7 357.6 225.9 59.3
Average 150.8 116.7 205.3 155.8 260.5 205.6 25.9
4.3.2. Roof Insulation
Table 12 shows the results of analyzing the changes in energy performance due to
roof improvements for the 20 senior centers using ECO2 simulations, with reference to the
values in Table 6. Regarding the material for roof improvement, a PF board, known for its
practical effect of enhancement, was applied [38,39]. The average improvement rate of the
roofs after the application of a PF board was 6.4%, and the N-senior center exhibited the
lowest improvement effect. In addition, the I-senior center exhibited the median improve-
ment effect with an improvement rate of 4.8%. For I senior center, the heat transfer coeffi-
cient values before and after the application of the roof improvements were 0.581 W/m
2
K
and 0.150 W/m
2
K, respectively.
Hence, according to the simulation outcomes, our projections indicate a reduction of
10.5 kWh/m
2
y in the annual primary energy consumption per unit area subsequent to the
implementation of PF boards for roof enhancement (Table 12).
Figure 3. ECO2 simulation input screen before and after wall improvement at K‑senior center.
4.3.2. Roof Insulation
Table 12 shows the results of analyzing the changes in energy performance due to
roof improvements for the 20 senior centers using ECO2 simulations, with reference to the
values in Table 6. Regarding the material for roof improvement, a PF board, known for its
practical eect of enhancement, was applied [38,39]. The average improvement rate of the
roofs after the application of a PF board was 6.4%, and the N‑senior center exhibited the
lowest improvement eect. In addition, the I‑senior center exhibited the median improve‑
ment eect with an improvement rate of 4.8%. For I senior center, the heat transfer coe‑
Energies 2024,17, 5576 10 of 29
cient values before and after the application of the roof improvements were 0.581 W/m2K
and 0.150 W/m2K, respectively.
Table 12. ECO2 simulation results of energy demand, energy consumption, and primary energy
consumption before and after the application of roof improvements.
Improvement
Eect
(Analysis of 20 Models)
Demand or Consumption per Unit Area per Year (Unit: kWh/m2y)
Energy Demand Energy Consumption Primary Energy Consumption
Before After Before After Before After Improvement Rate (%)
Lowest N 142.4 139.1 201.3 196.7 247.8 237.7 4.2
Median I 125.4 118.8 176.2 166.7 230.9 220.4 4.8
Highest G 234.5 207.8 290.9 255 371.2 330.8 12.2
Average 150.8 140.4 205.3 190.9 260.5 243.4 6.4
Hence, according to the simulation outcomes, our projections indicate a reduction of
10.5 kWh/m2y in the annual primary energy consumption per unit area subsequent to the
implementation of PF boards for roof enhancement (Table 12).
4.3.3. Window
For windows, an ECO2 simulation analysis was conducted for senior centers D, J, K,
L, N, Q, S, and T, excluding the 12 centers where window improvement projects had al‑
ready been carried out. For the post‑window improvement condition, low‑e double‑glazed
glass and PVC frames, currently the most widely utilized in energy‑saving design stan‑
dards, were implemented [11,41–43]. According to the results of the ECO2 simulations,
the average window improvement rate was 8.0%, and the J‑senior center exhibited the
least improvement. The K‑senior center exhibited the median improvement eect with an
improvement rate of 4.4% (Table 13).
Table 13. ECO2 simulation results of energy demand, energy consumption, and primary energy
consumption before and after the application of window improvements.
Improvement
Eect
(D, J, K, L, N, Q, S, T)
Demand or Consumption per Unit Area per Year (Unit: kWh/m2y)
Energy Demand Energy Consumption Primary Energy Consumption
Before After Before After Before After Improvement Rate (%)
Lowest J 120.3 119.6 171.1 170.9 224.8 224.3 0.1
Median K 127.3 121.1 174.3 167.0 222.5 213.7 4.4
Highest S 137.0 110.5 170.9 139.7 222.5 185.6 22.3
Average 141.5 130.6 195.0 181.6 247.2 231.4 8.0
The windows of the K‑senior center were composed of 10 mm‑thick (5 mm + 5 mm)
PVC, and the heat transfer coecient was 3.1 W/m2K. After the improvement with low‑e,
double‑glazed glass, the heat transfer coecient became 2.1 W/m2K. Therefore, based on
the simulation results, we anticipated that the primary energy consumption per unit area
per year would diminish by 8.8 kWh/m2y with the window enhancement measures.
4.3.4. Door
ECO2 simulations were performed on the door systems of 17 senior centers, with
reference to the values in Table 8. The senior centers designated as C, F, and N, where
structural alterations to the doors were unfeasible, were excluded from the analysis. As for
the conditions after the door improvements, the heat transfer coecients were applied by
referring to the energy‑saving design standard. For the door materials, the same materials
as before the improvements were applied, considering the structural characteristics. [11].
Energies 2024,17, 5576 11 of 29
The ECO2 simulation results indicated that the average door improvement rate was
1.8%, and the R‑senior center exhibited the lowest improvement. Among the 17 senior
centers considered in this analysis, the S‑senior center exhibited the median value, and the
corresponding improvement rate was 1.2% (Table 14).
Table 14. ECO2 simulation results of energy demand, energy consumption, and primary energy
consumption before and after door improvements.
Improvement
Eect
(A, B, D, E, G, H, I, J, K, L,
M, N, O, P, Q, R, S, T)
Demand or Consumption per Unit Area per Year (Unit: kWh/m2y)
Energy Demand Energy Consumption Primary Energy Consumption
Before After Before After Before After Improvement Rate (%)
Lowest D 207.8 206.5 296.9 295.4 357.6 355.6 0.6
Median S 137.0 135.4 170.9 168.3 222.5 219.8 1.2
Highest R 115.0 110.5 160.7 154.1 210.7 203.5 3.5
Average 146.3 143.8 199.8 196.7 255.2 250.5 1.8
The doors used in the S‑senior center were made of steel, and the overall heat trans‑
fer coecient was 2.7 W/m2K. After the door improvements, the applied overall heat
transfer coecient of the doors was 1.7 W/m2K. The simulation predicts that the annual
primary energy consumption per unit area will decrease by 2.7 kWh/㎡y when applying
door renovations.
4.3.5. Boiler
An ECO2 simulation analysis was conducted on 17 senior centers, excluding G, L, and
P, where condensing boilers was already applied, as referenced in Table 9. The conditions
for boiler improvement involved the application of condensing boilers characterized by
low nitrogen oxide emissions and a high thermal eciency [44,45].
The ECO2 simulation results indicated that the average improvement rate was 6.5%,
as listed in Table 15, and the C‑senior center exhibited the lowest improvement eect. The
N‑senior center showed the median improvement eect among the 17 senior centers, with
a corresponding improvement rate of 8.2% (Table 15).
Table 15. ECO2 simulation analysis results of boilers before and after improvements: energy de‑
mand, energy consumption, and primary energy consumption.
Improvement
Eect
(A, B, C, D, E, F, H, I, J, K,
M, N, O, Q, R, S, T)
Demand or Consumption per Unit Area per Year (Unit: kWh/m2y)
Energy Demand Energy Consumption Primary Energy Consumption
Before After Before After Before After Improvement Rate (%)
Lowest C 209.1 209.1 269.4 262.5 337.8 331.3 2.0
Median N 142.4 142.4 201.3 187.8 247.8 229.0 8.2
Highest I 125.4 125.4 176.2 158.3 230.9 212.0 8.9
Average 149.0 149.0 202.8 188.9 257.0 241.6 6.5
Figure 4shows the ECO2 simulation input screens of the N‑senior center, which ex‑
hibited the median improvement eect. In the N‑senior center, 18.6 kW gas boilers were
installed on both the rst and second oors before the improvement. After the improve‑
ment, they were replaced with condensing boilers of the same capacities. Therefore, the
simulation predicts that the annual primary energy consumption per unit area will de‑
crease by 18.8 kWh/㎡·y with the boiler renovation.
Energies 2024,17, 5576 12 of 29
Energies 2024, 17, x FOR PEER REVIEW 13 of 30
Figure 4. ECO2 simulation input screens of N-senior center before and after boiler improvement.
5. Standard Models
Subsequently, a standard model for the senior centers was selected to validate the
earlier ECO2 simulation results and analyze the economic impact of the primary energy
improvement measures.
5.1. Selection of Standard Models
Among the 20 senior centers, the standard models were determined based on the
ECO2 simulation results. Following an analysis of the average characteristics related to
floor area, energy demand, energy consumption, and primary energy consumption,
which had the most significant impact on annual primary energy consumption, the stand-
ard models were selected, as outlined in Table 16. Table 16 specifies the average character-
istics, designated as J, K, and S, for each condition, representing the standard models for
the senior centers.
Table 16. Selected standard models based on median values of each category of 20 senior centers in
the metropolitan area.
Division Standard Model
Total floor area S
Annual energy requirement per unit area
Annual energy consumption per unit area K
Annual primary energy consumption per unit area J
Table 17 shows the results of the ECO2 analysis of the primary energy consumption
before and after the energy performance improvements were applied to the standard
models J, K, and S. According to the results of a standard model energy performance im-
provement analysis, as shown in Figure 5, the improvements to the exterior walls had the
Figure 4. ECO2 simulation input screens of N‑senior center before and after boiler improvement.
5. Standard Models
Subsequently, a standard model for the senior centers was selected to validate the
earlier ECO2 simulation results and analyze the economic impact of the primary energy
improvement measures.
5.1. Selection of Standard Models
Among the 20 senior centers, the standard models were determined based on the
ECO2 simulation results. Following an analysis of the average characteristics related to
oor area, energy demand, energy consumption, and primary energy consumption, which
had the most signicant impact on annual primary energy consumption, the standard mod‑
els were selected, as outlined in Table 16. Table 16 species the average characteristics,
designated as J, K, and S, for each condition, representing the standard models for the
senior centers.
Table 16. Selected standard models based on median values of each category of 20 senior centers in
the metropolitan area.
Division Standard Model
Total oor area
S
Annual energy requirement per unit area
Annual energy consumption per unit area K
Annual primary energy consumption per unit area J
Table 17 shows the results of the ECO2 analysis of the primary energy consumption be‑
fore and after the energy performance improvements were applied to the standard models
J, K, and S. According to the results of a standard model energy performance improvement
Energies 2024,17, 5576 13 of 29
analysis, as shown in Figure 5, the improvements to the exterior walls had the strongest ef‑
fects on models J and K, and the window improvements had the strongest eect on model
S as shown by doed boxes. Overall, the element that showed the greatest improvement
was determined to be the windows.
Table 17. ECO2 simulation results showing primary energy consumption after application of im‑
provements to outer walls, roofs, windows, doors, and boilers in standard models J, K, and S.
Standard
Model
Annual Primary Energy Consumption per Unit Area (Unit: kWh/m2y)
Wall Roof Window Door Boiler
Before After Before After Before After Before After Before After
J 224.8 202.5 224.80 214.20 224.80 224.30 224.80 220.00 224.80 210.50
K 222.5 194.5 222.50 212.10 222.50 213.70 222.50 215.80 222.50 204.50
S 222.5 203.2 222.50 217.10 222.50 185.60 222.50 219.80 222.50 211.60
Average 223.3 200.1 223.3 214.5 223.3 207.9 223.3 218.5 223.3 208.9
Energies 2024, 17, x FOR PEER REVIEW 14 of 30
strongest effects on models J and K, and the window improvements had the strongest
effect on model S as shown by doed boxes. Overall, the element that showed the greatest
improvement was determined to be the windows.
Table 17. ECO2 simulation results showing primary energy consumption after application of im-
provements to outer walls, roofs, windows, doors, and boilers in standard models J, K, and S.
Standard Model
Annual Primary Energy Consumption per Unit Area (Unit: kWh/m
2
y)
Wall Roof Window Door Boiler
Before After Before After Before After Before After Before After
J 224.8 202.5 224.80 214.20 224.80 224.30 224.80 220.00 224.80 210.50
K 222.5 194.5 222.50 212.10 222.50 213.70 222.50 215.80 222.50 204.50
S 222.5 203.2 222.50 217.10 222.50 185.60 222.50 219.80 222.50 211.60
Average 223.3 200.1 223.3 214.5 223.3 207.9 223.3 218.5 223.3 208.9
Next, the energy performance improvement effect of the window improvements was
verified.
Figure 5. ECO2 primary energy consumption analysis results by applying exterior wall, roof, win-
dow, door, and boiler improvements to standard models J, K, and S: (a) ECO2 primary energy con-
sumption analysis results of standard models J, (b) K, and (c) S. (d) ECO2 simulation results of av-
erage primary energy consumption of standard models J, K, and S.
(a) (b)
(c) (d)
Figure 5. ECO2 primary energy consumption analysis results by applying exterior wall, roof, win‑
dow, door, and boiler improvements to standard models J, K, and S: (a) ECO2 primary energy con‑
sumption analysis results of standard models J, (b) K, and (c) S. (d) ECO2 simulation results of aver‑
age primary energy consumption of standard models J, K, and S.
Energies 2024,17, 5576 14 of 29
Next, the energy performance improvement eect of the window improvements
was veried.
5.2. Verication of Energy Performance Improvement
To validate the improvements observed in the windows with the most signicant im‑
pact, this study selected aged senior centers within the metropolitan area and conducted
simulations and verication experiments. Figure 6presents diagrams of the α‑ and β‑
senior centers, where the verication experiment was conducted. To determine the compo‑
sition of the windows in the α‑ and β‑senior centers, the window thickness was measured
using an EDTM Glass‑Chek Pro GC3000 instrument. As shown in Table 18, the windows
of the α‑senior center were 6 mm thick and composed of PVC frames, while the windows
of the β‑senior center were 6 mm thick and composed of aluminum frames. For the win‑
dow and door improvements, a heat transfer coecient of 1.8 W/m2K, an overall thickness
of 22 mm (5 mm + 12 mm (Air) + 5 mm), and PVC frames were applied.
Energies 2024, 17, x FOR PEER REVIEW 15 of 30
5.2. Verification of Energy Performance Improvement
To validate the improvements observed in the windows with the most significant
impact, this study selected aged senior centers within the metropolitan area and con-
ducted simulations and verification experiments. Figure 6 presents diagrams of the α- and
β-senior centers, where the verification experiment was conducted. To determine the com-
position of the windows in the α- and β-senior centers, the window thickness was meas-
ured using an EDTM Glass-Chek Pro GC3000 instrument. As shown in Table 18, the
windows of the α-senior center were 6 mm thick and composed of PVC frames, while the
windows of the β-senior center were 6 mm thick and composed of aluminum frames. For
the window and door improvements, a heat transfer coefficient of 1.8 W/m2K, an overall
thickness of 22 mm (5 mm + 12 mm (Air) + 5 mm), and PVC frames were applied.
(a) (b)
Figure 6. Drawing of blower door test site: (a) α-senior center with an area of 114 m2 and (b) β-senior
center with an area of 115 m2.
Table 18. Construction completion years of α- and β-senior centers, and compositions of the applied
windows.
Senior Center Construction
Completion Year
Window Insulation Standard
(𝐓𝐡𝐞𝐫𝐦𝐚𝐥 𝐓𝐫𝐚𝐧𝐬𝐦𝐢𝐭𝐭𝐚𝐧𝐜𝐞 𝑼: W/m2K) Window Composition
α 1996 5.3 6 mm, PVC
β 2000 6.6 6 mm, AL
5.2.1. Simulation Analysis
To verify the improvement effect, an ECO2 simulation analysis was conducted. Ac-
cording to the ECO2 simulation, in the case of the α-senior center, the heating energy im-
provement rate of primary energy consumption was 28.5%, as illustrated in Figure 7. In
the case of the β-senior center, the heating energy improvement rate of primary energy
consumption was 17.1%, as illustrated in Figure 8. Therefore, the total energy demand and
consumption improved when the windows were improved. The energy improvement had
a greater impact on heating energy consumption than on cooling energy consumption
[46,47].
In this study, the results of the heating energy performance improvements by simu-
lation were verified by measuring the airtightness before and after the window improve-
ments [26,27,48].
Figure 6. Drawing of blower door test site: (a)α‑senior center with an area of 114 m2and (b)β‑senior
center with an area of 115 m2.
Table 18. Construction completion years of α‑ and β‑senior centers, and compositions of the applied
windows.
Senior Center Construction
Completion Year
Window Insulation
Standard
(Thermal Transmittance U:
W/m2K)
Window
Composition
α1996 5.3 6 mm, PVC
β2000 6.6 6 mm, AL
5.2.1. Simulation Analysis
To verify the improvement eect, an ECO2 simulation analysis was conducted. Ac‑
cording to the ECO2 simulation, in the case of the α‑senior center, the heating energy im‑
provement rate of primary energy consumption was 28.5%, as illustrated in Figure 7. In the
case of the β‑senior center, the heating energy improvement rate of primary energy con‑
sumption was 17.1%, as illustrated in Figure 8. Therefore, the total energy demand and
consumption improved when the windows were improved. The energy improvement
had a greater impact on heating energy consumption than on cooling energy consump‑
tion [46,47].
Energies 2024,17, 5576 15 of 29
Energies 2024, 17, x FOR PEER REVIEW 16 of 30
Figure 7. ECO2 simulation results of α-senior center after window improvements.
Figure 8. ECO2 simulation results of β -senior center after window improvements.
5.2.2. Blower Door Test
The improvement in airtightness due to window improvements affects both the heat-
ing energy demand and consumption. The strong correlation between airtightness and
energy consumption was confirmed by Jeon [48] and Choi [26]. Therefore, airtightness
measurement experiments were conducted to verify the energy performance improve-
ments of the windows and doors. The measurement was taken in the male room (39.3 m²)
of the α-senior center and the male room (16.9 m²) of the β-senior center, as shown in
Figure 6. The airtightness test was conducted after installing a blower fan (TEC Minneap-
olis Blower Door™ System, Minneapolis, MN, USA) at the door, as shown in (a) and (c)
of Figure 9. The blower door test was conducted under indoor and outdoor pressure dif-
ference conditions ranging from 10 to 65 Pa, following the positive/negative pressuriza-
tion method (ASTM E779) [28–30]. The airtightness was measured more than eight times
using the pressurization and depressurization methods for the pressure range of 10–65
Pa.
Figure 7. ECO2 simulation results of α‑senior center after window improvements.
Energies 2024, 17, x FOR PEER REVIEW 16 of 30
Figure 7. ECO2 simulation results of α-senior center after window improvements.
Figure 8. ECO2 simulation results of β -senior center after window improvements.
5.2.2. Blower Door Test
The improvement in airtightness due to window improvements affects both the heat-
ing energy demand and consumption. The strong correlation between airtightness and
energy consumption was confirmed by Jeon [48] and Choi [26]. Therefore, airtightness
measurement experiments were conducted to verify the energy performance improve-
ments of the windows and doors. The measurement was taken in the male room (39.3 m²)
of the α-senior center and the male room (16.9 m²) of the β-senior center, as shown in
Figure 6. The airtightness test was conducted after installing a blower fan (TEC Minneap-
olis Blower Door™ System, Minneapolis, MN, USA) at the door, as shown in (a) and (c)
of Figure 9. The blower door test was conducted under indoor and outdoor pressure dif-
ference conditions ranging from 10 to 65 Pa, following the positive/negative pressuriza-
tion method (ASTM E779) [28–30]. The airtightness was measured more than eight times
using the pressurization and depressurization methods for the pressure range of 10–65
Pa.
Figure 8. ECO2 simulation results of β‑senior center after window improvements.
In this study, the results of the heating energy performance improvements by simu‑
lation were veried by measuring the airtightness before and after the window improve‑
ments [26,27,48].
5.2.2. Blower Door Test
The improvement in airtightness due to window improvements aects both the heat‑
ing energy demand and consumption. The strong correlation between airtightness and en‑
ergy consumption was conrmed by Jeon [48] and Choi [26]. Therefore, airtightness mea‑
surement experiments were conducted to verify the energy performance improvements of
the windows and doors. The measurement was taken in the male room (39.3 m2) of the α‑
senior center and the male room (16.9 m2) of the β‑senior center, as shown in
Figure 6. The airtightness test was conducted after installing a blower fan (TEC Minneapo‑
lis Blower Door™ System, Minneapolis, MN, USA) at the door, as shown in (a) and (c) of
Figure 9. The blower door test was conducted under indoor and outdoor pressure dier‑
ence conditions ranging from 10 to 65 Pa, following the positive/negative pressurization
Energies 2024,17, 5576 16 of 29
method (ASTM E779) [28–30]. The airtightness was measured more than eight times using
the pressurization and depressurization methods for the pressure range of 10–65 Pa.
Energies 2024, 17, x FOR PEER REVIEW 17 of 30
(a) (b)
(c) (d)
Figure 9. Experimental setup and results of blower door test before window construction: (a) blower
door test conducted at α-senior center, and (b) test results at α-senior center. (c) Blower door test
conducted at β-senior center, and (d) test results at β-senior center.
According to the results of the blower door test conducted before the window im-
provements, the building infiltration in the α-senior center was 2271 m³/h with a pressure
difference of 50 Pa during depressurization, and 3435 m³/h with a pressure difference of
50 Pa during pressurization. In the β-senior center, the building infiltration was 1693 m3/h
with a pressure difference of 50 Pa during depressurization and 2945 m3/h with a pressure
difference of 50 Pa during pressurization.
The results of the blower door test conducted after the window improvements are
depicted in Figure 10. In the α-senior center, the building infiltration was 1310 m³/h with
a pressure difference of 50 Pa during depressurization, and 1636 m³/h with a pressure
difference of 50 Pa during pressurization. In the β-senior center, the building infiltration
was 1461 m³/h with a pressure difference of 50 Pa during depressurization, and 2724 m³/h
with a pressure difference of 50 Pa during pressurization.
Figure 9. Experimental setup and results of blower door test before window construction: (a) blower
door test conducted at α‑senior center, and (b) test results at α‑senior center. (c) Blower door test
conducted at β‑senior center, and (d) test results at β‑senior center.
According to the results of the blower door test conducted before the window im‑
provements, the building inltration in the α‑senior center was 2271 m3/h with a pressure
dierence of 50 Pa during depressurization, and 3435 m3/h with a pressure dierence of
50 Pa during pressurization. In the β‑senior center, the building inltration was 1693 m3/h
with a pressure dierence of 50 Pa during depressurization and 2945 m3/h with a pressure
dierence of 50 Pa during pressurization.
The results of the blower door test conducted after the window improvements are
depicted in Figure 10. In the α‑senior center, the building inltration was 1310 m3/h with
a pressure dierence of 50 Pa during depressurization, and 1636 m3/h with a pressure
dierence of 50 Pa during pressurization. In the β‑senior center, the building inltration
was 1461 m3/h with a pressure dierence of 50 Pa during depressurization, and 2724 m3/h
with a pressure dierence of 50 Pa during pressurization.
Energies 2024,17, 5576 17 of 29
Energies 2024, 17, x FOR PEER REVIEW 18 of 30
(a) (b)
(c) (d)
Figure 10. Blower door test after window improvement: (a) photograph of window constructed in
α-senior center, and (b) blower door test results under depressurization and pressurization at α-
senior center. (c) Photograph of window constructed in β-senior center, and (d) blower door test
results under depressurization and pressurization at β-senior center.
The results of the blower door test for the α- and β-senior centers were analyzed and
converted into air change per hour (ACH) as shown in Table 19.
Table 19. Air change per hour (ACH) of α- and β-senior centers according to the blower door test
results. Pressure difference = 50 Pa.
Category Depressurization
ACH (1/h)
Pressurization
ACH (1/h)
Average
ACH (1/h)
α-senior center
Before 25.16 31.18 28.17
After 11.90 14.86 13.38
Improvement rate 52.70% 52.34% 52.50%
β-senior center
Before 39.21 68.19 53.70
After 33.83 63.07 48.45
Improvement rate 13.72% 7.51% 9.78%
The measured air change per hour (ACH) showed an improvement of 52.34% for the
α-senior center, which was approximately 35.24% higher than the simulation result. For
the β-senior center, the measured improvement was approximately 4.62% lower than the
simulated improvement effect of 9.78%. Therefore, we concluded that the ACH could be
improved by at least 9.78% by replacing the existing single-pane windows.
Therefore, when prioritizing energy performance improvement factors in senior cen-
ters, an energy consumption analysis through a detailed diagnosis of each performance
Figure 10. Blower door test after window improvement: (a) photograph of window constructed in α‑
senior center, and (b) blower door test results under depressurization and pressurization at α‑senior
center. (c) Photograph of window constructed in β‑senior center, and (d) blower door test results
under depressurization and pressurization at β‑senior center.
The results of the blower door test for the α‑ and β‑senior centers were analyzed and
converted into air change per hour (ACH) as shown in Table 19.
Table 19. Air change per hour (ACH) of α‑ and β‑senior centers according to the blower door test
results. Pressure dierence = 50 Pa.
Category Depressurization
ACH (1/h)
Pressurization
ACH (1/h)
Average
ACH (1/h)
α‑senior center
Before 25.16 31.18 28.17
After 11.90 14.86 13.38
Improvement
rate 52.70% 52.34% 52.50%
β‑senior center
Before 39.21 68.19 53.70
After 33.83 63.07 48.45
Improvement
rate 13.72% 7.51% 9.78%
Energies 2024,17, 5576 18 of 29
The measured air change per hour (ACH) showed an improvement of 52.34% for the
α‑senior center, which was approximately 35.24% higher than the simulation result. For
the β‑senior center, the measured improvement was approximately 4.62% lower than the
simulated improvement eect of 9.78%. Therefore, we concluded that the ACH could be
improved by at least 9.78% by replacing the existing single‑pane windows.
Therefore, when prioritizing energy performance improvement factors in senior cen‑
ters, an energy consumption analysis through a detailed diagnosis of each performance
improvement factor is the most reliable method, but if a detailed diagnosis is dicult, it is
suggested to carry out improvements based on the process presented in this study.
The increase in a heating load due to an inltration is calculated using the following
equation. .
Q=ρcpV(ti−to)(1)
Here, ρrepresents the density, cpdenotes the specic heat of air at a constant pressure,
nis the air changes per hour, V is the volume of the space, tiis the indoor temperature, and
tois the outdoor temperature. The indoor temperature is assumed to be 26 ◦C.
The reduction in the heating load due to the inltration improvement is
33.1 kWh/(m2y) from the ECO2 simulation and 32.1 kWh/(m2y) from the blower door
test. As shown in Table 20, the two results show an error of 3.0%, indicating that the ECO2
simulation accurately predicts the actual heating load.
Table 20. Conditions of αcenter for heating load calculations due to inltrations.
Average
Outdoor
(◦C)
Air
Density
(kg/m3)
Operation
Time per
Day (h)
Day of
Use per
Month
(d)
ACH
50 Pa
(1/h)
ACH
10 Pa
(1/h)
Inltration
(cmh)
Heat Loss
(kWh)
Annual Heat
Loss per Area
(kWh/m2y)
December Before 0.9 1.287 821 28.17 5.634 509.26 770.78 19.61
After 13.38 2.676 241.88 366.10 9.32
January Before −2.1 1.301 822 28.17 5.634 509.26 913.83 23.25
After 13.38 2.676 241.88 434.04 11.04
February Before 0.2 1.290 819 28.17 5.634 509.26 718.49 18.28
After 13.38 2.676 241.88 341.26 8.68
As described in Figure 11, the rst step is to check whether the window has been
remodeled through a visual inspection. Next, if the windows have not been remodeled,
priority is given to improving the windows, and if the windows have already been re‑
modeled, improvement of the exterior wall insulation, replacement with a high‑eciency
condensing boiler, and replacement of the roof insulation are suggested.
This study also included an economic analysis of the standard model to analyze not
only the eect of improving energy performance, but also the economic burden caused by
the construction.
Energies 2024,17, 5576 19 of 29
Energies 2024, 17, x FOR PEER REVIEW 20 of 30
Figure 11. Energy performance improvement factor selection step considering the energy improve-
ment effect.
5.3. Economic Analysis of the Standard Model
To analyze the economic effect of improvements, the annual heating energy cost and
life cycle cost of the standard model were conducted [31–34].
In the economic analysis, product price, labor cost, demolition cost, additional costs
(asbestos removal costs, structural maintenance costs, electric capacity expansion costs,
etc.), and waste disposal cost were considered [34,35].
The unit cost analysis results for the J-senior center are shown in Table 21, for the S-
senior center in Table 22, and for the K-senior center in Table 23. The results show that the
highest improvement construction cost is for the exterior wall, followed by the window,
roof, and boiler, respectively.
Table 21. Unit cost analysis of energy improvement construction in J-senior center.
Wall
(Area:
173.28 m
2
)
Window
(Area:
15.17 m
2
)
Roof
(Area:
43.68 m
2
)
Boiler
(18.6, 34.9 kW)
Product price 20,152 3795 2323 1115
(153,315 KRW/m
2
) (329,791 KRW/m
2
) (70,121 KRW/m
2
)
Labor cost 2592 628 207 358
(37,429 KRW/m
2
) (54,572 KRW/m
2
) (6256 KRW/m
2
)
Demolition cost 2328 158 587 215
Additional cost 6823 1327 759 -
Waste disposal cost 682 133 76 44
Total cost 32,577 6041 3953 1732
Costs as of 18 April 2023 (unit: USD 1 = KRW 1318.3).
Figure 11. Energy performance improvement factor selection step considering the energy improve‑
ment eect.
5.3. Economic Analysis of the Standard Model
To analyze the economic eect of improvements, the annual heating energy cost and
life cycle cost of the standard model were conducted [31–34].
In the economic analysis, product price, labor cost, demolition cost, additional costs
(asbestos removal costs, structural maintenance costs, electric capacity expansion costs,
etc.), and waste disposal cost were considered [34,35].
The unit cost analysis results for the J‑senior center are shown in Table 21, for the S‑
senior center in Table 22, and for the K‑senior center in Table 23. The results show that the
highest improvement construction cost is for the exterior wall, followed by the window,
roof, and boiler, respectively.
Table 21. Unit cost analysis of energy improvement construction in J‑senior center.
Wall
(Area:
173.28 m2)
Window
(Area:
15.17 m2)
Roof
(Area:
43.68 m2)
Boiler
(18.6, 34.9 kW)
Product price
20,152 3795 2323
1115
(153,315
KRW/m2)
(329,791
KRW/m2)
(70,121
KRW/m2)
Labor cost
2592 628 207
358
(37,429
KRW/m2)
(54,572
KRW/m2)(6256 KRW/m2)
Demolition cost 2328 158 587 215
Additional cost 6823 1327 759 ‑
Waste disposal
cost 682 133 76 44
Total cost 32,577 6041 3953 1732
Costs as of 18 April 2023 (unit: USD 1 = KRW 1318.3).
Energies 2024,17, 5576 20 of 29
Table 22. Unit price analysis of energy improvement construction in S‑senior center.
Wall
(Area:
132.35 m2)
Window
(Area:
48.67 m2)
Roof
(Area:
29.9 m2)
Boiler
(18.6, 18.6 kW)
Product price
15,392 12,175 1590
956
(153,315
KRW/m2)
(329,791
KRW/m2)
(70,121
KRW/m2)
Labor cost
1979 2015 142
314
(37,429
KRW/m2)
(54,572.3
KRW/m2)(6256 KRW/m2)
Demolition cost 1778 278 402 188
Additional cost 5211 4257 520 ‑
Waste disposal
cost 521 426 52 188
Total cost 24,882 19,151 2706 1646
Costs as of 18 April 2023 (unit: USD 1 = KRW 1318.3).
Table 23. Energy improvement construction unit price analysis results of K‑senior center.
Wall
(Area:
132.35 m2)
Window
(Area:
16.87 m2)
Roof
(Area:
51.72 m2)
Boiler
(22.8 kW)
Product price
13,921 4220 2751
546
(153,315
KRW/m2)
(329,791
KRW/m2)
(70,121
KRW/m2)
Labor cost
1790 698 245
169
(37,429
KRW/m2)
(54,572.3
KRW/m2)(6256 KRW/m2)
Demolition cost 1608 118 695 102
Additional cost 4713 1476 899 ‑
Waste disposal
cost 471 148 90 21
Total cost 22,504 6660 4680 839
Costs as of 18 April 2023 (unit: USD 1 = 1318.3 KRW).
As shown in the table, the construction costs for improving elderly facility elements
are substantial. However, when undertaking energy performance improvement projects
for elderly care facilities, the government provides partial nancial support for the con‑
struction costs [36,37]. Currently, in order to reduce greenhouse gas emissions, the gov‑
ernment is implementing green remodeling policies and energy improvement projects in
facilities for the elderly and children [49–51]. Therefore, participation in various govern‑
ment support projects is expected to reduce the construction costs of such improvements.
The lifecycle analysis of this study excluded the asset value appreciation rate, focusing
solely on heating energy consumption, which accounts for the highest usage [31,33,34,52].
The heating energy, maintenance, and replacement costs were analyzed using the present
value analysis method, taking into account the ination rate [1].
To determine the annual heating consumption rate, the primary heating energy con‑
sumption obtained from the ECO2 simulation was computed [31,34,53]. Heating energy
costs were calculated by analyzing the consumption of primary fuel, LNG, and electricity.
Equations (2) and (3) were used for the annual heating energy consumption per unit
area to derive the gas heating rates and power usage rates.
Energies 2024,17, 5576 21 of 29
The formula for the gas heating cost is given by Equation (2).
Cf=Ca f Ehec,fAs(2)
The variables in Equation (2) are dened as follows:
Cf: Heating energy cost (USD).
Ca f : Average gas heating unit cost (USD/Mcal).
Ehec,f: Annual heating energy consumption per unit area (kWh/m2).
As: Total oor area of the senior center (m2).
The gas unit price was determined based on the seasonal gas cost in the metropoli‑
tan area, 0.0620 USD/Mcal during the winter season and 0.0613 USD/Mcal for the other
seasons [54].
The formula for the electric power cost is expressed as Equation (3).
Cp=Cap Ehec,pAs+CB(3)
The variables in Equation (3) are dened as follows:
Cp: Electric power energy cost (USD).
Cap : Average electric power unit cost (USD/kWh).
Ehec,p: Annual heating energy consumption per unit area (kWh/m2).
As: Total oor area of the senior center (m2).
CB: Additional factor over basic cost (USD).
In Equation (3), an electric unit cost of 0.074 USD/kWh was applied by referring to
seasonal electricity rate data, and for a base rate of CB, USD 0.690 was applied. By using
Equations (2) and (3), the energy costs according to the annual heating energy consumption
per unit area before and after the improvements were calculated [31,34,53].
In this study, a life cycle cost analysis was conducted for a 40‑year period. In accor‑
dance with the building’s age, remodeling or reconstruction of the exterior walls, windows,
and roof was undertaken [54–57]. For the life cycle cost analysis, the present value coef‑
cient and the present value coecient of annuity were computed using Equations (4)
and (5).
The present value coecient formula (3) was applied to the equipment maintenance
and replacement costs. The assumption is made that boiler replacements occur every ten
years. Therefore, it was assumed that boiler replacements occurred four times during a
40‑year lifespan [58].
The discount rate of 1.89%, representing the average annual increase in household
gas and electricity taris from 2012 to 2022, was applied to calculate the present value
coecient and annuity present value coecient [1].
The present value coecient formula is expressed as Equation (4).
FC=1
(1+r)n(4)
The variables in Equation (4) are dened as follows:
FC: Present value coecient (‑).
r: Average consumer price ination rate between 2012 and 2022 (‑).
The formula for the annuity present value coecient is given in Equation (5).
FEC =(1+r)n−1
r(1+r)n(5)
The variables in Equation (5) are dened as follows:
FEC: Annuity present value coecient (‑).
r: Average consumer price ination rate between 2012 and 2022 (‑).
Energies 2024,17, 5576 22 of 29
Furthermore, as mentioned in previous research, a supplementary life cycle analysis
was conducted to reect the sharp ination in 2022, incorporating a 5.5% ination rate for
that year [1].
Based on the results of the life cycle cost analysis of the heating costs over 40 years for
the enhancement of the exterior walls, windows, and roofs, as summarized in Table 24, it
was conrmed that the implementation of the enhancements provided no economic benet.
However, when receiving construction subsidies, the improvement in heating costs was
most signicantly observed in the window enhancement of the S model. In addition, when
accounting for the construction cost subsidies, the wall factor yielded the greatest average
improvement benet across the J, K, and S standard models.
Table 24. Results of life cycle cost analysis conducted over 40 years for improvement of each factor
in the standard models J, K, and S.
Improvement
Factor Senior Center Annuity Present
Value Coecient
Energy Cost * Prot (USD)
Before
(USD/y)
After
(USD/y)
** 1.89% ** 1.89%
Wall
J
27.89
762 642 −29,230
K 482 393 −20,022
S 833 721 −21,758
Window
J 762 762 −6041
K 482 460 −6046
S 833 653 −14,214
Roof
J 762 707 −2419
K 482 450 −3788
S 833 803 −1869
Costs as of 18 April 2023 (unit: USD 1 = 1318.3 KRW). * Amount may change depending on the building condition.
Installation cost (including mechanical, electrical, and labor). ** 1.89%: average consumer price ination rate
between 2012 and 2022.
Table 25 shows the results of the present value analysis of the life cycle costs of boiler
maintenance and replacement over a 40‑year period. The cost of replacing an aging boiler
was analyzed based on the model from Company B.
Table 25. Results of life cycle analysis of boiler replacement over 40 years following boiler improve‑
ment of standard models J, K, and S.
Ination
Rate Year Present Value
Coecient
Construction Cost by Year When Considering the Present Value Factor
J K S
Before After Before After Before After
1.89%
*Construction cost 1019 1732 1019 839 1019 1496
10 0.829 845 1436 845 696 845 1241
20 0.688 700 1191 700 577 700 1029
30 0.570 581 987 581 478 581 853
40 0.473 482 819 482 397 482 707
Sum 3626 6164 2607 2147 2607 3830
Construction Prot −2538 640 −1700
Costs as of 18 April 2023 (unit: USD 1 = 1318.3 KRW). *Costs of condensing boilers are subsidized by
the government.
As shown in Table 24, only the K model was analyzed to be economically viable. In the
case of the K model, the building was small, so a boiler with a small capacity was sucient,
making it economically feasible.
Energies 2024,17, 5576 23 of 29
Table 26 presents the results of the 40‑year life cycle cost analysis of the heating costs
based on boiler operations.
Table 26. Life cycle analysis results considering heating costs over 40 years after boiler improvements
in standard models J, K, and S.
Improvement
Factor
Senior
Center
Present Value Coecient
of Annuity
Energy Cost Energy Prot
(USD)
* Total Prot
(USD)
Before After
** 1.89% ** 1.89% ** 1.89%
Boiler
J
27.89
762 717 1255 −1.238
K 482 442 1116 1756
S 833 793 1116 −584
Costs as of 18 April 2023 (unit: USD 1 = 1318.3 KRW). * Amount may change depending on the building condition.
Installation cost (including mechanical, electrical, and labor). ** 1.89%: average consumer price ination rate
between 2012 and 2022.
Therefore, when construction costs are not supported, the economic benet is nega‑
tive in all the cases. However, replacing a standard boiler with a condensing boiler resulted
in the smallest decit.
As a result of the life cycle cost analysis, as shown in Figure 12, only the boiler for the
K model was found to generate benets in total costs over 40 years.
Energies 2024, 17, x FOR PEER REVIEW 24 of 30
Table 26. Life cycle analysis results considering heating costs over 40 years after boiler improve-
ments in standard models J, K, and S.
Improvement
Factor
Senior
Center
Present Value Coefficient
of Annuity
Energy cost Energy Profit (USD) * Total profit (USD)
Before After
** 1.89% ** 1.89% ** 1.89%
Boiler
J
27.89
762 717 1255 −1.238
K 482 442 1116 1756
S 833 793 1116 −584
Costs as of 18 April 2023 (unit: USD 1 = 1318.3 KRW). * Amount may change depending on the
building condition. Installation cost (including mechanical, electrical, and labor). ** 1.89%: average
consumer price inflation rate between 2012 and 2022.
Therefore, when construction costs are not supported, the economic benefit is nega-
tive in all the cases. However, replacing a standard boiler with a condensing boiler re-
sulted in the smallest deficit.
As a result of the life cycle cost analysis, as shown in Figure 12, only the boiler for the
K model was found to generate benefits in total costs over 40 years.
Figure 12. Result of analysis of economic feasibility of applying each improvement factor in the case
of non-subsidized construction costs relating to welfare centers for the elderly.
In the case of improvement works, as shown in Figure 13, when only the net energy
improvement cost is analyzed, such as when government support or remodeling work is
mandatory, the S model’s windows have the highest energy profit amount, followed by
the J model’s external wall insulation improvement work, which shows the next highest
profit amount.
Figure 12. Result of analysis of economic feasibility of applying each improvement factor in the case
of non‑subsidized construction costs relating to welfare centers for the elderly.
In the case of improvement works, as shown in Figure 13, when only the net energy
improvement cost is analyzed, such as when government support or remodeling work is
mandatory, the S model’s windows have the highest energy prot amount, followed by
the J model’s external wall insulation improvement work, which shows the next highest
prot amount.
Therefore, when prioritizing the energy performance improvement factors for senior
centers while considering the economic eects [59,60], it is suggested to make improve‑
ments according to the process shown in Figure 14. As described in Figure 14, the rst
step is to determine whether the construction is eligible for support and to identify the
intended eects of the improvements. This process suggests that boiler improvements
should be prioritized when considering the economic eects, if no support is available.
Energies 2024,17, 5576 24 of 29
Energies 2024, 17, x FOR PEER REVIEW 25 of 30
Figure 13. Results of economic analysis of the application of each improvement factor in senior cen-
ters when construction costs are subsidized.
Therefore, when prioritizing the energy performance improvement factors for senior
centers while considering the economic effects [59,60], it is suggested to make improve-
ments according to the process shown in Figure 14. As described in Figure 14, the first
step is to determine whether the construction is eligible for support and to identify the
intended effects of the improvements. This process suggests that boiler improvements
should be prioritized when considering the economic effects, if no support is available.
Figure 14. Energy performance improvement factor selection step considering energy improvement
effects and economic improvement effects.
6. Discussion
This study evaluated energy performance improvements in aging senior centers by
analyzing the impact of various building retrofits, with each element’s energy-saving
Figure 13. Results of economic analysis of the application of each improvement factor in senior
centers when construction costs are subsidized.
Energies 2024, 17, x FOR PEER REVIEW 25 of 30
Figure 13. Results of economic analysis of the application of each improvement factor in senior cen-
ters when construction costs are subsidized.
Therefore, when prioritizing the energy performance improvement factors for senior
centers while considering the economic effects [59,60], it is suggested to make improve-
ments according to the process shown in Figure 14. As described in Figure 14, the first
step is to determine whether the construction is eligible for support and to identify the
intended effects of the improvements. This process suggests that boiler improvements
should be prioritized when considering the economic effects, if no support is available.
Figure 14. Energy performance improvement factor selection step considering energy improvement
effects and economic improvement effects.
6. Discussion
This study evaluated energy performance improvements in aging senior centers by
analyzing the impact of various building retrofits, with each element’s energy-saving
Figure 14. Energy performance improvement factor selection step considering energy improvement
eects and economic improvement eects.
6. Discussion
This study evaluated energy performance improvements in aging senior centers by
analyzing the impact of various building retrots, with each element’s energy‑saving ef‑
fects assessed through the ECO2 simulation tool. The results indicated the following av‑
erage improvement rates in primary energy consumption: walls (25.9%), windows (8.0%),
boilers (6.5%), roofs (6.0%), and doors (1.8%). These ndings demonstrate the importance
Energies 2024,17, 5576 25 of 29
of enhancing building envelope components, particularly walls and windows, to achieve
signicant energy conservation.
After interpreting these ndings, it becomes evident that retroing the walls and
windows was the most eective measure in reducing energy consumption by minimizing
heat loss, air inltration, and maintaining stable indoor temperatures. Windows, in par‑
ticular, proved crucial due to their potential for heat loss reduction, thus enhancing the
overall energy eciency. Boiler replacements also demonstrated considerable improve‑
ment in heating eciency, reducing heating energy demands signicantly. These results
align with the previous research, which highlights the importance of building envelope
retrots in enhancing energy eciency in facilities that serve elderly populations.
In terms of practical applications, this study provides a prioritized framework for
energy performance improvements in senior centers and other aging public facilities. By
indicating that windows and walls should be prioritized for maximum energy savings,
this framework oers a practical guideline for facility retrots within budget constraints.
Economically, the 40‑year life cycle analysis identied boiler replacements as the most cost‑
eective retrot. When nancial support is limited, focusing on boilers, followed by roof,
window, and wall upgrades, could optimize returns based on operational cost reductions.
With government subsidies, the recommended prioritization shifts to windows, exte‑
rior walls, boilers, and roofs, leveraging the nancial support to align with greenhouse gas
reduction initiatives and maximize energy performance. Current government initiatives
aimed at reducing carbon emissions present an ideal opportunity to implement compre‑
hensive retrots, particularly in building envelope improvements, to achieve substantial
primary energy savings in senior centers.
There are some limitations to this study, particularly regarding data accuracy and
scope. Due to a lack of preserved architectural records for many of the senior centers, the
baseline insulation conditions were estimated based on the Building Act standards relevant
to each building’s construction year, which may limit precision. Additionally, variations
in building congurations and material integrity were observed across the surveyed sites.
Although the ECO2 simulations and blower door tests provided valuable insights, the lack
of direct energy consumption data limited the accuracy of the projected energy savings.
Future studies should integrate long‑term energy monitoring systems to capture actual
energy use before and after retrots, thereby enhancing accuracy.
Further research could also consider the health‑related outcomes of energy‑ecient
retrots, such as indoor air quality improvements. Given that elderly residents spend
extended time indoors, integrating systems like total heat exchangers could simultane‑
ously improve air quality and energy eciency, thereby enhancing resident health and
comfort. Extending this study to various types of public facilities would also allow for a
more comprehensive comparison of the energy performance improvements under dier‑
ent conditions.
In conclusion, this study provides a prioritized approach to retroing senior centers
and other aging buildings, which can eectively achieve energy cost savings and carbon
reduction goals. The ndings highlight windows and walls as key components for energy
eciency and suggest that limited‑budget retrots should focus on maximizing the en‑
ergy impacts by prioritizing these elements. This study serves as a practical reference for
researchers and practitioners aiming to optimize energy improvements in aged facilities
and emphasizes that renewing aged structures is essential, given both the environmen‑
tal and economic imperatives. As such, this research contributes a foundational model
that future studies can build upon to develop sustainable, energy‑ecient strategies for
aging buildings.
7. Conclusions
In this study, a current status survey was conducted, targeting 20 senior centers in
the metropolitan area, and the energy performance improvement factors were selected
via a literature review. An ECO2 simulation was performed to analyze the changes in
Energies 2024,17, 5576 26 of 29
energy consumption, and an economic analysis was performed for the J, K, and S standard
models. Accordingly, a method for selecting the energy performance improvement factors
was proposed.
Based on these ndings, the main results of this study are as follows:
(1) As a result of the ECO2 simulation analysis, the following measures had the greatest
eect on primary energy consumption, in descending order: outer walls, windows,
boilers, roofs, and doors. The greatest eect for exterior wall improvements was ob‑
served in senior center D, and the average energy consumption improvement result‑
ing from the application of PF boards was 25.9%.
(2) As a result of the ECO2 simulation analysis, the senior center with the greatest im‑
provement eect relating to windows and doors was S, and the average improvement
rate was 8% when low‑e, double‑glazed windows were installed.
(3) The standard models J, K, and S senior centers were selected based on the average
type of oor area, energy demand, energy consumption, and primary energy con‑
sumption. For the energy consumption improvement eect of the performance im‑
provement measures on the standard models, the eect of the outer wall improve‑
ments on model J was 11.0%, and the eect of the window and door improvements
on model S was the greatest at 19.9%. For model K, the eect of outer wall improve‑
ment was the highest at 14.4%.
(4) A blower door test was conducted to verify the airtight performance of the build‑
ings after the window improvements. According to the results of a calculation of
the number of ventilations per hour, the α‑senior center exhibited an improvement
eect of 52.5%, which was about 35.2% higher than the simulation result. For the β‑
senior center, the improvement eect was about 4.6% lower than the 9.8% eect ob‑
tained in the simulation, but the eect of pressurization was similar to that obtained in
the simulation.
(5) According to the results of the energy performance improvement eect analysis in
this study, the improvement factors should be selected in the following order: win‑
dows, outer walls, boilers, roofs, and doors.
(6) As a result of the life cycle cost analysis of the heating costs over a 40‑year period, the
energy cost improvements of the J, K, and S standard models were the greatest after
window improvements. However, in the life cycle cost analysis conducted by includ‑
ing the construction costs, only the boiler improvement of the K model generated a
prot of USD 1756.
(7) According to the results of the economic feasibility analysis in this study, when car‑
rying out improvement work without government support, priority should be given
to improving the boiler, as it resulted in the lowest decit.
(8) In recent years, construction costs have risen excessively, and it has been conrmed
that some energy performance improvements are not economically feasible in terms
of life cycle costs. However, due to the aging of buildings, renewal work has become
a necessity rather than an option. When carrying out renewal work within a limited
budget, it is expected that this study will serve as a reference for prioritizing areas for
improvement by considering energy performance enhancements rst.
In future research, we plan to measure energy consumption before and after the appli‑
cation of various improvement factors during the conversion of the existing senior centers
into green buildings, with the aim of enhancing their energy performance. In addition, we
plan to analyze the eects of installing a total heat exchanger, which is eective at improv‑
ing indoor air quality and energy consumption in consideration of the health of the elderly,
whose activities are restricted to indoor seings if the outdoor air is contaminated. Finally,
the data were veried using condential measurement data that aect heating energy con‑
sumption, rather than direct data on energy consumption. Therefore, a comparison with
actual usage is limited.
Energies 2024,17, 5576 27 of 29
Author Contributions: Conceptualization, A.N.; methodology, A.N.; experiment, A.N.; software,
A.N., Y.I.K. and A.N.; verication, Y.I.K.; formal analysis, A.N.; investigation, A.N.; resources, A.N.;
data Curation, A.N. and A.N.; writing—original draft preparation, A.N. and A.N.; writing—review
and editing, Y.I.K.; visualization, A.N.; director, Y.I.K.; project management, Y.I.K.; funding, A.N.
All authors have read and agreed to the published version of the manuscript.
Funding: This study was supported by the Research Program funded by Seoul National University
of Science and Technology.
Data Availability Statement: The original contributions presented in the study are included in the
article, further inquiries can be directed to the corresponding author.
Conicts of Interest: The authors declare no conicts of interest.
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