Evaluation of energy and lighting in schools considering solar protections

Energy Reports 12 (2024) 1157–1177 Available online 18 July 2024 2352-4847/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). Research Paper Evaluation of energy and lighting in schools considering solar protections Cristi´ an Mu˜ noz-Viveros a , Carlos Rubio Bellido b , Alexis P´ erez-Fargallo c,* a Department of Theory and Design, Faculty of Architecture, Construction, and Design, University of Bío-Bío, Concepci´ on, Chile b Department of Building Construction II, University of Seville, Seville 41012, Spain c Faculty of Engineering, Architecture and Design, University of San Sebasti´ an, Concepci´ on, Chile ARTICLE INFO Keywords: Energy optimization Passive architecture Solar protections Lighting performance School classrooms ABSTRACT The implementation of the solar protections laid out in the regulations and certification systems decreases cooling demands, although it has an uncontrolled and even unforeseen effect on daylighting. Achieving a balance between both requirements is a challenge for facade design, energy behavior, and lighting performance since restricting solar radiation contributions decreases daylight’s contribution. This research aims to define a methodology that allows, in the early design stage, the evaluation of solar protection solutions that consider the optimal performance of daylight using annual dynamic indicators while maintaining adequate energy-saving levels. For this purpose, cooling energy consumption and dynamic indicators have been considered as the primary indicators, namely Spatial daylight autonomy (sDA), Annual Sunlight Exposure (ASE), and useful daylight illuminance (UDI) with variations of the Modified Solar Factor (MSF). Thermal and light performance assessments were made using energy modeling for two types of solar protection. The case study is a school classroom located in the city of Talca, in central Chile, considering window-to-wall ratios (WWR) of 40 %, 50 %, and 60 %. The premise’s thermal and light behavior were obtained with both types of solar protection. The sDA and UDI results allowed making an approximation of optimal solutions, however, the ASE values in all cases, were classified as unsuitable for classroom use. The analysis suggests that better limit values for both requirements are obtained by organizing the results by WWR instead of by the MSF that each solution identifies. This methodology compared solar protection options at an early design stage, reaching recommended light performance levels and energy savings of 70 % or more, for the two types of solar protection. It is concluded that to achieve minimum acceptable daylighting levels, in balance with cooling energy consumption, it is necessary to consider annual dynamic assessments with sDA and UDI as relevant indicators. 1. Introduction The construction sector is responsible for most primary energy consumption, globally reaching 40 % and one-third of greenhouse gas emissions (Vaisi et al., 2023). Educational buildings account for 17 % of the non-residential building stock (Droutsa et al., 2018) and are relevant for energy savings on being large surface area buildings, holding a large number of users, and, in some cases, due to intensive use of air conditioning equipment. In the last 20 years, energy consumption in the tertiary sector has increased energy consumption by 40 % for member countries of the Organization for Economic Cooperation and Development (OECD) and by 100 % in non-OECD countries. The energy efficiency of this sector is determined by indicators that have been classified according to the end use of the energy consumed, grouped into 5 main categories: heating, cooling, water heating, lighting, and other equipment (Energy Agency, n.d.). From this, Principal and Complementary Indicators have been defined, with the main energy indicator being Annual CO 2 Emissions (kg/m 2 ) or Annual Primary Energy (kW/m 2 ) and complementary, the breakdown of CO 2 emissions and primary energy consumption, and the energy demanded by the building for each of its main services. The complementary indicators explain the behavior of a building, focusing on the possible improvements (Direcci´ on General de Arquitectura et al., 1998a; Gasparella et al., 2011; Government of Spain. et al., 2009; MINVU, 2017). Depending on the use of the building and the climate, this energy demand may be higher for heating or cooling. When efforts are focused on reducing the demand for thermal energy, other secondary energy demands or associated performances are usually unknown or disregarded. To establish a variable’s behavior, energy simulation processes are required to evaluate the impact, in early design stages, that certain decisions have on end behavior and, therefore, building performance. For educational * Corresponding author. E-mail address: [email protected] (A. P´ erez-Fargallo). Contents lists available at ScienceDirect Energy Reports journal homepage: www.elsevier.com/locate/egyr https://doi.org/10.1016/j.egyr.2024.07.017 Received 5 February 2024; Received in revised form 1 July 2024; Accepted 12 July 2024 Energy Reports 12 (2024) 1157–1177 1158 buildings, lighting performance is important for activities that take place in the classrooms, in addition to providing psychological and physiological benefits such as improving emotions, motivation, the immune system, and the regulation of the circadian cycle, avoiding environments with artificial lighting (Bellia et al., 2015; Ma and Yang, 2023). However, this is a performance that counters the thermal, by generating opposing behaviors, where restricting the size of openings to control the entry of solar radiation, leads to a reduction of daylight contributions, and with this, the performance of this requirement. On the other hand, providing large amounts of daylight can lead to energy savings of up to 50 % (Lo Verso et al., 2021). Since light and thermal behavior are associated with the openings the building’s envelope has, it should be considered that both the optical properties of the glazed surfaces and opaque element components of the opening, as well as the geometry of the opening, its orientation, the WWR ratio, and the characteristics of the solar control or protection Nomenclature and abbreviations Con Thermal conductivity ASE Annual Sunlight Exposure (%) DB Design Builder Software - Energy simulation and daylight analysis. DGP Daylight Glare Probability DF Daylight factor emis Emissivity IRT Infrared transmission factor kg/m 2 Kilograms per square meter MINVU Ministry of Housing and Urban Planning MSF Modified solar factor NCh Chilean Standard N North NE Northeast NW Northwest sDA Spatial daylight autonomy (%) ST Solar Transmission Factor SR Solar Reflectance Factor U Thermal transmittance U value (W/m 2 (k) UDI Useful daylight illuminance VT Visible Transmission Factor VR Visible reflectance factor W West WWR Window-to-wall ratio (%) Fig. 1. Analysis process and tools. C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1159 elements influence its performance. (Fela et al., 2019; Zomorodian and Tahsildoost, 2017). Although this performance can be solved with artificial lighting, as the regulatory regulations indicate, visual comfort in this type of premises prioritizes the contribution of daylighting. Certification systems such as LEED (Leadership in Energy &Environmental Design) and others guide and give additional scores for projects that consider daylighting assessments using specific parameters (De Luca et al., 2019; Fig. 2. Outline of the base model made with SketchUp and window-to-wall ratios (WWR) for the analyzed percentages and overhang lengths used. Table 1 The shading factor for façade obstacles (corbel or overhang), MSF and its components, and for the setback. Source: Preparation by the Authors based on the Spanish Technical Building Code (General Direction of Architecture, 1998). Orientation. 0.2<L/H≤0.5 0.5<L/H≤1 1<L/H≤2 L/H>2 N 0<D/H≤0.2 0.82 0.5 0.28 0.16 0.2<D/H≤0.5 0.87 0.64 0.39 0.22 D/H>0.5 0.93 0.82 0.6 0.39 NE/NW 0<D/H≤0.2 0.9 0.71 0.43 0.16 0.2<D/H≤0.5 0.94 0.82 0.6 0.27 D/H>0.5 0.98 0.93 0.84 0.65 E/W 0<D/H≤0.2 0.92 0.77 0.55 0.22 0.2<D/H≤0.5 0.96 0.86 0.7 0.43 D/H>0.5 0.99 0.96 0.89 0.75 Orientation. 0.05<D/W≤0.1 0.1<D/W≤0.2 0.2<D/W≤0.5 D/W>0.5 N 0.05<D/H≤0.1 0.82 0.74 0.62 0.39 0.1<D/H≤0.2 0.76 0.67 0.56 0.35 0.2<D/H≤0.5 0.56 0.51 0.39 0.27 D/H>0.5 0.35 0.32 0.27 0.17 NE/NW 0.05<D/H≤0.1 0.86 0.81 0.72 0.51 0.1<D/H≤0.2 0.79 0.74 0.66 0.47 0.2<D/H≤0.5 0.59 0.56 0.47 0.36 D/H>0.5 0.38 0.36 0.32 0.23 E/W 0.05<D/H≤0.1 0.91 0.87 0.81 0.65 0.1<D/H≤0.2 0.86 0.82 0.76 0.61 0.2<D/H≤0.5 0.71 0.68 0.61 0.51 D/H>0.5 0.53 0.51 0.48 0.39 C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1160 Piraei et al., 2022). These parameters have been evaluated using static methods, based on given geometric configurations and optical properties of the opening elements such as the Modified Solar Factor (MSF), and dynamic methods that combine information from climate files with energy simulation programs, obtaining annual behavior analysis, using the solar seasonal variation and the availability of daylight (Zhu et al., 2020). Among the most widely used annual dynamic assessment methods are the Daylight Factor (DF); Spatial Daylight Autonomy (sDA) (Arango-Díaz et al., 2022); Annual Sunlight Exposure (ASE) (Abdollahzadeh et al., 2020; AL-Dossary and Kim, 2020; Ekici et al., 2019, 2021; M. ElBatran and Ismaeel, 2021; Ma and Yang, 2023; Mangkuto, Fela, et al., 2019; Marzouk et al., 2022; Mostafavi et al., 2022); Daylight Glare Probability (DGP) (replacing ASE) (Mangkuto, Dewi, et al., 2019; Uribe et al., 2019); and Useful Daylight Illuminance (UDI) (Besbas et al., 2022; Montaser Koohsari and Heidari, 2022). To maintain suitable daylighting levels, these analyses establish minimum, acceptable, and/or suggested ranges and, in some cases, lower and upper limits for their evaluations. These acceptance parameters for the different indicators do not establish a direct relationship with other performance levels such as thermal behavior. However, it can be deduced from the results whether one configuration or another can affect heating or cooling (Koˇ sir et al., 2018). Conversely, thermal evaluations of solar control strategies can suggest their light performance, which implies a synergistic formulation between both requirements so that combined compliance is easier for designers (Sepúlveda et al., 2020). Hence, it is necessary to find suitable design variables that can balance daylighting and energy performance, achieving potential energy savings by decreasing lighting and thermal energy requirements. On the other hand, uncontrolled daylighting can cause a psychological, physiological, and biological imbalance in people (Gaviria et al., 2020); as well as overheating and glare, taking into account that the design variables with the biggest impact are the WWR, solar control devices, and windows (AL-Dossary and Kim, 2020; Salom´ on and Ambroggio, 2022). The simultaneous evaluation of these and other parameters has made it possible to more effectively calibrate the contribution of both requirements, being able, through energy performance simulation processes, to establish the risks or impacts of implementing one decision or another at an early design stage and thereby determine those parameters that can provide more reliable results (Agarwal et al. n.d.). When a multi-objective evaluation is used, it is possible to address the design response in a balanced way by adjusting the result to the optimized condition and in turn achieving a more sustainable building where glare and excessive heat gains are avoided while, at the same time, minimizing energy demand, and maximizing daylight availability, and with it, visual comfort (Besbas et al., 2022; Uribe et al., 2019; Wen et al., 2023). (Wei et al., 2020) evaluate 14 Green Building certification schemes and establish 4 main components to evaluate the indoor environment quality, where thermal and lighting contribute 49 % and air quality and acoustic aspects the remaining 51 %. With this, the search to achieve the goal established by a primary indicator will be possible, as long as other performance levels, not necessarily energy-based and that ultimately establish an adequate indoor environment quality, are met. The aim of this research is to define a methodology that allows looking closer at solar protection solutions that consider optimal daylighting performance through annual dynamic indicators, in the early design stage, while maintaining adequate energy-saving levels. For this purpose, the cooling energy consumption and the dynamic daylight lighting indicators sDA, ASE, and UDI have been considered the primary indicators using the variation of the MSF and the WWR, to come closer to a result that balances both performance levels. 2. Methodology The research methodology was organized into 6 stages (Fig. 1). Stage 1 comprised determining the MSF by numerical calculation, using different dimensions for the depth of the overhangs and their equivalent in glazing for setback conditions, considering the established orientations and three window-to-wall ratio (WWR) levels: 40 %, 50 %, and 60 %. Stage 2 consisted of generating energy simulation and lighting performance models with Design Builder and the equivalent configuration of these solar protection variables. Stage 3 considered the results collection and simulation process associated with both solar protection strategies (overhang and setback) with the proposed WWR variations for cooling energy consumption (kW/hour per year; kW/hour m 2 per year and percentage savings (%) compared to the highest value of the series) and annual lighting performance (sDA, ASE, and UDI). Stage 4 included Table 2 Boundary conditions of simulations: loads, setpoints, and values. Loads Units Value Schedule Natural ventilation Ac/h 5 Monday to Friday 8 am to 6 pm Infiltration Ac/h 0.7 Annual General lighting-Normalized power density W/m 2 -100 lux 2.5 Monday to Friday 8 am to 6 pm Occupation density people/m 2 0.5 Monday to Friday 8 am to 6 pm Cooling ◦C 24 Monday to Friday 8 am to 6 pm Heating ◦C 21 Monday to Friday 8 am to 6 pm Lighting control Lux 400 work plane height 0.8 m Monday to Friday 8 am to 6 pm Table 3 Glazing used and its optical and thermal characteristics. Glazing ID Thickness mm ST SR1 SR2 VT VR1 VR2 IRT emiss1 emiss2 Con W/mK 0.82* Generic 6.000 0.820 0.074 0.074 0.900 0.081 0.081 0 0.840 0.840 1.000 0.90 14708 5.800 0.902 0.080 0.080 0.911 0.083 0.083 0 0.840 0.840 1.000 0.85 21013 6.000 0.849 0.075 0.075 0.900 0.081 0.081 0 0.840 0.840 1.000 0.72 3293 5.613 0.725 0.092 0.092 0.866 0.091 0.092 0 0.840 0.840 1.000 0.70 21547 6.360 0.703 0.074 0.072 0.840 0.082 0.081 0 0.840 0.840 0.818 0.51 5067 5.660 0.512 0.055 0.055 0.629 0.062 0.062 0 0.840 0.840 1.000 0.41 9834 5.918 0.408 0.050 0.050 0.438 0.052 0.052 0 0.840 0.840 1.000 0.27 17000 5.740 0.267 0.180 0.094 0.418 0.194 0.172 0 0.839 0.840 1.000 0.19 16122 6.107 0.189 0.254 0.256 0.240 0.279 0.266 0 0.840 0.840 0.812 0.16 17019 5.880 0.160 0.303 0.080 0.193 0.329 0.116 0 0.842 0.840 1.000 0.15 9611 5.691 0.147 0.254 0.299 0.172 0.231 0.226 0 0.840 0.773 0.985 ID: From the LBNL Window database, except for 0.82; ST: Solar Transmission Factor; SR: Solar Reflectance Factor (SR1: front; SR2: back); VT: Visible transmission factor; VR: Visible reflectance factor (VR1: front; VR2: back); IRT: infrared transmission factor; emis: emissivity (emiss1: front; emiss2: back); Con: thermal conductivity. Source: Preparation by the authors based on DesignBuilder (*) and LBNL Window data. C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1161 the analysis and comparison of the results of the 4 indicators. Stage 5 consisted of the sensitivity analysis of the variables using the R Studio software, to determine the impact of the parameters used, and establish reference values for the evaluated dimensions. Stage 6 consisted of determining optimal MSF and WWR values for each orientation. The threshold values for sDA and UDI consider 50 % as the lower limit; values equal to or greater than 50 % are considered acceptable and for ASE sets a maximum value of 10 %; acceptable, 7 %; and recommended, 3 % or less. For cooling energy consumption, the 40th percentile value has been used as a reference. This value is defined in the document "Energy Efficiency Rating of Buildings 7, Energy Rating Scale, New Buildings, Point 3.2 Obtaining the Scale Limits," indicating that the boundary between classes C and D corresponds to the 40th percentile for buildings that meet the requirements of the CTE. Although it does not align with the proposal by the CEN (European Committee for Standardization) to place it between classes B and C, this reference encourages the improvement of the thermal quality of buildings (Gobierno de Espa˜ na. et al., 2009). The experiment was made in the city of Talca, Chile, which has a marked seasonal variability. The cooling energy consumption and daylighting performance have been analyzed in a standard school classroom with WWR variations, considering solar protections with overhangs and optimized glazing for one year. 2.1. Base case A 9.00 ×6.00 ×3.00 m school classroom was used, with 20 cm thick reinforced concrete walls and a glazed surface on one of its facades with a WWR of 40 %, 50 %, and 60 % equivalent to a continuous 9 m long and 1.2 m, 1.5 m, and 1.8 m high pane, maintaining a lintel height at 2.40 m from the indoor floor level. The glazed facade is in contact with the outside and determines the model’s orientation. The other facades of the model have been considered adiabatic. The characteristics and dimensions of the model are shown in Fig. 2. The formula established in the Basic HE Document of the Spanish Technical Building Code was used to determine the MSF and the tables for the sizes of the openings and solar protection elements. The table established for the overhang and setback elements has also been used (Table 1)(Direcci´ on General de Arquitectura et al., 1998b). The MSF can be described as follows: MSF =SF⋅[(1−Fv)⋅g⊥ + FF⋅0,04⋅UF⋅∝](1) Where MSF is the Modified Solar Factor, SF is the shading factor, F_v is the fraction of the gap occupied by the glass, g⊥is the solar factor of the glass (UNE EN 410:1988), FF is the fraction of the gap occupied by the frame, UF is the transmittance of the frame (W/m 2 (K) and ∝is the absorbance, considering the color of the window frame. To obtain the value for the geometry of the case study’s glazed surface, solar protection variants were used with overhangs from 10 to 400 cm deep, single glazing, and half-white aluminum frames with a thermal bridge break for the North, Northeast, Northwest, East, and West orientations. To determine the characteristics of the case study’s envelope, those defined in the Energy Efficiency Guide for Educational Establishments of the Chilean Energy Efficiency Agency (2012) were used. The dynamic light performance indicators used are Special Daylight Autonomy (sDA), Annual Sunlight Exposure (ASE), and Useful Daylight Illuminance (UDI). sDA and ASE are part of the Lighting Measurement 83 (LM-83) guide published in 2013 by the Illuminating Engineering Society (IES). sDA is derived from the Daylight Autonomy (DA) indicator. It is defined as the percentage of the space’s area that has an illuminance of at least 300 lux for at least 50 % of the occupied hours during a year, considering a daily occupancy from 8:00 am to 6:00 pm. It sets a minimum value of 50 %, with suggested ranges where 55 % is Acceptable and 75 % is Preferred. ASE was conceived to reduce the risks of glare and overheating associated with the intensive use of daylighting in buildings. It is considered a complement to sDA. ASE looks to prevent spaces from receiving an excessive amount of solar radiation. It is defined as the percentage of the space that has an illuminance of 1000 lux or higher for 250 hours a year or more, considering a daily occupancy from 8:00 am to 6:00 pm. An ASE value of 25 % indicates that a quarter of the space exceeds the established conditions. Values above 10 % can lead to occupant visual discomfort, with 10 % being understood as Sufficient, 7 % as Acceptable, and 3 % as Preferred. UDI establishes the illuminance on the work plane within ranges considered suitable for performing normal activities without generating visual discomfort. It considers a minimum annual value of 50 % of the time within the illuminance ranges of 300–3000 lux. The results obtained by simulation indicate the area of an enclosure that is kept within those ranges (Ayoub, 2019). Table 4 MSF values by orientation for overhangs between 0.1 and 4.0 m and glazed surfaces considering the WWRof 40 %, 50 %, and 60 %. PROT N/E/W/ NE/NW HW ¼120 WWR 40 % HW ¼150 WWR 50 % HW ¼180 WWR 60 % OV 10 N 0.54 0.54 0.54 OV 45 N 0.54 0.54 0.54 OV 80 N 0.33 0.33 0.54 OV 90 N 0.33 0.33 0.54 OV 125 N 0.18 0.33 0.33 OV 160 N 0.18 0.18 0.33 OV 170 N 0.18 0.18 0.33 OV 245 N 0.11 0.18 0.18 OV 320 N 0.11 0.11 0.18 OV 330 N 0.11 0.11 0.18 OV 365 N 0.11 0.11 0.11 OV 400 N 0.11 0.11 0.11 SF 0.85 N 0.54 0.54 0.54 SF 0.51 N 0.33 0.33 0.33 SF 0.27 N 0.18 0.18 0.18 SF 0.16 N 0.11 0.11 0.11 OV 10 E/W 0.61 0.61 0.61 OV 45 E/W 0.61 0.61 0.61 OV 80 E/W 0.51 0.51 0.61 OV 90 E/W 0.51 0.51 0.61 OV 125 E/W 0.36 0.51 0.51 OV 160 E/W 0.36 0.36 0.51 OV 170 E/W 0.36 0.36 0.51 OV 245 E/W 0.14 0.36 0.36 OV 320 E/W 0.14 0.14 0.36 OV 330 E/W 0.14 0.14 0.36 OV 365 E/W 0.14 0.14 0.14 OV 400 E/W 0.14 0.14 0.14 SF 0.85 E/W 0.60 0.60 0.60 SF 0.51 E/W 0.51 0.51 0.51 SF 0.27 E/W 0.36 0.36 0.36 SF 0.16 E/W 0.14 0.14 0.14 OV 10 NE/NW 0.59 0.59 0.59 OV 45 NE/NW 0.59 0.59 0.59 OV 80 NE/NW 0.47 0.47 0.59 OV 90 NE/NW 0.47 0.47 0.59 OV 125 NE/NW 0.28 0.47 0.47 OV 160 NE/NW 0.28 0.28 0.47 OV 170 NE/NW 0.28 0.28 0.47 OV 245 NE/NW 0.11 0.28 0.28 OV 320 NE/NW 0.11 0.11 0.28 OV 330 NE/NW 0.11 0.11 0.28 OV 365 NE/NW 0.11 0.11 0.11 OV 400 NE/NW 0.11 0.11 0.11 SF 0.85 NE/NW 0.60 0.60 0.60 SF 0.51 NE/NW 0.47 0.47 0.47 SF 0.27 NE/NW 0.28 0.28 0.28 SF 0.16 NE/NW 0.11 0.11 0.11 Note: PROT: Type of solar protection; HW: Height of window opening; WWR: window-wall percentage; OV: Overhangs; SF: Glazing shading factor. C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1162 2.2. Boundary conditions and simulation The climate file of the location under evaluation was obtained using the Meteonorm v.8.0 software, considering the official station this suggested. To determine the daily solar radiation (Wh/m 2 x day) at the site, data was taken from the Chilean Standard 1079 Of 2019 and the Climate Consultant software v 6.0, as well as the EPW file obtained, for those values that were not found in the Standard’s table. For the city of Talca, the qualitative and appreciation values, according to NCh 1079, are an average temperature of 20.3◦C (very high - very hot) in January, and 7.6◦C (low - cold) in July. The average monthly oscillation in January is 18.9◦C (average), and in July, 11.4◦C (average). The sunshine in January is 6445 Wh m 2 day (Strong), and in July, 926 Wh m 2 x day (Very low). Classroom occupancy periods are set as Monday to Friday, from 8 a.m. to 6 p.m., with an occupancy of 27 people, and an infiltration of 0.7 air renewals per hour (ac/h), for one year, without discounting winter and summer holidays (Table 2). Lighting systems are considered as being on when 400 lux is required at 0.8 m above the work plane. For the adiabatic envelope of the classroom’s walls, reinforced concrete floors and roofs are used without coating, except for the wall containing the glazed surface which has a 60 mm thick expanded polystyrene thermal insulation with the U value of 0.59 W/m 2 K established by local regulations. The Design-Builder (DB) energy simulation program uses the EnergyPlus calculation engine, which makes it possible to model geometries of complex openings and efficiently incorporate boundary conditions and the modification of the overhang and glazing parameters, covering, in these dimensions, the entire range of values that can be obtained by applying the numerical relationships indicated in Table 1. The MSF values for glazed surfaces are defined using calculations for the overhang and its equivalent, a 0.1 m deep setback. The values for the overhangs are separated into 4 ranges: 0.10–0.45–0.80 m; 0.90–1.25–1.60 m; 1.70–2.45–3.20 m; and, 3.30–3.65–4.00 m. These ranges establish varying values for the N, E-W, and NE-NW orientations. In turn, each of these conditions is evaluated with the WWR values of 40–50 and 60 %. For the simulations with overhang, single glazing is established, with an ST of 0.82. The optical glazing characteristics are presented in Table 3. With the MSF values of the combinations determined through the calculation process, these are input in the DB model to obtain the energy consumption values for cooling and annual lighting performance. This is repeated for each MSF and ST combination defined. The overhang condition is evaluated with an ST of 0.82 and the overhang sizes established for the orientations (N, NE, NW, E, and W). This allows making a comparison of the values obtained and, subsequently, determining the impact that the use of one strategy or another has on the energy consumption for cooling and the lighting performance using the sDA, ASE, and UDI indicators. The R Studio software was used to determine the statistical values associated with the results obtained considering the total cooling energy data and their dependence on the sDA, ASE, and UDI values. Once the energy simulation results were obtained, the data were entered into RStudio to run a sensitivity analysis, considering daylighting and cooling energy consumption indicators. A p-value of 0.05 was considered as Fig. 3. Cooling energy consumption values by WWR and type of solar protection for the North orientation. C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1163 an indicator of significance for the variables. Stage 6 considers the results analysis process using the data obtained from the energy simulations. Performance was assessed through simulations considering different types of solar protection (overhang and glazing), Window-to-Wall Ratios (WWR) of 40 %, 50 %, and 60 %, and the Solar Factor (SF) of the glazing. The analysis included tabulating and organizing data to identify configurations that provide optimal thermal conditions. The optimal condition in this study includes energy reduction and visual comfort. Here are the criteria used to define these optimal conditions: •Energy Reduction: cooling energy consumption (kWh/m 2 per year) and percentage energy savings. The energy-saving variable was ordered from highest to lowest, and a restriction line was set at the value of the 40th percentile or the closest one to that. This approach helps in identifying configurations that offer significant energy savings. •Visual Comfort: Visual comfort was measured using annual dynamic daylighting indicators, namely Spatial Daylight Autonomy (sDA) and Useful Daylight Illuminance (UDI), with minimum values of 50 % for both sDA and UDI. These indicators ensure that the design provides sufficient natural light without causing glare or overheating. For each orientation and WWR, the following combinations of optimal values were identified: •Lowest cooling energy consumption (green line to indicate the optimal energy solution). •Best performance in Spatial Daylight Autonomy (sDA) and Useful Daylight Illuminance (UDI) (blue line to denote the optimal lighting solution). •Reference based on the 40th percentile of the energy consumption (red line). This comprehensive analysis allows us to balance both energy efficiency and occupant comfort effectively. We believe that these criteria provide a robust framework for defining and achieving the ’optimal’ condition in building design. Fig. 4. Cooling energy consumption values by WWR cooling and type of solar protection for East, West, Northeast, and Northwest orientations. C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1164 3. Results and discussion 3.1. Obtaining properties for solar protections Applying the MSF’s formula and the WWR, the correlation was established between the values of the type of solar protection and its equivalent for overhang lengths-WWR and glazing Shading Factor (SF)- WWR. This relationship allowed the grouping of each MSF category indicated in Table 4. For the North orientation, the equivalent MSF values between glazing and overhang allow grouping different options for the defined WWR percentages. However, for the E-W and NE-NW orientations, there is a difference between the highest values. Only those values that match have been considered for the following analyses. 3.2. Cooling results The cooling energy consumption results for the city of Talca show that the MSF values for the North orientation are always higher for those associated with the glazing for the most restrictive values, regardless of the WWR considered. However, the values associated with MSF 0.54 can end up being very similar when the WWR is higher. For MSF 0.11, the glazing with a WWR of 40 % (1257.71 kWh year) consumes almost twice as much as the overhang of the same WWR. For MSF 0.18, the glazing with a WWR of 60 % consumes more than three times as much as the overhang of the same WWR. For MSF 0.33, the glazing with a WWR of 60 % consumes almost three times as much as the overhang of the same WWR. For MSF 0.54, the glazing with a WWR of 40 % (3994.76 kWh year) consumes 1.6 times more than the overhang of the same WWR. Fig. 3 shows the cooling energy consumption values grouped by WWR for the North orientation, and Fig. 4, for the rest of the orientations. 3.3. Lighting results sDA, ASE, and UDI data are collected to evaluate the daylighting performance with the different MSF values and WWR. The data were obtained from simulations performed in Design Builder, obtaining tabulated values for the annual results. 3.3.1. sDA results The minimum value is set as 50 % of the hours of the year with at least 300 lux, with 55 % acceptable, and 75 % or more being recommended. Fig. 5 summarizes the values obtained for the different overhang sizes and WWR values, and Fig. 6 summarizes the values for the glazing. It is seen that most of the data obtained for overhangs reaches an acceptable (55 %) and recommended (75 %) value, with the overhangs of a greater depth and lower WWR proportions being outside the range. The highest values are concentrated in the proportions of WWR50 and WWR60. The compliance range is seen for all overhang and WWR proportion levels, but drops above 2.45 m, reaching neither the recommended (75 %) nor acceptable (55 %) limit for WWR40. It is seen that glazing with SF 0.16, 0.19, and 0.15 does not reach an acceptable level (55 %). 3.3.2. ASE results A maximum value of 10 %, acceptable of 7 %, and recommended of 3 % or less are set. Fig. 7 summarizes the values obtained for the different overhang sizes and WWR values, and Fig. 8 summarizes the values for the glazing. It is seen that all the data obtained for overhangs do not reach the Fig. 5. sDA values for overhang sizes and WWR used for N, E-W, and NE-NW orientations. C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1165 maximum (10 %), acceptable (7 %), and preferred (3 %) values, with the deeper overhangs in all the WWR having the highest values. The lowest values (best results) are for the WWR50 and WWR60 for the overhang lengths of 10 cm and 45 cm. It is seen that the glazing does not reach suitable values in any of the series, with the highest values being associated with SF 0.16, 0.19, and 0.15 under WWR60 and the lowest under SF 0.85, 0.90, 0.51, 0.72, and 0.70 with WWR50 and WWR60. 3.3.3. UDI results A minimum value of 50 % of the time within the illuminance range of 300–3000 lux is established. Fig. 9 summarizes the values obtained for the different overhang sizes and WWR values, and Fig. 10 summarizes the values for the glazing. It is seen that all the data obtained for overhangs exceed the recommended value. The values with WWR40 and lengths up to 330 cm reach the maximum, as does WWR50–60, with lengths up to 400 cm. It is seen that all the glazing reaches the recommended value, with SF 0.27, SF 0.51, and SF 0.41 having a better performance. 3.4. Definition of optimal value. Grouping of data by WWR The calculation of the MSF allows linking different types of solar protection and WWR under the same value. This implies that, at the same MSF value, there should be the same thermal performance, which has already been proven not to be the case, not only between different types of solar protection but also between variants of the same type, attaining significant differences in the results (Mu˜ noz-Viveros et al., 2022). With this background information, it is possible to assume that the lighting performance may have variations of the same type. The results obtained through the different simulations have been grouped by orientation and by WWR. The data are organized following the cooling energy savings considering the highest value of the analyzed data series. Since all the results obtained for the ASE variable remain outside the range, these have been discarded from the following analyses. The data have been grouped according to the WWR that characterizes them, independent of their MSF. This criterion allows making a similar approach to what the regulatory documents establish when identifying a design parameter as a starting point to define the type of solar protection. The significance of the parameters is evaluated for each series of values, using RStudio to establish whether all those included need to be considered. For WWR40–50 and 60 the value of R2, related to the effectiveness of the model, was over 87 %, which indicates that the variables as a whole explain the result of cooling Energy Consumption (Table 5). The p-value (statistical contrast) obtained was less than 0.05, which is why it is considered valid with a significance of the variables above 99.9 %, with the UDI value having the greatest significance for the daylighting indicators in the three cases. 3.5. Definition of the optimized cooling energy-light performance values Graphs were built, based on the results obtained, with the 0.1–0.9 percentiles for the variables for each WWR defined. 3.5.1. North orientation Table 6 indicates the 40th percentile values for the North orientation. A graph was made, with the data obtained and considering ordering the energy-saving variable from highest to lowest, establishing the restriction lines for the different variables. For sDA and UDI, a straight line Fig. 6. sDA values for the glazing and WWR used in the N, E-W, and NE-NW orientations. C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1172 4.5. Northeast orientation For WWR 40, the energy performance varies by +164.17 %; the sDA, 22.00 %, and the UDI, 0 % (both with 100 %), with a difference of 1573.91 kWh year or 28.10 kWh/m 2 year. For WWR 50, the energy performance varies by +110.64 %; the sDA, 29.33 %, and the UDI, 0 % (both with 100 %), with a difference of 946.66 kWh year or 16.90 kWh/ m 2 year, and for WWR 60, the energy performance varies by 59.54 %; the sDA, 10.00 %, and the UDI, 0 % (both with 100 %), with a difference of 538.63 kWh year or 9.62 kWh/m 2 year. For all the WWRs considered, the best energy performance values have a variation of up to 1.84 kWh/m 2 year, and, for the light performances, this variation reaches up to 19.45 kWh/m 2 year. The differences are greater between the optimal performance by WWR, exceeding in two of the three WWR levels, twice the lower value, associated with energy performance. For WWR40–50 and 60, the optimum thermal Fig. 16. Characteristic values of the West facing solutions with WWR40–50 and 60 and optimal solutions for the cooling energy consumption parameters in kWh/m 2 year; percentage energy savings, sDA, and UDI. Table 8 40th percentile values for the parameters of the different WWRS applied to the Northeast and Northwest orientations. NORTHEAST NORTHWEST WWR PERCENTIL REF kWh a˜ no REF_m 2 kWh/m 2 a˜ no REFporc_ah % sDA % UDI % 40 NE 40 1232,36 22,00 69,39 58,30 100,00 50 NE 40 1591,73 31,79 62,60 87,43 100,00 60 NE 40 1443,27 25,77 73,50 97,33 91,33 40 NO 40 1198,72 21,40 68,83 58,37 99,97 50 NO 40 1568,16 28,00 65,15 88,20 99,97 60 NO 40 1443,37 25,77 71,83 98,17 93,00 WWR: Window-to-wall ratio; REF: Energy consumption for cooling REF_m2: energy consumption for cooling per square meter; REFporc_ah: percentage of cooling energy savings based on the highest value in the series; sDA: spatial light autonomy; UDI: Useful daylight illuminance. C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1173 performance is associated with an MSF of 0.11 (Overhangs of 245 cm and 400 cm). Optimal lighting performance for WWR40 has an MSF of 0.47 (Overhang of 80 cm) and for WWR50–60 has an MSF of 0.28 (Overhangs of 170 cm and 245 cm). 4.6. Northwest orientation For WWR 40, the energy performance varies by +165.46 %; the sDA, 21.50 %, and the UDI, 0 % (both with 100 %), with a difference of 1502.71 kWh year or 26.82 kWh/m 2 year. For WWR 50, the energy performance varies by +116.77 %; the sDA, 26.50 %, and the UDI, 0 % (both with 100 %), with a difference of 898.50 kWh year or 16.04 kWh/ m 2 year, and for WWR 60, the energy performance varies by +67.00 %; the sDA, 8.83 %, and the UDI, 0 % (both with 100 %), with a difference of 579.08 kWh year or 10.34 kWh/m 2 year. For all the WWRs considered, the best energy performance values have a variation of up to 2.47 kWh/m 2 year, and, for the light performances, this variation reaches up to 17.26 kWh/m 2 year. The differences are greater between the optimal performance by WWR, exceeding in two of the three WWR levels, twice the lower value, associated with energy performance. For WWR40–50 and 60 the optimum thermal performance is associated with an MSF of 0.11 (Overhangs of 245 cm and 400 cm). Optimal lighting performance for WWR40 has an MSF of 0.47 (Overhang of 80 cm) and for WWR50–60 has an MSF of 0.28 (Overhangs of 170 cm and 245 cm). 4.7. General considerations The analysis is done under a "shoebox" type model, although with characteristics closer to a classroom typology, with WWR of 40 %, 50 %, and 60 %, taking into account that these preliminary models allow initial approaches, and do not always represent reality (Ayoub, 2019). It has been possible to show that sDA and UDI are suitable as indicators of daylight contributions (Koˇ sir et al., 2018; Sun et al., 2020; Turan et al., 2020; Zomorodian and Tahsildoost, 2019) although sDA may comply, ASE does not (Englezou and Michael, 2020). In turn, the ASE analyses performed showed values that were always far from the recommended values without managing to approach the acceptable reference. Therefore, it is suggested not to consider ASE for making early design decisions, as the other two parameters can provide greater clarity regarding daylight minimums or excesses. Although the results do not show great differences between the E-W and NE-NW pairs, it is considered appropriate to evaluate each case individually since certain combinations may present relevant end energy consumptions. Even though the 40th percentile is a recommended reference, it was found that certain data series do not provide enough options that reach an acceptable level in the variables and may not achieve an optimal Fig. 17. Characteristic values of the Northeast facing solutions with WWR40–50 and 60 and optimal solutions for the cooling energy consumption parameters in kWh/m 2 year; percentage energy savings, sDA, and UDI. C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1174 solution. However, considering the alternative lower limit defined by the best light performance when the sDA and UDI variables reach the maximum, may expand the battery of solutions. Despite this, it is seen that opting for the best lighting performance can increase energy consumption by twice or more compared to the thermal optimum of a series. The analysis of the results raises the discussion about the current values associated with MSF since it focuses to a greater extent on thermal performances, leaving aside the light performances relationship. The dynamic analysis allows confirming that solutions under the same MSF value do not necessarily deliver similar light performance values. Although some cases do not have significant differences in this aspect, they do in terms of energy consumption, which can be more than double in some cases. The following points are considered limitations of this study: 1. The evaluation has considered three WWR proportions (40–50 and 60 %) for school classroom use in a single location in Chile. 2. The analysis was established in an annual period, from January to December, without discounting the summer and winter vacation periods, which are critical periods for energy demand, both in heating/cooling and in daylighting, which would partially reflect the condition of a classroom. 3. Premises with a single exterior facade of reinforced concrete and exterior insulation were considered, without finishes on the interior walls. 4. The weather file was obtained from the Meteonorm v 7.0 software, being able, in some cases, to extrapolate values from places that do not have the same local climatic variations. 5. Glazed surfaces have been defined as single glazing from the Design Builder database, so it does not represent real glazing in all its dimensions. The rest of the glazing comes from the LBNL Window software database and is associated with a real product. 6. The results for ASE showed values above the recommended value for all cases, so it was suggested not to consider this parameter for early design evaluations. However, it is possible that with other strategies or combinations of elements, it is possible to achieve recommended values. 7. Protection with overhangs and optimized windows have been considered, however, it is felt appropriate to evaluate other recommended solar protection options for E-W orientations either through horizontal or vertical louvers. While the article may have the potential to promote broader applications and universal adoption, the aim of this manuscript is to define a methodology that delves into solar protection solutions by considering Fig. 18. Characteristic values of the Northwest facing solutions with WWR40–50 and 60 and optimal solutions for the cooling energy consumption parameters in kWh/m 2 year; percentage energy savings, sDA, and UDI. C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1175 the optimal performance of natural lighting through annual dynamic indicators at the early design stage, while maintaining adequate levels of energy savings. For this purpose, only a local climate has been employed, which may pose a limitation. Nevertheless, it is expected that other researchers will use the described methodology and extend the results to other parts of the world 5. Conclusions The experiment required 36 thermal energy simulations and 36 light performance simulations for the overhang lengths and lighting with the overhang lengths in the five defined orientations (N, NE, NW, E, and W), plus 20 simulations for the values corresponding to the setback in both performances, using the same orientations. The results generated data and possible graphs for comparison, establishing a methodology that allows looking closer at, during an early design stage, solar protection solutions considering the optimal daylighting performance through annual dynamic indicators while maintaining adequate energy-saving levels. To be able to link the different solutions, it was necessary to initially group those that could be similar regardless of the WWR they had. This generated two grouping options: by MSF and by WWR. Grouping by MSF allows determining which combination and WWR can achieve a balance between both requirements. However, grouping by WWR allows a closer approximation to the early design decision process because it delivers MSF options based on a possible facade appearance and not the other way around. The findings showed that the same MSF value generates a series of possible solutions to evaluate both its thermal and light performance by grouping by different WWR. From these results it is possible to observe that the dispersion of the values can generate very efficient solutions from the energy point of view, but not so from the light point of view. The optimal lighting system is presented in most cases as a solution that consumes more cooling energy. It also establishes intermediate options that do not have much difference compared to the optimal lighting case, but do for energy consumption. This procedure allowed grouping these optimal solutions by WWR, with this form being more appropriate for the early design process by first establishing the restriction of the facade configuration over the restriction of solar input. At the same time, the results suggest that it is appropriate to consider sDA and UDI as annual dynamic lighting indicators and discard the use of ASE when a tendency to maintain values outside the acceptable range is repeated in the analyses, thereby managing to establish solutions that deliver a better lighting performance and evaluate these together with the energy performances. The simultaneous evaluation of both thermal and light parameters has allowed raising a discussion on the MSF values associated with regulatory documents in Chile and the impact that certain strategies have on light performance. As noted above, this can have a relevant impact on both the circadian cycle and the users’emotional and psychological aspects. Despite this, the experiment is limited when considering a specific case and climate. Other analyses can be made in the future in different localities, considering alternative uses to the one used, to check the regulatory values that currently apply in other climates and latitudes, to evaluate other solar protection options and Table 9 Results for the best performances by WWR and by orientation. WWR NORTH ORIENTATION WWR Category PROT MSF REF kWh year REF_m 2 kWh/m 2 year REFporc_ah % sDA % UDI % 40 Best energy performance Overhang L245 0.11 680.43 12.13 82.97 77.00 100.00 40 Best lighting performance Overhang L080 0.33 1543.58 27.51 61.36 98.50 100.00 50 Best energy performance Overhang L400 0.11 646.48 11.52 86.49 70.67 100.00 50 Best lighting performance Overhang L170 0.18 1135.44 20.24 76.27 100.00 100.00 60 Best energy performance Overhang L400 0.11 693.95 12.37 87.41 93.17 100.00 60 Best lighting performance Overhang L245 0.18 1089.09 19.41 80.24 100.00 100.00 WWR EAST ORIENTATION WWR Category PROT MSF REF kWh year REF_m 2 kWh/m 2 year REFporc_ah % sDA % UDI % 40 Best energy performance Overhang L170 0.36 1724.54 30.79 47.11 78.17 100.00 40 Best lighting performance Overhang L080 0.51 2489.67 44.45 23.64 93.67 100.00 50 Best energy performance Overhang L400 0.14 1056.03 18.85 72.21 52.67 100.00 50 Best lighting performance Overhang L160 0.36 2191.99 39.14 42.32 100.00 100.00 60 Best energy performance Overhang L400 0.14 1207.03 21.55 72.11 70.00 100.00 60 Best lighting performance Overhang L170 0.51 2445.90 43.67 43.48 100.00 100.00 WWR WEST ORIENTATION WWR Category PROT MSF REF kWh year REF_m 2 kWh/m 2 year REFporc_ah % sDA % UDI % 40 Best energy performance Overhang L245 0.14 1169.51 20.88 65.45 50.33 100.00 40 Best lighting performance Overhang L090 0.51 2447.78 43.70 27.68 94.17 100.00 50 Best energy performance Overhang L400 0.14 944.65 16.87 75.85 56.33 100.00 50 Best lighting performance Overhang L160 0.36 2206.06 39.39 43.60 100.00 100.00 60 Best energy performance Overhang L400 0.14 1109.68 19.81 74.91 80.33 100.00 60 Best lighting performance Overhang L245 0.36 1899.30 33.91 57.05 99.33 100.00 WWR NORTHEAST ORIENTATION WWR Category PROT FSM REF kWh a˜ no REF_m 2 kWh/m 2 a˜ no REFporc_ah % sDA % UDI % 40 Better energy performance Overhang L245 0,11 958,69 17,12 76,19 73,67 100,00 40 Better lighting performance Overhang L080 0,47 2532,60 45,22 37,10 95,67 100,00 50 Better energy performance Overhang L400 0,11 813,48 14,52 82,91 70,67 100,00 50 Better lighting performance Overhang L170 0,28 1693,58 30,24 64,43 100,00 100,00 60 Better energy performance Overhang L400 0,11 904,64 16,15 83,39 90,00 100,00 60 Better lighting performance Overhang L245 0,28 1443,27 25,77 73,50 100,00 100,00 WWR NORTHWEST ORIENTATION WWR Category PROT MSF REF kWh year REF_m 2 kWh/m 2 year REFporc_ah % sDA % UDI % 40 Best energy performance Overhang L245 0.11 907.94 16.21 76.39 73.83 100.00 40 Best lighting performance Overhang L080 0.47 2410.25 43.03 37.33 95.33 100.00 50 Best energy performance Overhang L400 0.11 769.47 13.74 82.90 73.50 100.00 50 Best lighting performance Overhang L170 0.28 1667.97 29.78 62.93 100.00 100.00 60 Best energy performance Overhang L400 0.11 864.29 15.43 83.13 91.17 100.00 60 Best lighting performance Overhang L245 0.28 1443.37 25.77 71.83 100.00 100.00 C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1176 establish the impact they have on lighting performance. Author Statement The authors declare that the manuscript has not been produced using AI. CRediT authorship contribution statement Alexis P´ erez-Fargallo: Writing –review &editing, Supervision, Project administration, Methodology. Cristian Mu˜ noz-Viveros: Writing –review &editing, Writing –original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Carlos Rubio-Bellido: Writing –review &editing, Supervision, Project administration, Methodology. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Acknowledgments The authors are grateful for the support provided by the Thematic Network 722RT0135 “Ibero-American Network of Energy Poverty and Environmental Welfare”(RIPEBA) funded by the call for Thematic Networks of the CYTED Program for 2021. This work was supported by Vicerrectoría de Investigaci´ on y Doctorados de la Universidad San Sebasti´ an –Fondo USS-FIN-24-APCS-17. References Abdollahzadeh, N., Tahsildoost, M., Zomorodian, Z.S., 2020. A method of partition design for open-plan offices based on daylight performance evaluation. J. Build. Eng. 29 https://doi.org/10.1016/j.jobe.2020.101171. Agarwal, M., Danseux, G., Pastore, L., &Andersen, M. (n.d.). Reliability Of Daylight And Energy Demand Evaluations For Decision Making At The Conceptual Design Stage. 121–128. 〈https://doi.org/10.26868/25222708.2019.210414〉. AL-Dossary, A.M., Kim, D.D., 2020. A study of design variables in daylight and energy performance in residential buildings under hot climates. Energies 13 (21). https:// doi.org/10.3390/en13215836. Arango-Díaz, L., Garavito-Posada, M.A., Calle-Medina, J.S., Murcia-Cardona, A.M., Montoya-Fl´ orez, O.L., Pinto-Quintero, S., 2022. Daylight sufficiency of indoor environments under climate change scenarios. Habitat Sustentable 12 (2), 40–51. https://doi.org/10.22320/07190700.2022.12.02.03. Ayoub, M., 2019. 100 Years of daylighting: A chronological review of daylight prediction and calculation methods. In: In Solar Energy, Vol. 194. Elsevier Ltd, pp. 360–390. https://doi.org/10.1016/j.solener.2019.10.072. Bellia, L., Spada, G., Pedace, A., Fragliasso, F., 2015. Methods to evaluate lighting quality in educational environments. Energy Procedia 78, 3138–3143. https://doi.org/ 10.1016/j.egypro.2015.11.770. Besbas, S., Nocera, F., Zemmouri, N., Khadraoui, M.A., Besbas, A., 2022. Parametricbased multi-objective optimization workflow: daylight and energy performance study of hospital building in Algeria. Sustainability 14 (19). https://doi.org/ 10.3390/su141912652. De Luca, F., Kiil, M., Simson, R., Kurnitski, J., Murula, R., 2019. Evaluating daylight factor standard through climate based daylight simulations and overheating regulations in Estonia. Build. Simul. Conf. Proc. 6, 3968–3975. https://doi.org/ 10.26868/25222708.2019.210915. Direcci´ on General de Arquitectura, V. y Suelo M. de Fomento (1998a). DB_HE_abril_ 2009_50_53. In C´ odigo T´ ecnico de la Edificaci´ on-Espa˜ na (pp. 43–46). Direcci´ on General de Arquitectura, V. y Suelo M. de Fomento (1998b). DB_HE_abril_ 2009_50_53. In C´ odigo T´ ecnico de la Edificaci´ on-Espa˜ na (pp. 43–46). Droutsa, K.G., Balaras, C.A., Dascalaki, E.G., Kontoyiannidis, S., Argiriou, A.A., 2018. Energy Use Intensities for Asset Rating of Hellenic Non-Residential Buildings. Glob. J. Energy Technol. Res. Updates Vol. 5. 〈http://cms.ashrae.biz/EUI〉. Ekici, B., Kazanasmaz, T., Turrin, M., Tasgetiren, M.F., Sariyildiz, I.S., 2019. A Methodology for daylight optimisation of high-rise buildings in the dense urban district using overhang length and glazing type variables with surrogate modelling. J. Phys.: Conf. Ser. 1343 (1) https://doi.org/10.1088/1742-6596/1343/1/012133. Ekici, B., Kazanasmaz, Z.T., Turrin, M., Tas ¸getiren, M.F., Sariyildiz, I.S., 2021. Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 1: background, methodology, setup, and machine learning results. Sol. Energy 224, 373–389. https://doi.org/10.1016/j.solener.2021.05.083. Energy Agency, I. (n.d.). Indicadores de Eficiencia Energ´ etica: Fundamentos Estadísticos. 〈www.iea.org/t&c/〉. Englezou, M., Michael, A., 2020. Assessment of daylight performance and the impact of shading devices for typical in-patient rooms in healthcare facilities in Cyprus. Procedia Manuf. 44, 277–285. https://doi.org/10.1016/j.promfg.2020.02.232. Fela, R.F., Utami, S.S., Mangkuto, R.A., Suroso, D.J., 2019. The effects of orientation, window size, and lighting control to climate-based daylight performance and lighting energy demand on buildings in tropical area. Build. Simul. Conf. Proc. 2, 1075–1082. https://doi.org/10.26868/25222708.2019.210677. Gasparella, A., Pernigotto, G., Cappelletti, F., Romagnoni, P., Baggio, P., 2011. Analysis and modelling of window and glazing systems energy performance for a well insulated residential building. Energy Build. 43 (4), 1030–1037. https://doi.org/ 10.1016/j.enbuild.2010.12.032. Gaviria, V.G., Díaz, L.A., Tell´ ez, E.H., 2020. Assessment of light performance in work environments, based on the non-visual effects of natural light on health. Archit., City Environ. 15 (43), 1–14. https://doi.org/10.5821/ace.15.43.8903. Gobierno de Espa˜ na, Ministerio de Industria, T. y Comercio, &Instituto para la Diversificaci´ on y Ahorro de Energía (2009). documentos_CALENER_07_Escala_Calif_ Energetica_A2009_A_5c0316ea.〈https://www.idae.es/uploads/documentos/do cumentos_CALENER_07_Escala_Calif_Energetica_A2009_A_5c0316ea.pdf〉. Koˇ sir, M., Igliˇ c, N., Kuniˇ c, R., 2018. Optimisation of heating, cooling and lighting energy performance of modular buildings in respect to location’s climatic specifics. Renew. Energy 129, 527–539. https://doi.org/10.1016/j.renene.2018.06.026. Lo Verso, V.R.M., Giuliani, F., Caffaro, F., Basile, F., Peron, F., Dalla Mora, T., Bellia, L., Fragliasso, F., Beccali, M., Bonomolo, M., Nocera, F., Costanzo, V., 2021. Questionnaires and simulations to assess daylighting in Italian university classrooms for IEQ and energy issues. Energy Build. 252 https://doi.org/10.1016/j. enbuild.2021.111433. M. ElBatran, R., Ismaeel, W.S.E., 2021. Applying a parametric design approach for optimizing daylighting and visual comfort in office buildings. Ain Shams Eng. J. 12 (3), 3275–3284. https://doi.org/10.1016/j.asej.2021.02.014. Ma, J., Yang, Q., 2023. Optimizing annual daylighting performance for atrium-based classrooms of primary and secondary schools in Nanjing, China. Buildings 13 (1). https://doi.org/10.3390/buildings13010011. Mangkuto, R.A., Dewi, D.K., Herwandani, A.A., Koerniawan, M.D., Faridah, 2019. Design optimisation of internal shading device in multiple scenarios: case study in Bandung, Indonesia. J. Build. Eng. 24 https://doi.org/10.1016/j.jobe.2019.100745. Mangkuto, R.A., Fela, R.F., Utami, S.S., 2019. Effect of façade thickness on daylight performance in a reference office building. Build. Simul. Conf. Proc. 2, 1044–1051. https://doi.org/10.26868/25222708.2019.210503. Marzouk, M., ElSharkawy, M., Mahmoud, A., 2022. Optimizing daylight utilization of flat skylights in heritage buildings. J. Adv. Res. 37, 133–145. https://doi.org/ 10.1016/j.jare.2021.06.005. MINVU. (2017). Resumen de Modificaciones y Rectificaciones de la Ordenanza General de Urbanismo y Construcciones * vigencia desde fecha publicaci´ on Diario Oficial. D. S. Fecha Vigencia Materia. 〈www.minvu.cl〉. Montaser Koohsari, A., Heidari, S., 2022. Subdivided venetian blind control strategies considering visual satisfaction of occupants, daylight metrics, and energy analyses. Energy Build. 257 https://doi.org/10.1016/j.enbuild.2021.111767. Mostafavi, F., Tahsildoost, M., Zomorodian, Z.S., Shahrestani, S.S., 2022. An interactive assessment framework for residential space layouts using pix2pix predictive model at the early-stage building design. Smart Sustain. Built Environ. https://doi.org/ 10.1108/SASBE-07-2022-0152. Mu˜ noz-Viveros, C., P´ erez-Fargallo, A., Rubio-Bellido, C., 2022. Influence of the type of solar protection on thermal and light performance in classrooms. Energy Rep. 8, 5329–5340. https://doi.org/10.1016/j.egyr.2022.04.007. Piraei, F., Matusiak, B., Lo Verso, V.R.M., 2022. Evaluation and optimization of daylighting in heritage buildings: a case-study at high latitudes. Buildings 12 (12). https://doi.org/10.3390/buildings12122045. Salom´ on, D., Ambroggio, S.A., 2022. Optimization of classroom design: use of natural light for visual comfort in Villa María, Argentina. Habitat Sustentable 12 (1), 74–89. https://doi.org/10.22320/07190700.2022.12.01.05. Sepúlveda, A., De Luca, F., Thalfeldt, M., Kurnitski, J., 2020. Analyzing the fulfillment of daylight and overheating requirements in residential and office buildings in Estonia. Build. Environ. 180 https://doi.org/10.1016/j.buildenv.2020.107036. Sun, C., Liu, Q., Han, Y., 2020. Many-objective optimization design of a public building for energy, daylighting and cost performance improvement. Appl. Sci. 10 (7) https:// doi.org/10.3390/app10072435. Turan, I., Chegut, A., Fink, D., Reinhart, C., 2020. The value of daylight in office spaces. Build. Environ. 168 https://doi.org/10.1016/j.buildenv.2019.106503. Uribe, D., Vera, S., Bustamante, W., McNeil, A., Flamant, G., 2019. Impact of different control strategies of perforated curved louvers on the visual comfort and energy consumption of office buildings in different climates. Sol. Energy 190, 495–510. https://doi.org/10.1016/j.solener.2019.07.027. Vaisi, S., Firouzi, M., Varmazyari, P., 2023. Energy benchmarking for secondary school buildings, applying the Top-Down approach. Energy Build. 279 https://doi.org/ 10.1016/j.enbuild.2022.112689. Wei, W., Wargocki, P., Zirngibl, J., Bendˇ zalov´ a, J., Mandin, C., 2020. Review of parameters used to assess the quality of the indoor environment in Green Building C. Mu˜ noz-Viveros et al. Energy Reports 12 (2024) 1157–1177 1177 certification schemes for offices and hotels. In: In Energy and Buildings, Vol. 209. Elsevier Ltd. https://doi.org/10.1016/j.enbuild.2019.109683. Wen, S., Hu, X., Hua, G., Xue, P., Lai, D., 2023. Comparing the performance of four shading strategies based on a multi-objective genetic algorithm: a case study in a university library. J. Build. Eng. 63 https://doi.org/10.1016/j.jobe.2022.105532. Zhu, L., Wang, B., Sun, Y., 2020. Multi-objective optimization for energy consumption, daylighting and thermal comfort performance of rural tourism buildings in north China. Build. Environ. 176, 106841 https://doi.org/10.1016/j. buildenv.2020.106841. Zomorodian, Z.S., Tahsildoost, M., 2017. Assessment of window performance in classrooms by long term spatial comfort metrics. Energy Build. 134, 80–93. https:// doi.org/10.1016/j.enbuild.2016.10.018. Zomorodian, Z.S., Tahsildoost, M., 2019. Assessing the effectiveness of dynamic metrics in predicting daylight availability and visual comfort in classrooms. Renew. Energy 134, 669–680. https://doi.org/10.1016/j.renene.2018.11.072. C. Mu˜ noz-Viveros et al.