Holistic analysis to reduce energy poverty in social dwellings in southern Spain considering envelope, systems, operational pattern, and income levels

Depósito de investigación de la Universidad de Sevilla https://idus.us.es/ Esta es la versión aceptada del artículo publicado en: This is an accepted manuscript of a paper published in: Energy (2024) : 24/10/2024 DOI: https://doi.org/10.1016/j.energy.2023.129796 Copyright: © 2023 Elsevier Ltd. All rights reserved. El acceso a la versión publicada del artículo puede requerir la suscripción de la revista. Access to the published version may require subscription. “This is an Accepted Manuscript of an article published by Elsevier in [Energy] on [2024], available at: https://doi.org/10.1016/j.energy.2023.129796” Title: Holistic analysis to reduce energy poverty in social dwellings in southern Spain considering envelope, systems, operational pattern, and income levels Authors: David Bienvenido-Huertas1, Daniel Sánchez-García*2, Carlos Rubio-Bellido3, David Marín-García4 1Department of Building Construction, University of Granada. Granada, Spain 2Department of Electrical Engineering, University Carlos III of Madrid, Madrid, Spain 3Department of Building Construction II, University of Seville. Seville, Spain 4Department of Graphical Expression and Building Engineering, University of Seville. Seville, Spain Abstract: Energy renovations carried out in energy poor households should consider a holistic view in the short-, mid- and long-term, especially in warm areas such as Andalusia, in the south of Spain. This study addresses the existing knowledge gap on technological and economic interactions in the energy poverty of family units in warm areas of Spain. To do this, the study performs a parametric analysis. In this parametric analysis, a dataset of 36,230,400 instances was developed considering envelope, HVAC systems, an operational pattern (based on static and adaptive thermal comfort models), and family units’ income levels. Likewise, the energy poverty ratio was compared based on the high share of energy expenditure in income (2M). The results showed that improving envelope and establishing adaptive operational patterns did not effectively reduce energy poverty cases in low-income families in the south of Spain. However, these strategies were appropriate in family units with greater incomes to remove energy poverty cases, regardless of the low reduction in energy consumption by improving the envelope. This study is a starting point to combine social aids, energy improvements and rational energy use through adaptive operational patterns. Keywords: Energy poverty; envelope; parametric analysis; adaptive thermal comfort; warm areas 1. Introduction Generally the built environment has poor energy performance [1,2]. This is mainly due to both the great building development before energy efficiency standards in many countries and the standard development with no demanding criteria until a few years ago [3]. Consequently, building energy consumption is high [4,5], thus leading to energy poverty situations, an uncommon social phenomenon in developed countries until two decades ago. However, it is increasing in all countries due to the recent strong economic crisis and energy increase. Energy poverty is different in developed and developing countries [6]. Energy poverty varies by region: some struggle with energy supply access [7,8] or suitable installations [9], while others face issues meeting basic heating or cooling needs [9,10]. These phenomena depend on the type of country (e.g., inability for keeping household under comfort conditions in developed countries, and difficulty to access to energy in developing countries [11]); however, both phenomena could take place together: low-income family units cannot pay energy bills, thus cutting supply [12]. Consequently, performance policies on energy poverty should be immediately adopted to avoid more serious problems and that combine the energy quality of homes, costs, income and the energy transition [13]. For instance, children living today under poor conditions are likely to be in the same situation as they become adult [14,15]. Governments should therefore address this situation. Recently, the Spanish government adopted the National Strategy against Energy Poverty 2019-2024, aiming to cut energy poverty cases by 50% by 2025 [16]. The four indexes of the European Union Energy Poverty Observatory (EPOV), currently named as Energy Poverty Advisory Hub (EPAH), are used to quantify energy poverty cases, thus assessing energy poverty from various perspectives, although it is mainly the combination of energy expenditure and incomes considering various analysis thresholds [17]. However, these analyses could be limited as low-income users consume minimum energy to reduce bill costs, although their health is affected. Recent studies indicate that energy poverty affects users' health [18,19] as families spend extended periods outside thermal comfort [20], even leading to the death of a household member [21,22]. Performance measures against energy poverty could therefore be useful for family units to keep their dwellings under appropriate thermal comfort conditions [9,10]. It is worth stressing that most energy bill prices are influenced by HVAC systems as the energy consumption of electrical household appliances [23] and domestic hot water [24] is often lower. Consequently, energy poverty cases could be reduced by reducing their energy consumption. In addition, building stock’s decarbonisation goals would be met to reduce the impact of climate change. Several studies have reported the relationship of decarbonisation performance of the built environment and energy poverty [25,26]. However, performing in buildings to tackle climate change could be ineffective to reduce energy poverty. This could be due to rebound effects (consumption expectation increases [27,28]) and strict energy transition policies in some countries [29,30], leading to increased energy poverty when switching fuels [31] (e.g. using gas instead of carbon). For instance, Lise Desvallées discusses the challenges and contradictions faced by social housing providers in Southern Europe when addressing energy efficiency and social inclusion due to obsolete housing and energy poverty. It highlights a preference for retrofitting building envelopes over 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 renewable energy solutions, potentially overlooking the complexity of energy poverty situations in the social housing sector [32]. Building energy renovation could reduce energy consumption, particularly in areas with high climate severity [33], and keep users under thermal comfort conditions a longer time [34], an aspect mainly analysed in the winter months [35,36], although studies on the summer months are increasing [37,38]. However, high investment payments [39] and rebound effects [28] could limit building energy renovation, regardless of the financing plans in Spain [40]. Educating users on operational patterns for HVAC systems could be another performance measure: Gianfrate et al. [41] established that adjusting setpoint temperatures in HVAC systems could reduce the severity of energy poverty cases, and Ghose et al. [42] determined that an appropriate resource use could be among the best energy efficiency performance. The possibility of applying operational patterns based on adaptive thermal comfort models have also been studied. The energy saving in HVAC systems has been analysed by adapting setpoint temperatures to adaptive model limits [43], thus reducing energy consumption [44]. The possibilities of reducing energy poverty has also been studied, obtaining appropriate results in current and future scenarios [45,46]. Various approaches could therefore be used for both energy poverty reduction and appropriate performance, particularly in regions where energy renovation performances are not very effective due to high temperatures. In the state of the art, few studies focused on action limitations in energy poverty cases in warm climate zones. There is therefore a knowledge gap on the performance possibilities of acting in the built environment in which cooling energy demand is greater. This study aims to address the existing limitations by holistically evaluating the energy poverty situation of family units in warm climatic zones, which constitutes the novelty aspect. To do this, the following questions are raised: - Is energy renovation (envelope and HVAC systems) effective in avoiding energy poverty in all cases in southern Spain? - Does the use of adaptive patterns serve to reduce cases of energy poverty? - Is it necessary to adopt measures other than the energy renovation of buildings to avoid energy poverty? In this regard, Andalusia (in the south of Spain) is characterized by warm climate conditions which could be more severe in the next years due to climate change [47]. Likewise, this region is among the most affected by energy poverty [48] due to both a deficient building stock [34] and the fact that the employment and income rate is the lowest in Spain [49]. This study therefore contributes to literature and practice by proposing a holistic analysis of the performance required to reduce energy poverty in the region. The analysis was conducted from two perspectives: building energy characteristics and users’ operational pattern. In economic terms, only family units’ income level was considered. To have a detailed knowledge of the effect of the variables considered on the built environment in southern Spain, a parametric analysis was carried out. In this parametric analysis, configurations of wall, roof, floor, windows, HVAC system, operational pattern and family income were combined, generating a dataset of 36,230,400 instances. 2. Methodology The methodology carried out in this study is divided into 3 different sections: (i) in the first one, the dataset is described , which is composed of an extensive number of building energy simulation results generated in a parametric analysis, which considers multiple U-values for the envelope, multiple performance rates (COP/EER) values for the HVAC system, multiple setpoint temperatures, and finally, multiple income levels; (ii) the second one explains the calculation of the energy prices used in this study; and (iii) the third one, describes the limitations of the study. 2.1. Dataset Numerous dwelling configurations were analyzed using a large dataset to understand energy poverty reduction limitations in southern Spain. A social housing building representing the common geometries of this type of buildings in the region was selected. The building consists of 51 social housing units across four floors, featuring 2 to 3 rooms and a total area of 60 to 90 m2 (Fig. 1). The building was modelled with EnergyPlus and validated according to the ASHRAE Guideline 14’s criteria [50] reported in previous studies [45,51]. The model of the building was parametrized (Fig. 2). Dwellings on ground, middle and top floors were used. Dwellings on the second floor, and not on the first floor, were used as previous studies showed that energy consumption values in dwellings on the first and second floors were the same [45]. A total of 37 dwellings were therefore considered. Parametrization involved configuring each dwelling with varied envelope and system settings. Regarding the envelope, options were explored for façade, floor, roof, and windows. The thermal transmittance of façade, floor, and roof varied between 0.1 and 2.0 W/(m²K), with intervals of 0.1, resulting in 20 different configurations in each element. As a result, there were envelope configurations including both building solutions with poor energy performance (high thermal transmittance values) and effective solutions (low thermal transmittance values). Changing the thermal transmittance value did not vary the useful surface area of dwellings. Likewise, 3 typologies of windows were considered. Table 1 includes their values. Window 1 is a monolithic glass and a metallic framework without thermal bridge break, and Windows 2 and 3 are more advanced solutions, with double glass and frameworks with lower thermal transmittance. On the other hand, various combinations were considered for HVAC systems. The most used system in the south of Spain is the heat pump. A total of 17 typologies of heat pump were considered by varying the coefficient of performance (COP) and the energy efficiency ratio (EER). Table 2 includes their performance values. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 1. Geometry of the building used in the study. Fig. 2. Workflow of the dataset simulation process. Table 1. Typologies of windows. Type of window U-value [W/(m²K)] Window 01 5.7 Window 02 3.1 Window 03 2.5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Table 2. Typologies of HVAC systems. Type of HVAC system EER COP HVAC 01 2.1 2.3 HVAC 02 2.2 2.4 HVAC 03 2.3 2.5 HVAC 04 2.4 2.6 HVAC 05 2.5 2.7 HVAC 06 2.6 2.8 HVAC 07 2.7 2.9 HVAC 08 2.8 3.0 HVAC 09 2.9 3.1 HVAC 10 3.0 3.2 HVAC 11 3.1 3.3 HVAC 12 3.2 3.4 HVAC 13 3.3 3.5 HVAC 14 3.4 3.6 HVAC 15 3.5 3.7 HVAC 16 3.6 3.8 HVAC 17 3.7 3.9 Operational patterns for HVAC systems were studied in both static (fixed setpoint temperatures) and adaptive (changing setpoint temperatures) profiles. These setpoint temperatures were based on EN 16798-1:2019 [52], which is the European adaptive thermal comfort standard. This standard is a development of a previous standard (EN 15251:2007 [53]), which was set with data from the smart controls and thermal comfort (SCATs) project [54]. The standard includes static and adaptive approaches. As for the static approaches, the values established in 3 use categories were considered: (i) Category I, with temperatures of 25.5 ºC for cooling and 21 ºC for heating; (ii) Category II, with temperatures of 26 ºC for cooling and 20 ºC for heating; and (iii) Category III, with temperatures of 27 ºC for cooling and 18 ºC for heating. As for the adaptive model, the development of the EN 16798-1:2019 is similar to other adaptive models, such as that from ASHRAE 55-2019 [55]. Linear correlations were established for upper and lower thermal comfort limits, compared to the running mean outdoor temperature (RMOT). This is a fictitious variable that determines outdoor temperature oscillations in the previous days using weightings. RMOT is useful to know whether the adaptive model could be applied. For this purpose, RMOT should be between 10 and 30 ºC. If not, the adaptive model could not be applied. However, the target in this studio is achieving thermal comfort at all hours in the year (8760), therefore a setpoint range was needed also below 10 and above 30 °C. For this purpose, this study considered the hypothesis used in other studies in which the limit values of RMOT (10 and 30 ºC) are horizontally extended [56], in order consider a comfort range and heating and cooling setpoint temperature for any value of RMOT. As an example, if RMOT has a value of 7 ºC, the limit values of the thermal comfort model for RMOT of 10 ºC are used. It is worth highlighting that the configuration of the set temperatures in Table 3 shows all the possibilities based on the adaptive thermal comfort models. However, cooling conditions with an RMOT lower than 10 ºC were not given in the study. Table 3 summarises the configurations for operational patterns. The limit values associated with the 3 categories of the adaptive thermal comfort model of EN 16798-1 were used. This allowed comparisons to be made between the static and adaptive pattern for each category used. Table 3. Operational patterns. Mode Catego ry Acrony m Cooling setpoint temperature (°C) Heating setpoint temperature (°C) 𝑅𝑀𝑂𝑇 <10 º𝐶 10 º𝐶 ≤ 𝑅𝑀𝑂𝑇 ≤30 º𝐶 𝑅𝑀𝑂𝑇 >30 º𝐶 𝑅𝑀𝑂𝑇 <10 º𝐶 10 º𝐶 ≤ 𝑅𝑀𝑂𝑇 ≤30 º𝐶 𝑅𝑀𝑂𝑇 >30 º𝐶 Static mode 1 Sta-C1 25.5 21 2 Sta-C2 26 20 3 Sta-C3 27 18 Adaptive model 1 Ada-C1 24.1 𝑅𝑀𝑂𝑇 ∙ 0.33 +18.8 + 2 30.7 19.1 𝑅𝑀𝑂𝑇 ∙ 0.33 +18.8 − 3 25.7 2 Ada-C2 25.1 𝑅𝑀𝑂𝑇 ∙ 0.33 +18.8 + 3 31.7 18.1 𝑅𝑀𝑂𝑇 ∙ 0.33 +18.8 − 4 24.7 3 Ada-C3 26.1 𝑅𝑀𝑂𝑇 ∙ 0.33 +18.8 + 4 32.7 17.1 𝑅𝑀𝑂𝑇 ∙ 0.33 +18.8 − 5 23.7 Likewise, the public income indicator of multiple effects (IPREM in Spanish) was used as referential value to establish various income levels. IPREM is an index used in Spain to obtain economic aids. This indicator is not the same as the minimum guaranteed interprofessional wage. IPREM has developed slower than the minimum guaranteed interprofessional wage, so family units with low economic resources could obtain economic aids. The basis of IPREM for the year 2019 was 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 used. A total of 8 income typologies were considered by applying factors between 0.5 and 4 times the IPREM, with intervals of 0.5. Table 4 includes its values. Table 4. Income combinations according to IPREM. Abbreviations Factors applied to IPREM in the hypothesis analysis of family units’ incomes Yearly net income [€] IPREM 0.5 0.5 3,759.80 IPREM 1.0 1.0 7,519.59 IPREM 1.5 1.5 11,279.39 IPREM 2.0 2.0 15,039.18 IPREM 2.5 2.5 18,798.98 IPREM 3.0 3.0 22,558.77 IPREM 3.5 3.5 26,318.57 IPREM 4.0 4.0 30,078.36 Each combination of façade, floor, roofs, windows, HVAC systems, operational patterns, and incomes was applied to 37 dwellings. Floor and roofs were grouped together as no dwelling had roof and floor as envelope elements. A dataset made up of 36,230,400 instances was obtained by combining all these factors. Likewise, all instances were simulated in EnergyPlus by using the internal load profile included in the Spanish building technical code for residential buildings [57]. This profile is characterized by the variation of occupancy loads, equipment, and lighting according to the time and day of the week (Table 5). Moreover, the B4 climate zone was used as it corresponds to one of the areas with the greatest climate severity due to high temperatures according to the climate classification established by the Spanish building technical code. . Table 5. Percentage load distribution in the simulations performed with EnergyPlus. Loads Hours and load (W/m²) 0:00 – 6:59 07:00 – 14:59 15:00 – 17:59 18:00 – 18:59 19:00 – 22:59 23:00 – 23:59 Occupation (sensible load) Weekdays 2.15 0.54 1.08 1.08 1.08 2.15 Weekend 2.15 2.15 2.15 2.15 2.15 2.15 Occupation (latent load) Weekdays 1.36 0.34 0.68 0.68 0.68 1.36 Weekend 1.36 1.36 1.36 1.36 1.36 1.36 Lighting Weekdays and weekend 0.44 1.32 1.32 2.2 4.4 2.2 Equipment Weekdays and weekend 0.44 1.32 1.32 2.2 4.4 2.2 The energy consumption distributions obtained with the average energy consumption values in the region were compared to validate the results of the simulation. To do this, the results included in the Energy and Cities report of the Spanish Energy Club [60] were used. Fig. 3 shows the energy consumption distribution obtained in the dataset and its comparison with the average consumption values in the region. The average energy consumption value in the region was within the result distribution. Lower and greater values were due to the characteristics of the parametric study, considering both high technical performance buildings and low energy performance buildings. Fig. 3. Comparison of actual average energy consumption in the region and simulated energy consumption. 2.2. Energy poverty analysis The high share of energy expenditure in income (2M) was used to analyse energy poverty as it is used by EPOV for the same purpose. The 2M indicator establishes that family units that pay over twice the average national energy expenditure in the energy bill are under energy poverty situation [58]. This indicator is widely used [59,60] because it provides a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 quantitative analysis to energy poverty assessment and to the possibility of adapting the energy expenditure threshold value to the characteristics of each country. In Spain, the representative threshold value has been characterized by energy expenditure. The study by Sánchez-Guevara Sánchez et al. [17] determined that the threshold value in Spain for the 2M indicator is 10%, thus coinciding with the threshold established by Boardman [61]. Thresholds of 10% were compared by determining the energy poverty ratio (EPR) in each case study. EPR is a relationship between household energy cost and income level (Eq. (1)). It is worth stressing that households tried to keep always thermal comfort conditions. Other casuistries related to energy poverty, such as hidden energy poverty, were therefore not considered. 𝐸𝑃𝑅 = 𝐶 𝐼∙100 [%] 𝐶𝑎𝑠𝑒 𝑖𝑛 𝑓𝑢𝑒𝑙 𝑝𝑜𝑣𝑒𝑟𝑡𝑦 if 𝐸𝑃𝑅 ≥ 2𝑀 (10%) (1) where 𝐶 is household energy consumption cost [€], and 𝐼 is household income [€]. Family units’ income level (values related to IPREM) has been previously described. As for the energy cost, energy consumption was obtained in each case through the parametric process. However, it was not household energy cost. The methodology existing in Spain was used to determine it. The rate was the voluntary price for the small consumer (PVPC in Spanish). PVPC was created by the Spanish government in 2014 to establish a regulatory rate that reduce family units’ risk [62]. It is related to low-income family units, so studies on energy poverty in Spain have used it [63,64]. The energy cost that a family unit should pay is obtained by summing several concepts (Eq. (2)): energy term (𝐸𝑇), power term (𝑃𝑇), electricity tax (𝐸𝑙𝑇), rent of measurement equipment (RME), and value added tax (𝑉𝐴𝑇). 𝐸𝑇 is related to energy consumption and is obtained by multiplying energy consumption by the cost of kWh (Eq. (3)). Likewise, 𝐸𝑇 was obtained by considering hourly energy consumption as PVPC has an hourly variable rate. 𝑃𝑇 was applied to always guarantee a power within dwellings. It includes costs from both grid access and marketing margin in the contracted power (Eq. (4)). A power of 4.6 kW was considered, which was appropriate when using a heat pump and electrical household appliances simultaneously. Finally, the remaining concepts are related to taxes: (i) 𝐸𝑙𝑇 was applied, thus increasing 𝐸𝑇 and 𝑃𝑇 by 5.1127% (Eq. (5)); and (ii) 𝑎 𝑉𝐴𝑇 of 21% was applied to the sum of ET, PT, ElT, and EMR (Eq. (6)). Moreover, the time series of PVPC corresponding to the year 2019 was considered. 𝐸𝐶 =𝐸𝑇 +𝑃𝑇 + 𝐸𝑙𝑇 + 𝑅𝑀𝐸 + 𝑉𝐴𝑇 (2) 𝐸𝑇 = 𝐸𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 ∙ 𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑖𝑛 𝑘𝑊ℎ (3) 𝑃𝑇 = 0.115188 ∙ 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑒𝑑 𝑝𝑜𝑤𝑒𝑟 ∙ 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑎𝑦𝑠 (4) 𝐸𝑙𝑇 = 0.051127 ∙(𝐸𝑇 +𝑃𝑇) (5) 𝑉𝐴𝑇 = 0.21 ∙ (𝐸𝑇 +𝑃𝑇 + 𝐸𝑙𝑇 + 𝑅𝑀𝐸) (6) 2.3. Limitations of the study A parametric analysis has been performed to address most of the built environment; however, there are limitations. The first one was associated with family performance to assess energy poverty. In this study, family units always tried to maintain thermal comfort conditions to assess family units that did not compromise thermal comfort conditions or health. Operational patterns varied users’ behaviour, but always maintained thermal comfort conditions indoors. The use of the 2M indicator was therefore logical and consistent, and other possibilities of energy poverty, such as hidden energy poverty, were not considered. The results of this study could be different in those families that reduced energy cost with less use of housing systems. Another limitation was the analysis scale. Energy poverty was assessed on an annual scale. However, results could vary monthly, especially in the months with greater demand. Finally, implementing social aids to address energy bill costs was not considered. The socioeconomic component was the family units’ income level through percentage relationships with IPREM. Results could therefore vary if social assistance is included. From the technical perspective of the building and income level, the results of this study aimed to address the variation of the energy poverty situation of families that live in warm areas. 3. Results First, the influence of the thermal transmittance values of façade and roofs/floor on energy poverty cases was analysed. Figs. 4 and 5 show the distributions of EPR values according to the various opaque envelope configurations and using a static operational pattern (without distinguishing the configurations of static setpoint temperatures), as well as the various family units’ income levels. As for the influence of the thermal transmittance values of the façade, the use of low thermal transmittance values was related to EPR distributions with lower values. The lowest thermal transmittance configuration reduced EPR quartile values between 1 and 5%. Nonetheless, these variations could be insignificant considering the number of cases that could overcome 10% of the energy poverty threshold value. Fig. 4 shows that the distributions related to low income levels (IPREM 0.5, 1, 1.5, and 2) obtained more energy poverty cases: (i) IPREM 0.5 obtained EPR values between 21 and 33% in Q1 , between 22 and 36% in Q2, and between 23 and 39% in Q3, with a maximum value of 52%; (ii) IPREM 1.0 obtained EPR values between 11 and 16% in Q1, between 11 and 18% in Q2, between 12 and 20% in Q3, and maximum values between 14 and 26%; (iii) IPREM 1.5 obtained maximum values between 11 and 17% in the three quartiles, although EPR value was lower than 10% in some cases; and (iv) some combinations with the lowest thermal transmittance obtained quartiles with an EPR value greater than 10%. Incomes equal or greater than IPREM 2.5 did not obtain energy poverty cases, so family units’ income level was a significant factor to remove energy poverty risk. Likewise, the floor on which the dwelling was located influenced EPR values. This study analysed three types of floors (ground, middle and top floors), and EPR distributions depended on them. Dwellings on the ground floor presented lower energy poverty risk as their distributions 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 obtained a lower interquartile range and a lower average value. EPR distributions on the ground floor obtained reductions between 5 and 20% with an income level corresponding to IPREM 0.5, between 3 and 10% with IPREM 1.0, between 2 and 7% with IPREM 1.5, between 1 and 5% with IPREM 2.0, between 1 and 4% with IPREM 2.5, between 1 and 3% with IPREM 3.0, and between 1 and 2% with IPREM 3.5 and 4.0. Dwellings on the ground floor influenced when family units’ income level was very low. When family units’ income level increased, the difference between living on the ground floor and on the other floors did not significantly vary EPR values (aspect shown in the similarity of EPR distributions). Allocating lowincome family units in dwellings on the top floor would imply greater energy poverty risk as EPR value is increased (due to the increase of energy consumption in the dwelling). The same behaviour was detected in the thermal transmittance values related to roof and floor (Fig. 5). The lowest thermal transmittance values obtained EPR values lower than the worst envelope configurations, but their impact was low (with EPR reductions between 1 and 2%). The use of various typologies of windows had a similar effect (Fig. 6). Although EPR values were reduced between 1 and 5% by improving the window, the percentage of instances with values greater than the energy poverty threshold did not significantly vary. Thus, the income level significantly influenced the possibility of reducing EPR values. In the analyses in Figs. 4 and 5, family units behaved according to a static operational pattern, i.e., users used fixed configurations for setpoint temperatures of HVAC systems. However, adopting adaptive operational patterns could be an opportunity for lower energy consumption, thus reducing energy poverty risk. To analyse this aspect, Fig. 7 shows the point clouds with EPR values by using adaptive and static patterns. The configurations of each operational pattern were included in Table 3, in the Methodology section. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 4. Distribution of EPR values according to façades and income level by using a static operational pattern. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65  Families with income levels equal to or lower than the IPREM will always be in a situation of energy poverty. This occurs regardless of the technical characteristics of the dwelling. The improvement of the envelope and HVAC systems meant improvements in energy poverty for families with incomes higher than the IPREM. Therefore, there is a clear relationship between the effectiveness of the building improvement and the family's income level to see if it is an effective measure against energy poverty.  In the case of low-income families, the incorporation of socioeconomic aid that complements the technical improvement of buildings is essential. It has been confirmed that families with high incomes did not present situations of energy poverty in any of the combinations of buildings obtained. Therefore, it is essential to propose combined measures to improve income together with the improvement of buildings in order to obtain good results.  In addition to technical and economic issues, it is important to highlight the thermal adaptation of users as a measure to reduce energy poverty. In this sense, the results showed how the use of adaptive patterns was one of the most appropriate measures to reduce cases of energy poverty. This did not happen with the static patterns in which a high number of cases of energy poverty were achieved. Thus, the use of adaptive patterns based on EN 16798-1 could lead to an improvement in the situation of families, without the need to initially implement financial aid or improvements to the building. The combined use of all measures could be the key to achieving coherent action to improve the situation of families in hot climate zones. To conclude, the results of this study are significant to design an appropriate strategy to remove energy poverty in the dwelling in the south of Spain. Adopting energy renovation strategies is not an effective measure by itself, so appropriate behaviour strategies (based on adaptive thermal comfort models) should be included, as well as social aids. These aspects would be useful to prevent dwelling from being in energy poverty situations in current and future scenarios. It is convenient to clarify that the strategy of the policies resulting from the study presents an application for hot climatic zones. In cold areas, variations in the results are to be expected, since the technological improvement of the building could take on a greater role with respect to operational patterns. This difference in the expected energy savings between energy improvement of buildings and operational patterns depending on the climatic zone has been reflected in previous studies, so the results could be different from those of hot climatic zones. In addition to this aspect, further studies should address some limitations. For instance, the variations presented by assessing energy poverty at different levels. Previous studies have shown that assessing energy poverty in the months of greater demand (e.g., January and July) could vary the results, compared to annual analyses. The monthly analysis could be considered by using parametric criteria in the future context, thus determining the effectiveness of building design and operational pattern to reduce energy poverty in the future. In addition, the significance of income levels and aids should be addressed. Previous studies have shown that economic aids and unemployment subsidies in Spain are not effective by themselves to prevent cases of energy poverty. In this study, the component of financial aid in the energy bill has not been addressed. As a result of the improvement obtained in this study when higher income and building improvements were combined, further studies should parametrically analyse appropriate thresholds for economic aids according to family units’ income levels. Finally, the use of renewable energy systems, such as photovoltaics or geothermal [83], should be evaluated. In this study, technical improvement was limited to envelope and HVAC systems. In future studies, the improvement achieved with the use of renewable energy systems and the effect of variations in energy contracts for these systems, such as surplus tariffs, should be addressed. Acknowledgment This study was funded by the Spanish Ministry of Science and Innovation, under the research project PID2021-122437OA- I00 “Positive Energy Buildings Potential for Climate Change Adaptation and Energy Poverty Mitigation (+ENERPOT)” and the Andalusian Ministry of Development, Articulation of the Territory and Housing, under the research project US.22-02 “Implicaciones en la mitigación del cambio climático y de la pobreza energética mediante nuevo modelo de confort adaptativo para viviendas sociales (ImplicAdapt)”. The authors also acknowledge the support provided by the Thematic Network 722RT0135 “Red Iberoamericana de Pobreza Energética y Bienestar Ambiental (RIPEBA)” financed by the call for Thematic Networks of the CYTED Program for 2021. Abbreviations 2M: High share of energy expenditure in income Ada -C2: Adaptive mode, Category 2 Ada -C3: Adaptive mode, Category 3 Ada-C1: Adaptive mode, Category 1 ASHRAE: American Society of Heating, Refrigerating and Air-Conditioning Engineers COP: Coefficient of performance EER: Energy efficiency ratio EPAH: Energy Poverty Advisory Hub EPOV: Energy Poverty Observatory EPR: Energy poverty ratio HVAC: Heating, ventilation and air conditioning IPREM: Public income indicator of multiple effects PVPC: Voluntary price for the small consumer RMOT: Running mean outdoor temperature SCATs: Smart controls and thermal comfort 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Sta-C1: Static mode, Category 1 Sta-C2: Static mode, Category 2 Sta-C3: Static mode, Category 3 U: Thermal transmittance References [1] Kurtz F, Monzón M, López-Mesa B. 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