Decision-support method for profitable residential energy retrofitting based on energy-related occupant behaviour

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: Journal of Cleaner Production (2019): 06/2/2024 DOI: https://doi.org/10.1016/j.jclepro.2019.03.089 Copyright: /© 2019 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 [Journal of Cleaner Production] on [2019], available at: : https://doi.org/10.1016/j.jclepro.2019.03.089” 1 Decision-support method for profitable residential energy retrofitting based 1 on energy-related occupant behaviour. 2 3 Abstract 4 Low-carbon energy retrofitting in buildings plays an important role because 75% of European 5 buildings are considered inefficient and more than 40% are currently over 50 years old. The 6 economic reinvestment of energy retrofit actions through reduced energy bills, as energy 7 directives promote, greatly depends upon the energy consumption patterns. In support of the 8 decision-making process towards a low-carbon energy transition in multi-family buildings, this 9 paper introduces a novel assessment method that evaluates the profitability of energy efficiency 10 measures, according to standard operating conditions derived from energy performance 11 certificate procedures and real occupant energy consumption scenarios, through a parametric 12 analysis. The aim is to assess the real energy and economic savings of retrofitting actions, 13 depending on different energy-related occupant scenarios, and to enable comparisons with other 14 buildings, providing a valuable model to identify the most feasible and low-carbon energy 15 strategies in residential energy retrofitting. A Spanish multi-family building from 1942 is taken 16 as the reference case study. The results show that energy savings for dwellings vary up to 80%, 17 and the net present value per dwelling differs by up to 20,000€ between different energy 18 consumption patterns. The most appropriate energy efficiency measures according to low, 19 medium or high consumption scenarios are highlighted. 20 21 Keywords: energy renovation; low-carbon energy; occupant behaviour; consumption 22 scenarios; decision-making; multi-family buildings. 23 2 NOMENCLATURE 24 𝑎 operation year 𝐶𝑓 annual cash flow COP coefficient of performance DHW domestic hot water ED energy demand (kWh/m2 a) EEM energy efficiency measure EER energy efficiency ratio EIFS exterior insulation and finishing system EPBD Energy Performance of Building Directive EPC Energy Performance Certificates EPS expanded polystyrene insulation HP heat pump IC investment cost (€) NPV net present value (€) OECD Organisation for Economic Co-operation and Development PBP payback period PEC primary energy consumption SHGC seasonal heat gain coefficient of shading devices 𝑟 discount rate TB thermal-break U thermal transmittance (W/m2K) XPS extruded polystyrene 25 3 1. INTRODUCTION 26 The building sector is responsible for 36% of global final energy consumption and more than 27 55% of the electricity demand (International Energy Agency, 2017, 2013a). In the European 28 Union, this sector is responsible for approximately 40% of energy consumption and 36% of 29 CO2 emissions (European Commission, 2018, 2014), of which 70% corresponds to heating, 30 cooling and domestic hot water (International Energy Agency, 2017, 2013a), being mostly 31 fossil fuels based (European Commission, 2016). 32 33 European statements consider that almost 75% of building stock is energy inefficient (European 34 Commission, 2015a). Regarding building aging, more than 40% of current housing stock are 35 over 50 years old (Aksoezen et al., 2015; International Energy Agency, 2017) and 75% of 36 buildings anticipated for 2050 are already built, in the case of the European Union and OECD 37 member countries (Cuchí and Sweatman, 2013; International Energy Agency, 2013b). 38 Therefore, the energy renovation of existing buildings has a huge potential to lead to significant 39 energy savings (Arumägi et al., 2017). 40 41 Following these guidelines, European policies aim to support low-carbon energy transition 42 through sustainable renovation strategies. The European Energy Performance of Buildings 43 Directive (EPBD) (European Commission, 2018) underlines the need to implement new 44 methods and protocols for energy retrofitting processes in the building stock, ensuring viable 45 and efficient operations to achieve environmental targets, as well as to improve the quality of 46 life of citizens (Thuvander et al., 2012). Moreover, EPBD considers that financing through 47 energy bill savings is the main option to finance the energy retrofitting process (European 48 Commission, 2018). However, it is also essential to consider the users’ energy consumption 49 attitude, and each socioeconomic context, to achieve sustainable and efficient interventions 50 (Heiskanen et al., 2013). Lizana et al. (2016) stated that economic savings, related to energy 51 bills, might not be enough for end-users to support energy retrofitting at current energy prices. 52 In addition, as pointed by Vilches et al. (2017), families with low income levels represent an 53 important barrier for carrying out retrofitting actions. 54 55 European standards and regulations are developing energy performance calculation methods to 56 support the EPBD, focused on enabling comparisons with other buildings and evaluating real 57 energy and economic savings in building retrofitting processes. The most common are the 58 energy assessment methods, in the form of Energy Performance Certificates (EPC) (AENOR, 59 2012; European Commission, 2010), which are aimed at showing the energy performance of 60 buildings and informing end-users of potential energy savings (European Commission, 2015a). 61 However, most EPC procedures are based on standard operating conditions, occupancy 62 profiles, and other default values that generate discrepancies between energy simulation and 63 real energy use (Lizana et al., 2017). The results of these standard procedures distort the EPC 64 purpose of informing about the real energy saving potential (European Commission, 2015a), so 65 one of the main challenges in energy renovation is how to obtain realistic energy saving values 66 according to real energy consumption patterns. 67 68 Different studies have identified and discussed the high impact of energy-related occupant 69 behaviour on the economic and energy performance of low-carbon retrofitting actions. Wallis 70 et al. (2016) suggested that the use of energy behavioural attitudes provides more detailed 71 information about the electricity consumption and thus allows choosing a more appropriate 72 policy planning. According to Hong et al. (2016), occupant behaviour greatly influences the 73 real consumption by using the thermostat settings, opening or closing windows, use of air 74 conditioning systems, lights and stand-by of appliances, among others. Stieß and Dunkelberg 75 4 (2013) developed an empirical study of 1000 homeowners in Germany which considers the 76 importance of users’ knowledge about energy consumption related to the decision-making 77 process in standard refurbishment measures. Bedir and Kara (2017) studied the influence of 78 consumption patterns on different profiles of electricity consumption in Dutch housing stock. 79 Liang et al. (2016) proposed the need to design a decision-making system that considers the 80 influence of the occupation model and the occupant behaviour to achieve a green retrofit. Li et 81 al. (2018) identified the influence of different types of end-users in the final success of 82 interventions, suggesting participatory decision/evaluation procedures that involve them in 83 sustainable projects. Perera et al. (2018) stated that, including a socioeconomic evaluation is 84 crucial, to identify the most desirable interventions in building renovation for different profiles, 85 through a multi criteria decision-making approach. Serrano-Jiménez et al. (2017) introduced a 86 new energy renovation strategy which proposed different levels of intervention (mild, moderate 87 and intense), based on the socioeconomic context of each region, going against the European 88 concept of deep renovation adopted by the Global Building Performance Network (GBPN, 89 2013), and evaluating interventions adapted to each requirement (Femenías et al., 2018). 90 91 As a step forward, this research defines a new economic and energy assessment method of 92 energy efficiency measures to support decision-making in energy retrofitting of multi-family 93 buildings. It involves a parametric analysis between different energy retrofit alternatives and 94 energy consumption scenarios of dwellings (standard and real energy consumption profiles: 95 high, medium and low), overcoming the problem that emerges when different users or 96 stakeholders do not follow the consumption standards of EPC procedures. The method allows 97 identifying best available solutions for all dwelling scenarios, guaranteeing the global 98 profitability of actions, according to every energy-related occupant behaviour. 99 100 The novelty of this method is based on the integration of real scenarios based on energy-related 101 occupant behaviour in energy simulations together with those operating parameters established 102 by the EPC standards, to evaluate both the energy and economic savings of energy efficiency 103 measures, through a sensitivity analysis. This procedure fulfils two main purposes of the EPBD 104 and EPC [24]: showing the energy performance of the building to enable comparisons with 105 other buildings; and, informing end-users of potential energy savings, in order to motivate them 106 to invest in improving the energy efficiency of the building. It uses a model’s iterative 107 calibration procedure, based on real building performance, which allows a high final accuracy 108 to be achieved. This method enables a new procedure that allows homeowners, landlords or 109 investors to identify the most appropriate energy retrofitting actions in each scenario, as 110 suggested by Bolis et al. (2017) or Pombo et al. (2016), showing real economic feasibility. In 111 addition, the graphic output of the results allows sustainable strategies to be designed, according 112 to the effectiveness and feasibility of solutions, for all energy-related behaviour contexts. 113 114 The paper is structured as follows. Firstly, the method is defined in a general scheme, where 115 the main contributions are highlighted, and the five stages into which it is divided are defined. 116 Secondly, the method is applied in a residential neighbourhood in Southern Europe, considered 117 as a reference multi-family building typology, due to its construction period, constructive 118 composition and low-medium income population. Thirdly, energy efficiency measures (EEMs) 119 and packages are evaluated in different energy consumption scenarios, where the total 120 investment cost (IC), the annual thermal energy demand (ED), the primary energy consumption 121 (PEC), and the Net Present Value (NPV), are calculated and compared. Finally, the results are 122 discussed, and the most appropriate measures or packages are identified, according to each 123 scenario, as well as reporting strategies and guidelines to support the decision-making process 124 for a sustainable low-carbon energy retrofitting. 125 5 2. MATERIALS AND METHOD 126 Figure 1 defines the assessment method to evaluate the energy savings and economic 127 reinvestments of energy retrofit projects in residential buildings. The method is designed to be 128 applied and adapted to any residential neighbourhood by previously requiring real energy data, 129 per dwelling, to identify the different energy consumption patterns of their occupants. 130 131 132 Figure 1. Five-stage operation of the decision-support assessment method. 133 134 As a step beyond the previous studies reported in the introduction, this research involves extra 135 variables by carrying out a parametric analysis with four consumption scenarios, three of them 136 according to real data from the occupants, and one of them, following the standard parameters 137 of EPC procedures. This research extends the results obtained in previous studies where the 138 return on investment, in different cost-optimal measures, are compared with one energy 139 consumption pattern, such as in Tadeu et al. (2016), Lizana et al. (2016), or Serrano-Jiménez 140 et al. (2017). This concept promotes decision-making in energy retrofitting through a joint 141 assessment of different energy consumption patterns. Stages 4 and 5 are in continuous relation 142 with Stage 2, which generates a more complete sensitivity analysis and helps the investor to 143 identify which proposals would be the most appropriate for each case study. In addition, the 144 inclusion of Stage 3, which is organised into three groups of passive, active and packages of 145 actions, allows useful results to be obtained for the small investor on the performance of 146 individual actions, and offers an overall analysis of the wide range of possibilities that induces 147 energy retrofitting in different proposals or packages (Ascione et al., 2015). The five stages, 148 into which this method is structured, are defined in detail below. 149 150 • Stage 1. Initial state of the existing building. The diagnosis of the initial state of the building 151 is obtained through technical inspections and energy audits following different normalised 152 procedures (AENOR, 2015; Gobierno de España, 2013a, 2013b) and specific technical manuals 153 and codes (Ministerio de Fomento, 2013; Ministerio de Vivienda, 2006). Building geometry, 154 location, orientation, constructive composition of the building envelope (façades, roofs, floors 155 and windows), ventilation and infiltration rates, as well as heating, cooling and hot water 156 systems, are characterised. 157 158 • Stage 2. Energy-related occupant behaviour patterns. The operating conditions and energy 159 consumption patterns in each dwelling are evaluated for the energy simulation, through 160 occupant surveys and energy bill assessments. Hourly operating schedules of occupation 161 (weekday and weekend), operating profiles of systems, lighting for internal gains, and set-point 162 temperatures, are specifically defined for each energy-related scenario. Four scenarios are 163 6 considered for the sensitivity analysis. Scenario 1 is characterised by the operating conditions 164 and default values of local EPC procedures. Scenarios 2, 3 and 4 represent the low, medium 165 and high consumption profiles for the case study, which are calibrated according to real energy 166 consumption data (energy bills), through an iterative calibration process. Further details of the 167 scenarios are described below: 168 - Scenario 1 is defined by the operating conditions and fixed parameters used for the EPC 169 in the region (European Commission, 2010; Gobierno de España, 2013b; Ministerio de 170 Vivienda, 2009). Set-point temperatures for on-peak and off-peak occupancy periods are 171 fixed at 25ºC and 27ºC for cooling, respectively, and 20ºC and 17ºC for heating. The 172 internal gains generated by occupants, lighting and appliances are considered according 173 to specific schedules. 174 - Scenario 2 considers the highest energy consumption pattern among all dwellings, 175 according to the results obtained in surveys and energy bills. Operating conditions are 176 based on survey results and calibrated according to energy bill data. 177 - Scenario 3 is characterised by the medium energy consumption pattern of all dwellings 178 according to the results obtained in surveys and energy bills. Operating conditions are 179 based on surveys and calibrated according to the performance, which is determined 180 through the average of the energy bill data. 181 - Scenario 4 considers the lowest energy consumption pattern among all dwellings 182 according to the results obtained in surveys and energy bills. Operating conditions are 183 based on surveys and calibrated according to energy bill data. 184 185 • Stage 3. EEMs and packages for energy retrofitting. A portfolio of individual technical 186 EEMs and packages to reduce thermal energy demand and consumption are defined and 187 characterised. Aiming to facilitate the comparison between the results, solutions are organised 188 into three groups: individual passive measures (group A); individual active measures (group 189 B); and, packages of measures (group C). The investment cost (IC) and the increased 190 maintenance costs are defined for each EEM. Data are obtained from local databases and 191 manufacturers’ reports. 192 193 • Stage 4. Energy assessment. The current energy performance of the building is evaluated by 194 the DOE 2.2 simulation engine. Then, the implementation of the proposed EEMs and packages 195 are simulated in each energy consumption scenario. The initial energy models for each scenario 196 are calibrated considering the different energy consumption profiles, which were characterised 197 in Stage 2. 198 199 • Stage 5. Economic assessment. The economic reinvestment of EEMs for each energy 200 consumption scenario is calculated through the Net Present Value (NPV). The NPV is a useful 201 economic concept for analysing the profitability of a planned investment or project. This term 202 is calculated within this research for each energy efficiency measure or package, and offers the 203 difference between the present value of cash inflows, and the present value of cash outflows 204 over a period of time. NPV is evaluated according to Eq. 1, where 𝑎 is the operation year, 𝑟 the 205 discount rate, and 𝐶𝑓 the annual cash flow. 206 207 𝑁𝑃𝑉 = ∑ 𝐶𝑓 (1+𝑟)𝑎 𝑎 𝑎=1 (1) 208 The operation year (𝑎) to calculate the NPV can vary between 15 and 30 years, according to 209 each socioeconomic context of application (Short et al., 2005). For the selected case study, the 210 7 operation year is set at 15 years, due to the high percentage of aging population and the socio- 211 economic level being low-medium, which demands a short-term amortization period (Kovacic 212 et al., 2015). After 15 years, most of the elderly will be over 80 years old, which is nearly the 213 average national life expectancy (Instituto Nacional de Estadística, 2013; Serrano-Jiménez et 214 al., 2018). 215 216 The discount rate (𝑟) is the rate of return used in a discounted cash flow analysis to determine 217 the present value of future cash flows. This is an important value that should be decided for 218 each context, according to the economic situation. Figure 2 shows the discount rate values of 219 low-risk banking products from the National Bank of Spain (Banco de España, 2017), during 220 recent years. Although there has been a significant drop in the trend, due to the financial crisis, 221 the end of the quantitative easing has meant that new economic expectations for the European 222 context estimate an increase of these rates, in a range of between 3 and 6%, for the coming 223 years (Hermelink et al., 2016; Mata et al., 2015). Therefore, 4.5% has been fixed as a discount 224 rate that is adapted to these new expectations, in addition to taking into account this application 225 context with reduced investments, and a moderate investor profile. 226 227 228 Figure 2. Evolution of the discount ratio values during the last years. 229 230 Lastly, 𝐶𝑓 is calculated as a function of the initial IC (€) and the annual economic savings per 231 dwelling (€), which is based on annual operating costs and the increase in annual maintenance 232 costs. In fact, annual operating costs (€) include an annual energy price increase of 4.5%, 233 according to its evolution over recent years (IDAE, 2015a, 2015b), and the different expected 234 scenarios for each energy source (IDAE, 2011; Prasanna et al., 2018). These values also need 235 to be adapted to each region and socioeconomic context. 236 3. REFERENCE CASE STUDY 237 A residential set of multi-family buildings known as "Remedios Viejo", and located in Seville 238 (Spain), was selected as the case study (Figure 3). It was built in 1942 and represents a reference 239 building sample of the mid-twentieth century in Mediterranean cities that currently has several 240 energy renovation needs (Barrios-Padura et al., 2015; Gamarra et al., 2018). This case study 241 pertains to the large housing stock of European cities, with more than a third of multi-family 242 buildings, prior to 1970 (European Commission, 2015b). Moreover, it presents an aging 243 population with 32% of its inhabitants over 65 years of age, representative of the world 244 population aging trend. 245 246 The neighbourhood has 324 dwellings that are divided into nine closed blocks. Each block 247 consists of four multi-family residential buildings of three storeys in height, enclosing a private 248 8 inner courtyard. All the dwellings have the same size, and occupancy ranges between one and 249 four residents per dwelling, with an average occupation value of 2.61 (IECA, 2012). 250 251 The residents of these buildings participated in this research, by responding to a brief survey 252 about the operating conditions and occupation periods in their dwellings, as well as to questions 253 related to behavioural patterns in energy consumption. The occupants also provided energy bills 254 with real energy consumption values from recent months. The participation sample was 255 collected from 176 dwellings, which represents 54% of the apartments in the neighbourhood. 256 257 258 Figure 3. Aerial location of the case study. 259 260 The case study was also characterised through technical inspections and audits. Main 261 characterisation parameters for the energy simulation are summarised in Table 1. Data are 262 divided into two groups: building envelopes and systems. 263 264 Table 1. Building characterisation for the energy assessment. Main parameters. 265 Element Characterisation Values Envelope Ventilation and infiltration 1 Permeability of windows (Pwindow) 80 (m3/h·m2 at 100 Pa of pressure) 1.25 Ach/h Permeability of walls (Pwall) 2.7 · Volume (m3/h) Windows Thermal Transmittance (U) Frame (20%) 5.7 W/m2·K 5.7 W/m2·K Glass (80%) 5.7 W/m2·K Solar Factor (SF) (0-1 value) 0.75 Absorptivity of frame (α) 0.70 Permeability of windows ≥80 m3/h·m2 Seasonal solar heat gain coefficient of Window assembly (SHGC) 30% of external solar protection for winter and summer 0.7 Façade Thermal transmittance (U) 2.68 W/m2·K Roof 1.49 W/m2·K Floor 3.58 W/m2·K Systems DHW Type: LPG Boiler Percentage of use: 100% Nominal yield (%) 0.85 Nominal power (kW) 24 kW Minimum solar contribution (%) 0 % Cooling Type: Air-air direct expansion, HP-E (Split) Percentage of use: 100% EER 2.5 Capacity 4.2 kW Consumption 1.68 kW Heating Type: Air-air direct expansion, HP-E (Split) Percentage of use: 60% COP 2.7 Capacity (kW) 4.5 kW Consumption 1.66 kW Type: Electrical heating (Joule Effect) Percentage of use: 40% Nominal yield (%) 1 Capacity (kW) 2 kW 266 15 Figure 9 illustrates the energy and economic performance of packages of measures combining 421 specific solutions regarding groups A and B (group C) for all the energy consumption scenarios. 422 423 424 Figure 9. Sensitivity analysis between NPV and PEC of packages. 425 426 The impact of energy consumption patterns on PEC and NPV is highly significant. There are 427 some packages that reach an 80% energy saving. Regarding the NPV values, there are very 428 high variations between one scenario and another. The same package can have enormous gains 429 for one scenario, or huge losses for another, with variations of up to 20,000€. 430 431 For Scenario 1 and 2, the profitability of solutions is mostly positive, however for Scenarios 3 432 and 4, most of the values are negative. In addition, the dotted lines that join each package are 433 very steep, which supposes a great variation between one consumption pattern and another. 434 Comparing every package, although c9 has the best energy performance, the NPV reinvestment 435 values can be favourable or unfavourable, according to each scenario. In fact, no package of 436 measures has favourable NPV values in Scenario 4, so the economic effectiveness of these 437 actions is only obtained with medium or high consumption patterns. 438 439 Comparing the reported results, a high variation in economic and energy performance of energy 440 retrofitting measures is found per energy consumption scenario. These new findings highlight 441 the importance of adjusting decision-making models in energy retrofitting to real energy 442 consumption patterns, and not only taking into account standard operating conditions, to fulfil 443 two main purposes of the EPBD and EPC procedures: enabling comparisons with other 444 buildings, and informing end-users of potential energy savings. All these advances justify the 445 significance of this study, promoting profitable and efficient energy renovation proposals 446 adjusted to the socioeconomic context of each neighbourhood. 447 448 Lastly, other particular contribution of this method is the graphic output of the obtained results, 449 which summaries the sensitivity analysis of solutions, comparing energy performance and the 450 economic return on investment per scenario. It allows technicians, property owners, end-users 451 and other stakeholders an easy check of different measures through an understandable graph, 452 in which horizontal and vertical slopes highlight the economic affordability of solutions. It also 453 16 facilities the decision-making in an early design stage of energy retrofitting interventions, and 454 promotes a responsible and optimized building renovation. 455 5. CONCLUSIONS 456 This research develops a new procedure to support the decision-making process towards a 457 sustainable energy retrofitting in the multi-family building stock. Different energy efficiency 458 measures and packages are evaluated, through a parametric analysis in a reference multi-family 459 building in Spain. The novelty of this method is based on the combination of energy and economic 460 assessment of solutions in four different energy consumption scenarios, one from standard 461 operating conditions from the national Energy Performance Certificate (EPC) procedure, and 462 the others, from real energy consumption patterns (high, medium and low). This procedure 463 fulfils two main purposes of the EPBD and EPC procedures, showing the energy performance 464 of the building to enable comparisons with other buildings and informing end-users of potential 465 energy savings, in order to motivate them to invest in improving the energy efficiency of the 466 building. This method addresses new strategies for policy making processes by promoting 467 energy renovation strategies through a profitability analysis based on real energy consumption 468 data, and highlighting most appropriate energy efficiency measures according to real needs, 469 leading to sustainable and profitable energy retrofitting actions. 470 471 The results obtained show that significant variations can be achieved between the different 472 scenarios per dwelling, reaching, for the same energy efficiency measure, from 20 to 80% energy 473 savings, and up to 20,000€ of variation in NPV values, according to the occupant behaviour 474 scenario. Thus, to reach a high level of cost effectiveness, each intervention must be fully analysed 475 according to each energy consumption pattern. 476 477 Considering NPV values, there are actions that have a positive or negative economic performance, 478 according to the scenario in which they are analysed. It is also important to stress the importance 479 of previously establishing the discount rate, as it is a highly influential factor in the economic 480 context in which the study is located. The lines linking the economic performance of each measure, 481 in different scenarios, vary considerably depending on whether they are active, passive or packages 482 of measures. Horizontal slopes in graphics ensure a major investment security for the users, 483 whereas higher slopes lead to large profits or losses, in the long-term, according to the consumption 484 patterns. 485 486 Regarding the performance of energy efficiency measures, passive measures are the best energy 487 efficiency actions for low-energy consumption patterns. Some active measures might be included 488 in medium-energy consumption levels, while in high-energy consumption levels, it would be 489 highly beneficial to include active energy efficiency measures, or packages, as they are likely to 490 produce high reduction in energy consumption and very high economic reinvestment, according 491 to the NPV results. There are active measures, such as b1 (efficient heat pump), and b6 (individual 492 biomass boiler), that have a beneficial NPV assessment in the four scenarios, so their 493 implementation would be economically viable in all the consumption patterns of this case study. 494 Otherwise, passive measures, beyond regulating interior thermal comfort, introduce substantial 495 reduction of the percentages of energy consumption with low investment risk, according to the 496 most unfavourable scenario. 497 498 Finally, it is highlighted that fixed operating conditions in EPC procedures are excessive when 499 compared with real consumption data, which implies imbalances in the results offered by official 500 procedures for retrofitting criteria. This important finding represents a political challenge to 501 17 overcome by official procedures, being a possible starting point for future research, towards more 502 realistic procedures of energy and economic assessment for energy retrofitting solutions. 503 ACKNOWLEDGEMENTS 504 This work was supported as part of the European Regional Development Fund (ERDF) of the 505 European Union for financial support through the project “(Re)habitation + (Re)generation + 506 (Re)programming. The recycling and sustainable management of Andalusian building stock. 507 Management of habitable surroundings from criteria of active aging, gender and urban 508 habitability”, and also as part of the ClimACT Project (SOE1/P3/P0429EU), within the Interreg 509 Sudoe Programme. The authors gratefully acknowledge the financial support of the Spanish 510 Ministry of Education and Research through two, pre-doctoral contracts, granted to Francisco 511 Jesús Lizana Moral (FPU14/06583) and Antonio José Serrano Jimenez (FPU15/00070). This 512 research was also made possible due to the support of the “Institute of Architecture and Building 513 Science” IUACC from the University of Seville (V-IUACC grants, mode B, 2018). 514 REFERENCES 515 AENOR, 2015. UNE-EN 41805:2015. Building diagnosis. Madrid. Spain. 516 AENOR, 2012. UNE-EN 15217:2012. Energy performance of buildings. Methods for 517 expressing energy performance and for energy certification of buildings. 518 Aksoezen, M., Daniel, M., Hassler, U., Kohler, N., 2015. Building age as an indicator for energy 519 consumption. Energy Build. 87, 74–86. https://doi.org/10.1016/j.enbuild.2014.10.074 520 Arumägi, E., Simson, R., Kuusk, K., Kalamees, T., 2017. 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