![Energy poverty assessment for total energy expenditure (left), and Energy poverty assessment for the Mediterranean climate (right) [10].](https://www.researchgate.net/publication/347830428/figure/fig1/AS:974901116674049@1609445947631/Energy-poverty-assessment-for-total-energy-expenditure-left-and-Energy-poverty_Q320.jpg)
![Energy Poverty Assessment model for Bizkaia, based on the energy poverty methodology proposed by Sánchez-Guevara [10].](https://www.researchgate.net/publication/347830428/figure/fig2/AS:974901116674050@1609445947668/Energy-Poverty-Assessment-model-for-Bizkaia-based-on-the-energy-poverty-methodology_Q320.jpg)
![Fuel poverty assessment graph for CAPV households for 2016 [10].](https://www.researchgate.net/publication/347830428/figure/fig3/AS:974901116674051@1609445947728/Fuel-poverty-assessment-graph-for-CAPV-households-for-2016-10_Q320.jpg)
![AVERAGE ENERGY CONSUMPTION AND COST PER HOUSEHOLD LOCATED IN BIZKAIA [27]](https://www.researchgate.net/publication/347830428/figure/tbl1/AS:974901120872448@1609445948090/AVERAGE-ENERGY-CONSUMPTION-AND-COST-PER-HOUSEHOLD-LOCATED-IN-BIZKAIA-27_Q320.jpg)
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Environmental and Climate Technologies
2020, vol. 24, no. 3, pp. 66–79
https://doi.org/10.2478/rtuect-2020-0086
https://content.sciendo.com
66
©2020 Silvia Perez-Bezos, Olatz Grijalba, Olatz Irulegi.
This is an open access article licensed under the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0), in the manner agreed with Sciendo.
Proposal for Prioritizing the Retrofitting of
Residential Buildings in Energy Poverty
Circumstances
Silvia PEREZ-BEZOS1*, Olatz GRIJALBA2, Olatz IRULEGI3
1–3CAVIAR Research Group, Department of Architecture, University of the Basque
Country (UPV/EHU), Plaza Oñati, 2, 20018 Donostia - San Sebastián, Spain
Abstract – The energy poverty derived from socio-economic imbalances affects mostly
households with fewer economic resources, being social housing complexes one of the most
vulnerable sectors. The insufficient access to energy and the incapability to maintain
dwellings at an adequate temperature can have negative impact on people’s health due to the
prolonged exposure to poor hygrothermal conditions. T herefore, the prioritization of building
retrofitting actions must be carried out regarding the actual state of the housing and the
family economy. This paper proposes the definition of a prioritization map that gave a general
knowledge of the energy vulnerability situation of the existing building stock. To this end, the
dwelling’s energy performance is analysed, focusing on the correlation among its
characteristics and the energy vulnerability of its inhabitants. In this way, dwellings with high
energy poverty potential are identified in order to develop different energy retrofitting
strategies. By applying this method to 14 case studies of social housing in Bilbao, Spain, it was
obtained a prioritization map with six levels of vulnerability that can serve as a tool for public
entities to design their future strategies. It has been proven that building compactness and
year of construction are important factors with a great impact on the heating demand and
final consumption in dwellings. Acknowledging the vulnerability context of the building stock
eases the decision-making process and the definition of intervention guidelines, prioritizing
those in a situation of greater vulnerability.
Keywords – Building energy renovation; energy poverty; low income households; social
housing; thermal performance
Nomenclature
Uwall Wall thermal transmittance W/m2K
Uwin Window thermal transmittance W/m2K
UA Heating demand kWh/m2y
Sf Shape Factor %
Sc Shape Coefficient m2/m2
* Corresponding author.
E-mail address: silvia.perezb@ehu.eus
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1. INTRODUCTION
1.1. Background
There is a socio-economic imbalance which mostly affects households with fewer economic
resources, being social housing complexes one of the most vulnerable sectors.
The insufficient access to energy and the incapability to maintain dwellings at an adequate
temperature [1] results into energy poverty, which depends on three main factors [2]:
− Energy cost;
− Low incomes;
− Low energy efficiency of dwellings.
A prolonged exposure to poor hygrothermal conditions in dwellings can have negative
impact on people’s health. Consequently, there is a high correlation between inadequately
heated or low indoor temperatures and Excess Winter Death rates [3] (EWD), which is also
strongly linked to high poverty rates, inefficient building stock and high energy costs [1].
Building deficiencies and the difficulty or high cost of its refurbishment affect respiratory
and cardiovascular health during winter periods, while high indoor temperatures may cause
diseases and increase mortality due to cardiovascular causes [4].
There are two widely recognised measurement approaches for energy poverty assessment
[5]: household income/expenditure-based indicators [6], [7], and consensual indicators based
on responses to material deprivation questionnaires [8]. The challenge with these
measurement approaches lies in the source of data used for the analysis. In most cases the
expenditure gathered in the Household Budget Surveys of each country's statistical offices is
taken for reference [9], however, as many households limit their energy consumption to meet
other needs, the indicators may not reflect the real energy demand. Sánchez-Guevara [10]
developed an energy poverty methodology based on minimal thermal habitability conditions
that gathers climatic, building and socioeconomic particularities of the country. The method
is focused on the energy expenditure required to achieve minimal thermal habitability
conditions in low income dwellings.
Several studies analysing the relationship between energy poverty and monetary poverty
suggest that low-income households are at greater risk of being in a situation of energy
vulnerability [11], [12]. In this context, there has been a steady rise in the expenses/income
ratio in Spanish households related to the increase in household energy consumption per
family unit and per person between 2006 and 2012, according to the 2018 Energy Poverty
Report [9].
One of the measures to alleviate this situation is the energy social subsidy, however, several
authors propose housing renovation and energy efficiency improvement as an appropriate
measure to reduce energy poverty [13], [14], with an improvement of the building
hygrothermal performance. Increasing building’s energy efficiency and reducing dwelling’s
primary energy consumption can play an important role in governments' energy targets [15].
Among the large amount and variety of buildings with potential for energy improvement,
social housing complexes are notable case where refurbishment is a priority due to the socio-
economic vulnerability of its residents. There are several difficulties in defining intervention
strategies in these buildings, requiring a quantitative method of analysis to assess the needs
of these households.
This research extends its scope to the city of Bilbao, north of Spain, as the architectural and
construction typologies analysed are those representatives of a city, and hence, the
conclusions can be extrapolated to other cities and countries. Acknowledging the
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vulnerability context of the building stock eases the decision-making process and the
definition of intervention guidelines, prioritising those in a situation of greater vulnerability.
1.2. Objectives
This paper proposes the definition of a prioritization map that gives a general knowledge
of the energy vulnerability situation of the existing building stock. To this end, the dwelling’s
energy performance is analysed, focusing on the correlation among its characteristics and the
energy vulnerability of its inhabitants. In this way, dwellings with high energy poverty
potential are identified in order to develop different energy retrofitting strategies. To achieve
the main objective of the research, the following objectives are attained:
− identifying the physical parameters of the building that influence dwelling’s heating
demand. The study focuses on the passive aspects and does not include active
conditioning systems;
− analysing the level of impact of the characteristics of buildings on the heating
consumption and demand;
− determining to what extent the heating demand can be, along with the income and the
price of energy, an indicator for evaluating the degree of vulnerability of a household.
In addition to the prioritization map, two partial results are obtained: annual heating demand
and cost calculation model and adapted energy poverty assessment for the case of Bilbao and
the Autonomous Community of the Basque Country.
2. APPROACH
2.1. Energy Poverty Assessment
Energy vulnerability assessment is based on the energy poverty assessment graph proposed
by Sánchez-Guevara [10], according to income/expenditure-based indicators, which
incorporates the climatic, building and socioeconomic characteristics of the country and the
region in the analysis. The method includes monetary and fuel poverty indicators:
− monetary poverty: based on the Eurostat methodology, where the poverty threshold is
defined as 60 % of the median household income;
− energy poverty: following the trend of studies and first proposed by Boardman [6], it
takes 10 % of income as the monetary poverty threshold and sets 20 % as severe energy
poverty and 5 % as vulnerability to energy poverty [10], as it follows:
Energy consumption Energy cost 10 %
Income
⋅>
(1)
Since we only want to consider heating cost for energy vulnerability assessment, it is
necessary to set the appropriate percentage for the threshold, as Sánchez-Guevara [10].
In Fig. 1 the one on the left represents the starting model, where the dwelling’s total energy
expenditure is considered. The one on the right shows one of those defined by Sánchez-
Guevara for the Mediterranean climate, where the annual heating and cooling expenditure is
considered.
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Fig. 1. Energy poverty assessment for total energy expenditure (left), and Energy poverty assessment for the Mediterranean
climate (right) [10].
2.2. The Influence of Building Characteristics on Heating Demand
The thermal performance of a building depends on various parameters that can be classified
into two major groups: design-physical factors of the environment and occupation factors
[16] that have an effect on energy use of a 42 % and 4.2 %, respectively [17].
After considering these values, it was decided to analyse the environmental and design
parameters to evaluate the impact of the building characteristics and demand on the energy
vulnerability of households.
Bektas et al. [18] summarizes the parameters that affect building’s thermal performance:
physical and environmental parameters (outdoor temperature, solar radiation and wind
direction and speed) and design parameters (shape factor, transparent/opaque surface,
orientation, thermal properties of building materials, and distance between buildings).
Building envelope characteristics have a direct effect on the thermal performance.
Within these parameters, the ratio between transparent and opaque surface represents an
impact of 20 % on the cooling demand and 11 % on heating demand [19].
The shape of the building influences the amount of solar radiation it receives and its energy
consumption, in particular, Elasfouri et al. [20] suggested that the radiation reaching the
building can increase the cooling demand by up to 25 %. Depecker et al. [21] defined the
relationship between building shape and energy demand, and concluded that for cold climates
energy consumption is proportional to compactness (m3/m2). A correct combination of the
shape factor (ratio of length to depth of the building) and orientation can result in 36 % energy
savings [22].
Several studies have investigated the relationship between urban compactness and access
to the sun of urban areas [23], [24]. Salvati et al. [25] suggested that an increase in density in
certain urban areas can lead to an increase in heating demand due to a decrease in solar
radiation. However, for densities above 40 %, the intensity of the heat island effect is greater
than the reduction in solar radiation. These parameters evidence the importance of building
design for energy saving, by applying passive design solution and using right materials and
design tools [26].
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3. METHODS AND METHODOLOGY
The method is divided in three stages. In the first stage, an adaptation of the energy poverty
assessment method was made, which relates the equivalent heating cost to the equivalent
annual income of the household. In the second stage, the influence of the physical
characteristics of the buildings on the heating demand is analysed, with energy simulations
and theoretical results from previous studies. Finally, 14 case studies are evaluated in order
to spot possible cases of energy vulnerability and necessity of intervention, and compared in
a prioritisation classification.
3.1. Adjusting the Energy Poverty Assessment
Sánchez-Guevara’s [10] energy poverty assessment measurement parameters include the
particularities of each location. Therefore, a specific adjustment was required for the case of
Bilbao and the Autonomous Community of the Basque Country (CAPV).
Firstly, the energy poverty threshold was adjusted, as the 10 % of the total income estimated
to cover the energy needs of a household. Table 1refers to total expenditure per household
(€) and includes consumption associated with sanitary hot water, lighting and domestic
appliances. In the energy vulnerability assessment, only the energy consumption for heating
is considered, so it is necessary to set an appropriate percentage for the threshold, as Sánchez-
Guevara exposed [10]. To this end, energy consumption in dwelling was determined by
equipment and energy source, using data from the Basque Country on household energy
consumption according to the SPAHOUSEC project, carried out by the “Instituto para la
Diversificación y Ahorro de la Energía” (IDAE) [27].
Subsequently, the energy expenditure of the dwelling was calculated based on the state
energy price, as shown in Table 1. The energy consumption and expenditure were calculated
using the following data:
− Dwelling energy consumption;
− Domestic hot water (DHW), heating and cooking systems, by energy source;
− Energy costs;
− Dwelling energy expenditure;
− Heating expenditure.
The percentage of heating expenditure determines the energy poverty threshold. This value
was calculated using energy price from Eurostat. The results established an energy poor
household when the expenditure on heating was higher than 3.4 % of the income.
TABLE 1. AVERAGE ENERGY CONSUMPTION AND COST PER HOUSEHOLD LOCATED IN BIZKAIA [27]
Final use Energy
Consumption, kWh
Energy
Consumption, % Energy Costs, € Energy Costs, %
Heating System 4142.73 40.10 659.62 33.59
Domestic Hot Water 2262.49 21.90 288.19 14.68
Cooking Systems 1239.72 12.00 271.78 13.84
Cooling Systems 10.33 0.10 2.86 0.15
Lighting 392.58 3.80 108.74 5.54
Electrical appliance 2283.15 22.10 632.43 32.21
Total 10 331.00 100.00 1963.62 100.00
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The monetary poverty threshold was calculated according to Sánchez-Guevara [10], as
60 % of the median income according to Eurostat methodology. Afterwards, data was taken
from the Household Budget Survey [28] defined for each autonomous community.
The average income for Bilbao was taken as the monetary poverty threshold. The monetary
poverty values and the energy poverty thresholds were then transferred to the energy poverty
assessment graph, adapted for the case of Bilbao.
Fig. 2. Energy Poverty Assessment model for Bizkaia, based on the energy poverty methodology proposed by
Sánchez-Guevara [10].
Once the method has been adjusted to the location chosen for the study, it is possible to
develop the energy poverty assessment with household’s annual income and heating costs
data, as shown in Fig. 2. According to their ubication in the graph, analysed cases were
classified in groups with lower or higher fuel poverty vulnerability. Groups G1, G2 and G3
represent those who are under some type of poverty, while groups G4 and G5 are determined
by those groups that, despite not being in a situation of poverty, are in a vulnerability
condition due to their proximity to the poverty threshold. Group G6 is considered to be outside
any type of poverty or vulnerability.
3.2. Annual Heating Demand and Cost Calculation Model
In the second phase of the study, the influence of the physical characteristics of the
buildings was analysed, considering the key aspects that determine their energy performance.
Bilous et al. [29] concluded that the variables that have the greatest influence on indoor
temperature are from highest to lowest: air exchange rate, heating gains, outdoor temperature
and solar gains. Considering that air exchange and heating gains are highly user-dependent,
they were not considered for the study. Instead, outdoor temperature and solar gains were
considered as project variables, related to the following parameters:
− Urban Density, %;
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− Building compactness or Shape Coefficient, m2/m3;
− Shape Factor, %;
− Orientation;
− Construction characteristics (U), W/m2K.
The first three parameters were analysed considering the theoretical results of previous
research, while the impact of orientation and construction characteristics were determined
carrying out energy simulations developed with the Design Builder program, with the Energy
Plus database.
The impact of Urban Density, Shape Coefficient and Shape Factor on building’s thermal
energy demand was analysed based on the results of previous studies, explained in the
subchapter 2.2. The Influence of Building Characteristics on Heating Demand.
Possible future research would focus on the analysis of user behaviour in heating
consumption. Jimenez-Bescos et al. [30] concluded that incorporating user behaviour into
building simulations, a more accurate estimation of energy consumption could be achieved.
The reference volume used for the energy simulations was defined as a linear block
typology, as it is a construction type widely used in urban areas, both in Bilbao and in Spain
in this type of social housing. The model has a shape factor of 2/1 with 5 floors and ground
floor with a north-south orientation of its longitudinal face. It has 378 m2 footprint, 54 m
length and 7 m width. The total height of the block is 15 m, and each floor has 2.5 m height.
The shape coefficient of the building is 2.9 m2/m3 with an urban density of 0 %.
A characterization of the construction systems used in the existing building stock was also
carried out (walls’ and windows’ thermal transmittance and percentage of openings in the
facade), following Terés’ [31] and “Caracterización del parque residencial de la CAPV para
la elaboración de un plan de acción a largo plazo” [32] study’s results, as shown in Table 2.
TABLE 2. ENCLOSURE CHARACTERIZATION OF THE BUILDING PARK, BASQUE COUNTRY
Construction
year Period Uwall, W/m2K Uwin, W/m2K Window
percentage, %
Heating demand
model , kWh/m2y
Before 1900 1 1.11 4.23 0.1 46.69
1901–1940 2 1.11 4.84 0.31 47.45
1941–1960 3 1.16 4.62 0.21 46.93
1961–1966 4 1.44 4.62 0.34 49.69
1967–1980 5 1.44 5.7 0.34 54.36
1981–2006 6 0.48 4.16 0.24 37.11
After 2006 7 0.41 2.76 0.22 31.28
Note: The thermal transmittance of the roof is taken as a constant value as it requires an exhaustive specific study
A total of 28 simulations were carried out, in which the construction year and thus the
construction characteristics were modified, as well as the orientation of the building in
relation to the longitudinal facades: 0° (north-south), 45° (northeast-southwest), 90° (east-
west) and 135° (northwest-southeast).
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Fig. 3. Heating demand variation according to the building orientation and construction period characteristics.
The main results of the simulation showed that the cases with the greatest demand were
those where the reference building was located in an east-west orientation, and specifically,
the most unfavourable period corresponded to buildings constructed between 1967 and 1980,
shown in Fig. 3.
Based on the influence and impact of the building's physical parameters on the energy
heating demand, a method to estimate the dwelling heating demand and annual heating costs
has been defined.
The method takes as a starting point the heating demand model (kWh/m2y) obtained from
the simulations in Table 2, to which the savings of each physical parameter of the building
are added or deducted. The data of savings is collected in Table 3, Table 4. Once the
building’s heating demand (kWh/m2y) is known, a sample dwelling is taken from it and the
estimated heating demand (kWh/y) and cost (€/y) is determined, applying Eq. (2), Eq. (3) and
Eq. (4).
22
Heating Demand Heating Demand Model 1 Energy Conservation (%)
kWh kWh
my my
= ⋅−
∑
(2)
2
22
Estimated Heating Demand Heating Demand Dwelling area (m )
kWh kWh
my my
= ⋅
(3)
EUR EUR
Estimated Heating Cost Estimated Heating Demand Energy Costs
kWh
y y kWh
= ⋅
(4)
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
Before 1900 1901-1940 1941-1960 1961-1966 1967-1980 1981-2006 After 2006
Heat Demand, kWh/m²
Building construction periods, years
0º 45º 90º 135º Trend line
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TABLE 3. SAVINGS IN HEATING DEMAND RELATIVE TO ORIENTATION 0° (N-S) FOR THE
DIFFERENT CONSTRUCTION PERIOD
Stage
Orientation
0° (N-S) 45° (NE-SW) 90° (E-W) 135° (NW-SE)
Sf 1/1 Sf 2/1 Sf 1/1 Sf 2/1 Sf 1/1 Sf 2/1 Sf 1/1 Sf 2/1
1 3.67 % 0 1.74 % –1.93 % –0.52 % –4.19 % 1.63 % –2.31 %
2 3.67 % 0 0.22 % –3.45 % –3.74 % –7.41 % –0.62 % –4.29 %
3 3.67 % 0 0.77 % –2.90 % –2.47 % –6.14 % 0.1 3% –3.54 %
4 3.67 % 0 0.14 % –3.53 % –3.83 % –7.50 % –0.68 % –4.35 %
5 3.67 % 0 0.52 % –3.15 % –3.56 % –7.23 % –0.20 % –3.87 %
6 1.51 % 0 –2.15 % –3.66 % –5.98 % –7.49 % –3.17 % –4.68 %
7 1.51 % 0 –2.61 % –4.12 % –6.41 % –7.92 % –3.83 % –5.34 %
TABLE 4. SAVINGS IN HEATING DEMAND BASED ON THE SHAPE COEFFICIENT AND URBAN DENSITY
Shape Coefficient Sc, m2/m3 Energy Conservation Urban Density, % Energy Conservation
<0.2 21 % 0–0.14 0
0.2–0,4 0 % 0.15–0.29 –8.88 %
0.4–0,6 –23 % 0.3–0.44 –7.12 %
0.6–0,8 –45 % 0.45–0.59 –10.04 %
0.8–1 –67 % 0.6–0.74 0.61 %
1–1.2 –90 % 0.75–1.00 13.60 %
3.3. Choice of the Case Study
Annual heating demand and cost calculation model was finally applied to different case
studies. The results were transferred to the energy poverty assessment method to verify its
applicability and to identify the situation of 14 residential social housing buildings located in
Bilbao. For the analysis, those buildings with municipal ownership of more than 75 % were
chosen, discarding those with less ownership since they represent a lesser capacity of
intervention by the managing entity.
It was decided to analyse these buildings considering the nature of vulnerability their
inhabitants are in, as being receivers of a social housing subsidy makes them eligible for the
present study.
4. RESULTS
4.1. Energy Poverty Assessment Method and Energy Cost Calculation Model
Two partial results were obtained: annual heating demand and cost calculation model and
adapted energy poverty assessment method for the case of Bilbao and the Autonomous
Community of the Basque Country. These results were analysed with 14 case studies in Bilbao
to prove their applicability.
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4.2. Housing Vulnerability Assessment
Different sources have been used for data collection. The average annual income was taken
from the data published by Eustat for the year 2016. The area, location and year of
construction have been provided by the Cadastre of Bizkaia and Alokabide respectively. An
urban density of 45 % was taken for the municipality of Bilbao, according to data published
by Jiménez Romera [33].
The annual heating demand of each case was calculated by adjusting the result with the
values of influence of the building’s physical parameters per m2. Subsequently, the theoretical
annual demand and the annual cost of heating for a typical dwelling in the selected building
were determined. The results showed annual heating costs between € 250 and € 560, with
annual incomes between € 12.387 and € 15.743. Two of the three cases with the highest
heating costs correspond to districts with lower annual incomes. Moreover, those buildings
identified as being in energy poverty circumstances are mainly located in vulnerable
neighbourhoods.
Although the case studies, built mainly after the entry into force of the Código Técnico de
la Edificación (CTE), have a similar base heating demand due to their close construction
years, there are remarkable differences in the estimated annual heating demand and annual
cost as a result of their reduced compactness, which considerably increases their demand.
This can be observed with the analysis of cases C3, C8 and C11. The first case has a base
heating demand of 31.28 kWh/m2y and 37.11 kWh/m²y for the following cases. The results
in Fig. 4 shows these three cases present the highest estimated heating demand.
Fig. 4. Annual heating demand variation and annual heating cost variation for Bilbao case studies.
0.00 20.00 40.00 60.00 80.00
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
Estimated Heating Energy Demand,
kWh/m²y
Case studies
0.0 200.0 400.0 600.0 800.0 1000.0
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
Estimated Heating Energy Costs, €/y
Case studies
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The variation in demand and annual cost of heating is shown in Fig. 4. It shows the
differences between demand and the final cost, influenced fundamentally by the surface areas
of the standard dwellings in each case.
The data of estimated heating energy costs and annual equivalent income are transferred to
the defined method, as shown in Fig. 5. Overall, Fig. 6 shows that the case studies analysed
are in Group 3 and Group 4, in a situation of energy poverty and vulnerability to energy
poverty respectively. There are two cases in Group 6, outside of any type of vulnerability.
Fig. 5. Fuel poverty assessment graph for CAPV households for 2016 [10].
Fig. 6. Location and classification into vulnerability groups of the analysed case studies.
0
5000
10000
15000
20000
25000
30000
35000
0.0 500.0 1000.0 1500.0 2000.0 2500.0
Annual equivalent income, €
Estimated Annual Heating Energy Costs, €
Median income
Monetary poverty line
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5. CONCLUSION
This paper presents a proposal for prioritising the retrofitting of residential buildings in
energy poverty circumstances, applied to 14 case studies in Bilbao. It has been observed that
social housing complexes represent one of the most vulnerable sectors, and therefore they are
notable case where refurbishment is a priority due to the socio-economic vulnerability of its
residents.
The energy vulnerability assessment method proposed, allows to identify the situation of
vulnerability of the analysed buildings. Measurement parameters included the particularities
of each location. Therefore, a specific adjustment was developed for the case of Bilbao and
the Autonomous Community of the Basque Country (CAPV).
It has been proven that compactness and year of construction are another important factor
with a great impact on the demand and final consumption of heating in dwellings. The results
showed annual heating costs between € 250 and € 560, with annual incomes between
€ 12.387 and € 15.743. Two of the three cases with the highest heating costs correspond to
districts with lower annual incomes.
Some limitations of the project were detected, such as the analysed geometries, adjustment
of the heating cost and the limited samples of buildings analysed. However, the main
objective of the project has been achieved with the definition of a prioritization map that gave
a general overview of the energy vulnerability situation of the existing social housing building
stock in Bilbao. The main contribution of the research lies in acknowledging the vulnerability
context of the building stock, which eases the decision-making process and the definition of
intervention guidelines, prioritising those in a situation of greater vulnerability. The proposed
system is accessible to different users and can serve as a tool for public entities to design their
future strategies.
Since the architectural and construction typologies analysed are also representative of a
city, the conclusions can be extrapolated to other national and foreign locations. Furthermore,
the energy poverty evaluation graph completes the two previously defined by Sánchez-
Guevara [10], according to the three climates included in the SECH-SPAHOUSEC project
[27].
ACKNOWLEDGEMENT
We would like to acknowledge Alokabide, public corporation under the Basque Government for the development of the
social function of housing through the rental policy, who has provided the necessary data for the correct definition of the study.
REFERENCES
[1] San Miguel-Bellod J., González-Martínez P., Sánchez-Ostiz A. The relationship between poverty and indoor
temperatures in winter: Determinants of cold homes in social housing contexts from the 40s–80s in Northern Spain.
Energy and Buildings 2018:173:428–442. https://doi.org/10.1016/j.enbuild.2018.05.022
[2] Santamouris M., Kolokotsa D. On the impact of urban overheating and extreme climatic conditions on housing, energy,
comfort and environmental quality of vulnerable population in Europe. Energy and Buildings 2015:98:125–133.
https://doi.org/10.1016/j.enbuild.2014.08.050
[3] HealyJ. D. Housing, Fuel Poverty and Health. Routledge, 2017.
[4] World Health Organization. Department of Housing and Urban Development, France. Ministère des affaires sociales
et de la santé, and United States. Environmental Protection Agency. WHO housing and health guidelines. US: WHO,
2018.
[5] Herrero S. T. Energy poverty indicators: A critical review of methods. Indoor and Built Environment 2017:26(7):1018–
1031. https://doi.org/10.1177/1420326X17718054
Environmental and Climate Technologies
____________________________________________________________________________ 2020 / 24
78
[6] Boardman B. Fuel Poverty: from cold homes to affordable warmth. London: Belhaven Press, 1991.
[7] Boardman B. Fixing Fuel Poverty: Challenges and Solutions. London: Earthscan, 2010.
[8] Eurostat. European Commission, European Union Statistics on Income and Living Conditions (EU-SILC) [Online].
[Accessed 16.06.2019]. Available: https://ec.europa.eu/eurostat/web/income-and-living-conditions/data/database
[9] Asociación de Ciencias Ambientales (ACA). Pobreza Energética en España 2018: Hacia un sistema de indicadores y
una estrategia de actuación estatal. Madrid, 2018. (in Spanish)
[10] Sánchez-Guevara Sánchez C., Neila González F. J., Hernández Aja A. Energy poverty methodology based on minimal
thermal habitability conditions for low income housing in Spain. Energy and Buildings 2018:169:127–140.
https://doi.org/10.1016/j.enbuild.2018.03.038
[11] Papada L., Kaliampakos D. Measuring energy poverty in Greece. Energy Policy 2016:94:157–165.
https://doi.org/10.1016/J.ENPOL.2016.04.004
[12] Paravantis J. A., Santamouris M. An analysis of indoor temperature measurements in low- and very-low-income
housing in Athens, Greece. Advances and Building Energy Research 2016:10(1):20–45.
https://doi.org/10.1080/17512549.2015.1014842
[13] Ürge-Vorsatz D., Tirado Herrero S. Building synergies between climate change mitigation and energy poverty
alleviation. Energy Policy 2012:49:83–90. https://doi.org/10.1016/j.enpol.2011.11.093
[14] Thomson, H. Thomas S., Sellstrom E., Petticrew M. Housing improvements for health and associated socio-economic
outcomes. Cochrane Database Syst. Rev. 2013:2. https://doi.org/10.1002/14651858.CD008657.pub2
[15] Prodanuks et al. Analysis of Energy Supply Solutions of Dwelling Buildings. Environmental and Climate Technologies
2019:23(3):182–189. https://doi.org/10.2478/rtuect-2019-0088
[16] FilippínC., Flores Larsen S., Ricard F. Improvement of energy performance metrics for the retrofit of the built
environment. Adaptation to climate change and mitigation of energy poverty. Energy and Buildings 2018:165:399–
415. https://doi.org/10.1016/J.ENBUILD.2017.12.050
[17] Guerra Santin O., Itard L., Visscher H. The effect of occupancy and building characteristics on energy use for space
and water heating in Dutch residential stock. Energy and Buildings 2009:41(11):1223–1232.
https://doi.org/10.1016/J.ENBUILD.2009.07.002
[18] Bektas Ekici B., Aksoy U. T. Prediction of building energy needs in early stage of design by using ANFIS. Expert
Systems with Applications 2011:38(5):5352–5358. https://doi.org/10.1016/j.eswa.2010.10.021
[19] Belussi L. et al. A review of performance of zero energy buildings and energy efficiency solutions. Journal of Building
Engineering 2019:25:100772. https://doi.org/10.1016/j.jobe.2019.100772
[20] Elasfouri A., Maraqa R., Tabbalat R. Shading control by neighbouring buildings: application to buildings in Amman,
Jordan. International Journal of Refrigeration 1991:14(2):112–116. https://doi.org/10.1016/0140-7007(91)90083-S
[21] Depecker P., Menezo C., Virgone J., Lepers S. Design of buildings shape and energetic consumption. Building and
Environment 2001:36(5):627–635. https://doi.org/10.1016/S0360-1323(00)00044-5
[22] Mingfang T. Solar control for buildings. Building and Environment 2002:37(7):659–664.
https://doi.org/10.1016/S0360-1323(01)00063-4
[23] Mohajeri N. et al. Effects of urban compactness on solar energy potential. Renewable Energy 2016:93:469–482.
https://doi.org/10.1016/J.RENENE.2016.02.053
[24] Vermeulen T., Knopf-Lenoir C., Villon P., Beckers B. Urban layout optimization framework to maximize direct solar
irradiation. Computers, Environment and Urban Systems 2015:51:1–12.
https://doi.org/10.1016/J.COMPENVURBSYS.2015.01.001
[25] Salvati A., Coch H., Morganti M. Effects of urban compactness on the building energy performance in Mediterranean
climate. Energy Procedia 2017:122:499–504. https://doi.org/10.1016/J.EGYPRO.2017.07.303
[26] Albatayneh A., Alterman D., Page A., Moghtaderi B. The Significance of Building Design for the Climate.
Environmental and Climate Technologies 2018:22(1):165–178. https://doi.org/10.2478/rtuect-2018-0011
[27] Análisis del consumo energético del sector residencial en España. Proyecto SECH-SPAHOUSEC. 2011. (in Spanish)
[28] Instituto Nacional de Estadística. Encuesta de presupuestos familiares. Base 2006 - Año 2017 [Online]. [Accessed
17.06.2019]. Available:
https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176806&menu=ultiDatos&idp=
1254735976608.
[29] Bilous I., Deshko V., Sukhodub I. Parametric analysis of external and internal factors influence on building energy
performance using non-linear multivariate regression models. Journal of Building Engineering 2018:20:327–336.
https://doi.org/10.1016/J.JOBE.2018.07.021
[30] Jimenez-Bescos C., Oregi X. Implementing User Behaviour on Dynamic Building Simulations for Energy
Consumption. Environmental and Climate Technologies 2019:23(3):308–318. https://doi.org/10.2478/rtuect-2019-
0097
[31] Terés-Zubiaga J., Martín K., Erkoreka A., Sala J. M. Field assessment of thermal behaviour of social housing
apartments in Bilbao, Northern Spain. Energy Build. 2013:67:118–135.
https://doi.org/10.1016/J.ENBUILD.2013.07.061
Environmental and Climate Technologies
____________________________________________________________________________ 2020 / 24
79
[32] Grupo Consolidado de Investigación Calidad de Vida en Arquitectura. Caracterización del parque residencial de la
CAPV para la elaboración de un plan de acción a largo plazo. 2018. (in Spanish)
[33] Jiménez Romera C. Tamaño y densidad urbana. Análisis de la ocupación de suelo por las áreas urbanas. Biblioteca
CF+S. 2015. (in Spanish)
