Window design in architecture: Analysis of energy savings for lighting and visual comfort in residential spaces

1 Window design in architecture: analysis of energy savings and visual comfort in residential spaces Author’s name and affiliation: Ignacio Acosta, Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla. Professor PhD. Corresponding Author. Miguel Ángel Campano, Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla. Researcher PhD. Juan Francisco Molina, Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla. Researcher PhD. Corresponding Author: Ignacio Acosta, University of Seville, Corresponding Author Tel. number: 0034647550654 Email: [email protected] Permanent address: Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla, 41012 Seville. Spain. Abstract Window design is decisive in providing appropriate visual comfort for occupants and sufficient energy savings in electric lighting. In daylighting, visual comfort is dependent on the maximum daylight autonomy. Moreover, energy consumption in electric lighting relies on daylight autonomy. The aim of this research is to quantify these metrics in a residential room for different window models and analyze the results obtained. The shape, size and position of the window are variable, as is the reflectance of the inner surfaces. Simulations are provided by the lighting simulation program DaySim 3.2. Following the trials, it was concluded that daylight autonomy is proportional to the glass surface and reflectance of surfaces at the back of the room, while its influence near the façade is negligible. However, energy consumption does not depend on window shape. It is also concluded that windows located higher up result in higher illuminance at the back of the room than those in centered locations. The conclusions of this research are contrasted with the analysis of daylight metrics for different locations and orientations of the window. Keywords: window, daylight autonomy, energy savings, daylighting, lighting simulation program. 2 1. Introduction and objectives 1.1. State of the art At present, energy saving is one of the most important variables in building design. Proper use of daylighting is essential in reducing energy consumption in electric lighting while maximizing visual comfort for occupants. As can be seen from previous research, daylighting improves visual perception [1] and synchronization of the circadian stimulus [2]. Accordingly, windows are the greatest resource to allow daylight into buildings [3]. A proper window design also improves the thermal comfort and produces a significant energy savings in electric lighting. [4]. The most common metric for quantifying daylighting in architecture is the daylight factor, as it defines the ratio of the illuminance inside a room to that observed outside under overcast sky conditions. Currently, the daylight factor is the most used metric in the analysis of daylighting [5]. Since daylight factors are evaluated under overcast conditions, the position of the sun is irrelevant, hence the room location and the window orientation do not affect to the calculation results. Therefore, the measurement of daylight factors is only conditioned by the geometry of the architecture. According to the definition of daylight factor, the determination of the illuminance at an interior point is immediate as long as the external illuminance is known. However, the daylight factor is not a completely reliable metric when defining the energy savings in electric lighting, given that it ignores daylight produced under clear sky conditions [6]. Therefore, as stated in current research, it is increasingly common to use dynamic metrics in daylighting studies, defining the energy savings according to the orientation of the window, location of the room and sky conditions. One of the most noteworthy dynamic metrics is daylight autonomy, proposed in 1989 by the Association Suisse des Electriciens [7] and redefined by Reinhart et al. [8]. Daylight autonomy is defined as the percentage of the year when a minimum illuminance threshold is met by daylight alone. According to this definition, the higher the daylight autonomy, the lower the energy consumption in electric lighting. Window design has been widely studied in the analysis of daylight factors. Most current treatises on lighting in architecture [9,10] study the proper sizes and shapes for windows. Studies on window design are usually based on empirical methods and lighting simulation programs. The results obtained from empirical methods are not accurate, as can be deduced from the daylight factor method [11], which is defined as a calculation procedure with limited calculation variables [12]. Other empirical procedures provide results with similar precision [13,14]. However, current lighting simulation programs provide a better accuracy than empirical procedures [15,16], making them useful tools for the study of daylighting and energy savings in architecture. 3 There is an abundance of studies of window design using static metrics from lighting simulation software. An example of this is the calculation method developed by Ghisi et al. [17], which defines the ideal window area for maximum efficiency considering the combination of electric and natural lighting. The authors conclude that the ideal window area should be larger in low thermal load orientations. Another interesting example can be observed in the research of Li et al. [18], who determined a calculation method which relies on the daylight coefficient concept. In this study, the authors determine a procedure based on multiple charts and tables for defining the illuminance values. Following this methodology, the study by Acosta et al. [19] determines the rules of thumb for window design based on the analysis of daylight factors under overcast sky conditions. When examining the study of static metrics, it is worth noting the research of Fasi et al. [20], which analyzes the energy savings when daylight and electric light are integrated while maintaining visual comfort. Most current studies supported by lighting simulation programs analyze daylight factors resulting from the use of shading devices or blinds. One noticeable research in this field is that by Alzoubi et al. [21] analyzing the performance of horizontal and vertical shading devices. The authors determine the optimal orientation for shading devices in order to get an illuminance threshold reducing the solar heat gain. It is also worth noting the study by Sanati et al. [22] which determines that window blinds and shading devices are not suitably used by the occupants and that higher reduction of energy consumption is obtained in electric lighting thanks to the use of slat systems . Moreover, the research by Villalba et al. [23] uses shading models based on blinds to determine the permeability of urban vegetation, providing a valuable contribution on this topic. Although dynamic metrics are not used in most of the current studies on daylighting, an important example of their use can be observed in the study by Munoz et al. [24], where the authors determine different dynamic metrics in an office room illuminated by openings. Studies on window design are not based solely on daylighting conditions. Another approach focuses on thermal comfort, as can be seen in some research [25,26]. Equally, new metrics such as the circadian stimulus are defined according to window design [27]. Overall it becomes patent that the study of windows is an endless source of research results. 1.2. Aim and objectives The aim of this research is to quantify the daylight dynamic metrics in a residential room for different models of windows, in order to analyze the results obtained. The shape, size and position of the window are variable, as is the reflectance of the inner surfaces of the room. Therefore, daylight autonomy is defined in more conventional residential rooms, serving as a reference for window design in architecture in order to reduce the energy consumption in electric lighting. Subsequently, the analysis of the resulting daylight autonomy values defines criteria for shape, size and position of the window, according to the orientation and location of the residential room. The design guidelines defined in this research also 4 consider other dynamic metrics such as maximum daylight autonomy, which establishes visual comfort in relation to a maximum illuminance threshold. 2. Description of Methodology for Calculation 2.1. Characteristics of the room model A virtual room measuring 3.0 m wide × 6.0 m deep × 3.0 m high, the size of a typical living room in the studied locations, was used to analyze the daylight dynamic metrics and the energy savings provided by windows. The room ceiling, walls and floor had a thickness of 0.25 m. A window of variable shape, size and position is located in the 3.00 m wide façade. The double pane window was 0.05 m thick with a visible transmission of 0.75. Daylight simulations were conducted using the window sizes listed in Table 1 with two room surface average reflectances, also listed in Table 1. The inner surfaces of the room were assumed to display Lambertian reflectances. The luminous intensity of reflected light was therefore directly proportional to the cosine of the angle between the observer's line of sight and the surface normal. All variables of the calculation model are shown in Figure 1: Figure 1: Calculation model. The daylight dynamic metrics are measured on five equidistant axes at 0.75 m, considering a symmetry axis respect to the window position. As can be observed in Figure 1, the study points are located on these axes with a spacing of 0.30 m from each other and at 0.60 m above floor level. 100 study points in total are used in each model. 5 The room model is defined according to window shape (square, horizontal and vertical), size (window-to-façade ratios of 10- 80%) and position (centered or upper). The reflectance of the inner surfaces is also variable depending on the values represented in Figure 1. 28 room models are established following the variables defined, as shown in Table 1: Table 1: Calculation models according to defined variables. Model Length/ Height Window surface Window dimensions Window/ Façade Window position Glass surface Glass solar factor Wall reflectance Floor reflectance Ceiling reflectance Average reflectance S.10.C.B 1 ꞏ 1 0.90 m² 0.95ꞏ0.95 m 10.00% Centered 0.64 m² 0.7 0.8 0.6 0.8 0.75 S.10.C.D 1 ꞏ 1 0.90 m² 0.95ꞏ0.95 m 10.00% Centered 0.64 m² 0.7 0.4 0.2 0.6 0.40 S.10.U.B 1 ꞏ 1 0.90 m² 0.95ꞏ0.95 m 10.00% Upper 0.64 m² 0.7 0.8 0.6 0.8 0.75 S.10.U.D 1 ꞏ 1 0.90 m² 0.95ꞏ0.95 m 10.00% Upper 0.64 m² 0.7 0.4 0.2 0.6 0.40 S.20.C.B 1 ꞏ 1 1.80 m² 1.34ꞏ1.34 m 20.00% Centered 1.41 m² 0.7 0.8 0.6 0.8 0.74 S.20.C.D 1 ꞏ 1 1.80 m² 1.34ꞏ1.34 m 20.00% Centered 1.41 m² 0.7 0.4 0.2 0.6 0.39 S.20.U.B 1 ꞏ 1 1.80 m² 1.34ꞏ1.34 m 20.00% Upper 1.41 m² 0.7 0.8 0.6 0.8 0.74 S.20.U.D 1 ꞏ 1 1.80 m² 1.34ꞏ1.34 m 20.00% Upper 1.41 m² 0.7 0.4 0.2 0.6 0.39 S.30.C.B 1 ꞏ 1 2.70 m² 1.64ꞏ1.64 m 30.00% Centered 2.22 m² 0.7 0.8 0.6 0.8 0.74 S.30.C.D 1 ꞏ 1 2.70 m² 1.64ꞏ1.64 m 30.00% Centered 2.22 m² 0.7 0.4 0.2 0.6 0.39 S.40.C.B 1 ꞏ 1 3.60 m² 1.90ꞏ1.90 m 40.00% Centered 3.06 m² 0.7 0.8 0.6 0.8 0.73 S.40.C.D 1 ꞏ 1 3.60 m² 1.90ꞏ1.90 m 40.00% Centered 3.06 m² 0.7 0.4 0.2 0.6 0.38 S.60.C.B 1 ꞏ 1 5.40 m² 2.32ꞏ2.32 m 60.00% Centered 4.71 m² 0.7 0.8 0.6 0.8 0.71 S.60.C.D 1 ꞏ 1 5.40 m² 2.32ꞏ2.32 m 60.00% Centered 4.71 m² 0.7 0.4 0.2 0.6 0.38 S.80.C.B 1 ꞏ 1 7.20 m² 2.68ꞏ2.68 m 80.00% Centered 6.40 m² 0.7 0.8 0.6 0.8 0.70 S.80.C.D 1 ꞏ 1 7.20 m² 2.68ꞏ2.68 m 80.00% Centered 6.40 m² 0.7 0.4 0.2 0.6 0.37 H.10.C.B 2 ꞏ 1 0.90 m² 1.34ꞏ0.67 m 10.00% Centered 0.65 m² 0.7 0.8 0.6 0.8 0.75 H.10.C.D 2 ꞏ 1 0.90 m² 1.34ꞏ0.67 m 10.00% Centered 0.65 m² 0.7 0.4 0.2 0.6 0.40 H.10.U.B 2 ꞏ 1 0.90 m² 1.34ꞏ0.67 m 10.00% Upper 0.65 m² 0.7 0.8 0.6 0.8 0.75 H.10.U.D 2 ꞏ 1 0.90 m² 1.34ꞏ0.67 m 10.00% Upper 0.65 m² 0.7 0.4 0.2 0.6 0.40 H.20.C.B 2 ꞏ 1 1.80 m² 1.90ꞏ0.95 m 20.00% Centered 1.45 m² 0.7 0.8 0.6 0.8 0.74 H.20.C.D 2 ꞏ 1 1.80 m² 1.90ꞏ0.95 m 20.00% Centered 1.45 m² 0.7 0.4 0.2 0.6 0.39 H.20.U.B 2 ꞏ 1 1.80 m² 1.90ꞏ0.95 m 20.00% Upper 1.45 m² 0.7 0.8 0.6 0.8 0.74 H.20.U.D 2 ꞏ 1 1.80 m² 1.90ꞏ0.95 m 20.00% Upper 1.45 m² 0.7 0.4 0.2 0.6 0.39 V.10.C.B 1 ꞏ 2 0.90 m² 0.67ꞏ1.34 m 10.00% Centered 0.58 m² 0.7 0.8 0.6 0.8 0.75 V.10.C.D 1 ꞏ 2 0.90 m² 0.67ꞏ1.34 m 10.00% Centered 0.58 m² 0.7 0.4 0.2 0.6 0.40 V.20.C.B 1 ꞏ 2 1.80 m² 0.95ꞏ1.90 m 20.00% Centered 1.35 m² 0.7 0.8 0.6 0.8 0.74 V.20.C.D 1 ꞏ 2 1.80 m² 0.95ꞏ1.90 m 20.00% Centered 1.35 m² 0.7 0.4 0.2 0.6 0.39 The variables of the calculation models were established following the most common values of shape, size and position of the window of a typical residential room. Although this study sample obviously cannot cover all possible hypotheses, it does aim to show the most usual case studies. 6 2.2. Parameters of the calculation program DAYSIM 3.1 is a validated RADIANCE-based daylighting analysis tool that uses a daylight coefficient approach combined with the Perez all-weather sky model [28] to predict the amount of daylight in and around buildings, based on direct normal and diffuse horizontal irradiances taken from a climate file. DAYSIM was developed to provide a more efficient calculation of illuminance or luminance time series under varying sky conditions than that originally provided by RADIANCE. This lighting software has been validated by several researchers [29,30] who have determined accuracy by applying the CIE test cases [15]. The calculation parameters used by this program in this research are shown in Table 2. Table 2: Parameters of the calculation program. Ambient Bounces 7 Ambient Divisions 1500 Ambient Su p e r -sam p les 100 Ambient Resolution 300 Ambient Accurac y 0.05 Limit Reflection 10 S p ecular Threshol d 0.0000 S p ecular Jitte r 1.0000 Limit Wei g ht 0.0040 Direct Jitte r 0.0000 Direct Sam p lin g 0.2000 Direct Rela y s 2 Direct Pretest Densit y 512 2.3. Calculation conditions 2.3.1. Location of the room model The main location of the room model corresponds to London, UK at 50º north latitude under predominantly overcast skies. All trials relating to shape, size and position of the window are developed under these weather conditions. In order to illustrate the impact of variation of daylight availability, three complementary locations were chosen for a further trial: Madrid, Spain, at 40º north latitude under predominantly clear skies, Stockholm, Sweden, near 60º north latitude under mainly overcast skies and Munich, Germany, close to 50º north latitude under predominantly intermediate skies. Weather data for all locations are taken from the Energy Plus reference [31], based on direct normal and diffuse horizontal irradiances and the sky model defined by Perez et al. [32]. 7 2.3.2. Orientation of the window The window orientation used for all calculation models and locations is north. Illuminance values are lower for north-facing windows [24], so this represents the worst case scenario for determining daylight illuminance. However, in order to expand on the conclusions obtained, other orientations were considered in a further trial. 2.3.3. Daylight metrics and conditions The illuminance threshold for the definition of daylight autonomy (DA) in this research corresponds to 250 lux, sufficient to develop the tasks usually carried out in a residential room. Proper daylight autonomy value is usually considered to be 50% [33]. Accordingly, the maximum daylight autonomy (DAmax), which defines the glare conditions under natural light [8], is set at 2,500 lux, ten times the threshold of daylight autonomy. Useful daylight illuminance (UDI) aims to determine when daylight levels can be of use for the occupant [34]. Based on the upper and lower thresholds of 2,000 lux and 100 lux, UDI results in three metrics: the percentages of the occupied times of the year when the UDI was achieved (from 100 to 2,000 lux), fell-short (lower than 100 lx), or was exceeded (more than 2,000 lux). The UDI range can vary depending on the location or culture conditions [35]. Proper useful daylight illuminance is considered to be 50% [33]. This metric is complementary to daylight autonomy and it does not represent the energy savings in electric lighting, hence it is ignored in this research. The occupancy time considered in this research begins at 8.00 am and finishes at 5.00 pm, following the utilization of natural light in a conventional residential room. In the case of the main location, London, UK, there is not enough sunlight after 5.00 pm to provide the minimum illuminance required when carrying out regular tasks in a residential room. 3. Calculation 3.1. Quantification of daylight dynamic metrics with square window According to the methodology described, Figure 2 shows the daylight dynamic metrics measured at the study points in residential rooms with square windows, considering a window-to-façade ratio between 10 and 20%. The room window is facing north and is located in London, UK. As can be observed in Figure 2, the maximum daylight autonomy is negligible in these room models, given that there is not enough glass surface to permit high sunlight incidence throughout the year. Moreover, the daylight autonomy is not sufficient for most of the room models, except in the cases of rooms with high reflectance and a window-to-façade ratio of 20% (S.20.C.B and S.20.U.B) for the zone near the façade. As can be seen, the daylight autonomy decreases drastically at the back of the room when the reflectance of the surfaces is lower, but does not usually vary when surface reflectance is higher. 8 Figure 2: Daylight dynamic metrics measured with square windows with a surface between 10 and 20% of the façade. Continuing with the results obtained, Figure 3 shows the daylight dynamic metrics measured at the study points in residential rooms with square windows, considering a window-to-façade ratio between 30 and 80%. The square windows in upper position with a surface equal to or greater than 30% of the façade are ignored in the calculation models, as the opening would be outside the surface of the façade. As can be deduced from Figure 3, maximum daylight autonomy is noticeable in the area near the façade for room models with a window-to-façade ratio equal to or higher than 60%, while the daylight autonomy is acceptable over the entire work plane. This means that there is enough daylight illuminance throughout the year for these room models, although there is a high probability of daylight glare occurring in the area near the window. 9 Figure 3: Daylight dynamic metrics measured with square windows with a surface between 30 and 80% of the façade. Considering the window-to-façade ratios between 30 and 40%, the daylight autonomy is acceptable, although this statement is only valid for the room models with high reflectance surfaces (S.30.C.B and S.40.C.B). Moreover, as maximum daylight autonomy is low for these room models, the daylight glare probability is acceptable. It is worth noting that the reflectance of the inner surfaces of the room is key in providing good illumination at the back of the room, regardless of window size. Window-to-façade ratios higher than 30% allow daylight autonomy values higher than 50% at the back of the room, as long as the reflectance of the inner surfaces is high. 16 the daylight autonomy values are directly proportional to the variation of the reflectance index at the back of the room, considering the largest window. This ratio decreases in the case of smaller windows. 5. Additional calculations 5.1. Quantification of daylight dynamic metrics with different orientations Seeking confirmation of the findings obtained, Figure 10 shows the daylight dynamic metrics measured at study points in residential rooms with square windows, considering a window-to-façade ratio of 20 and 40%. The window orientation is, in this case, variable and the room studied is in London, UK, as in previous trials. Figure 10: Daylight dynamic metrics measured with square windows with a surface of 20 and 40% of the façade and variable orientation. 17 As can be deduced from Figure 10, the orientation of the window does not affect daylight autonomy values when the window- to-façade ratio is equal to or higher than 40%. This is because larger windows easily attain the illuminance threshold set by the definition of daylight autonomy, regardless of the orientation of the window. However, it is to be noted that maximum daylight autonomy varies according to window orientation, varying from 1.2 meters for a north-facing window to 4.2 meters for a south-facing window. This means, as can be supposed, that sunlight reaches further into the room when the window is facing south. In the case of rooms with window-to-façade ratios equal to or lower than 20%, daylight autonomy values vary according to window orientation, obtaining higher values for windows facing south at the back of the room. In this case, the variation of maximum daylight autonomy for south-facing windows is also noticeable. In order to analyze the results obtained, the relative difference of daylight autonomy values is calculated according to window orientation, considering a window-to-façade ratio of 20%. This relative difference is defined as the ratio of the daylight autonomy values produced by variation in window orientations from those obtained by openings facing north. The daylight autonomy values evaluated correspond to those measured at the central axis. The relative difference of daylight autonomy according to window orientation is shown in Figure 11. Figure 11: Relative difference of daylight autonomy according to window orientation. As can be deduced from Figure 11, analyzing the zone nearest to the back of the room, the south-facing window produces triple daylight autonomy compared to north-facing windows. Moreover, east or west openings produce an increase of daylight autonomy values close to twice that of north-facing windows. The east-facing opening produces higher daylight autonomy values than that facing west because the occupancy hours defined in the methodology establish higher occupancy during the morning. 18 5.2. Quantification of daylight dynamic metrics with different locations The final additional calculation shows the daylight dynamic metrics according to different locations of the room model and a window-to-façade ratio of 20 and 40%. The room location is variable, considering four locations with different latitudes and weather conditions: London (LON), Madrid (MAD), Stockholm (STO) and Munich (MUN). The room sections are shown in Figure 12: Figure 12: Daylight dynamic metrics measured with square windows with a surface of 20 and 40% of the façade and variable location. 19 As can be deduced from Figure 12, the Madrid location, which corresponds to the lowest latitude with mainly clear skies, produces higher daylight autonomy values at the back of the room. However, the variation of dynamic metrics in the zone near the façade does not depend on the location of the room, as can be deduced from the comparison of the case study sections. As in the previous analysis, the relative difference of the daylight autonomy values is calculated according to the room location, considering a window-to-façade ratio of 20%. This relative difference is defined as the ratio of the daylight autonomy values produced by the different room locations to those obtained in London. The daylight autonomy values evaluated correspond to those measured at the central axis. The relative difference of the daylight autonomy according to room location is shown in Figure 13. Figure 13: Relative difference of daylight autonomy according to room location. As seen in Figure 13, the relative difference of daylight autonomy according to different locations tends to converge in the zone near the façade, that is to say, room location does not affect energy savings in the area close to the window. In addition, location is key in quantifying daylight autonomy at the back of the room. Considering the London location as the reference with an illuminance threshold of 250 lux, as defined in the methodology, the Madrid location, at 40º latitude with mainly clear skies, produces a nearly five-fold increase in energy savings in the area near the back of the room. In the case of the Munich location, near 50º latitude with intermediate skies, the increase is higher than three times. Finally, it is noticeable that Stockholm, located close to 60º latitude with mainly overcast skies, produces an increase in daylight autonomy higher than two times, despite the fact that it is at a higher latitude than London. Therefore, it is concluded that weather conditions are more significant than latitude in the calculation of daylight autonomy and the measurement of energy savings in electric lighting. 20 6. Conclusions Nowadays, building construction evolves toward new designs which aim to minimize energy consumption. Energy efficiency will certainly be one of the most important principles in building design in the near future. As exposed in the state of the art, daylight dynamic metrics are the most reliable tools for determining energy savings making proper use of daylighting, reducing energy consumption in electric lighting. In this research, the analysis of results has evaluated the variation of daylight dynamic metrics in accordance with the geometry and position of the opening, as well as the reflectance of the inner surfaces of the room, reaching several conclusions applicable to window design. The measurement of daylight dynamic metrics serves to argue the analysis of the results. In addition, it offers a database of daylighting provided by windows in residential rooms. For this reason, the most common window models of current architecture have been chosen for the assessment of energy savings. The study sample defined in the methodology cannot cover all possible hypotheses, but it does aim to show the most usual case studies. The first conclusion of this study can be deduced from Figure 6, which shows that all window shapes produce similar values of daylight autonomy at the central axis, regardless of the distance from the façade. However, as daylight autonomy values at the side axes are higher for horizontal windows it can be concluded that this shape produces more uniform illumination and is more effective in energy savings than other shapes of opening. From the analysis of window size in Figure 7 it can be observed that the daylight autonomy provided by variable sized openings is directly proportional to the glazed surface at the back of the room. Accordingly, it can be assessed that window size is not relevant for energy saving in the area near the façade. However, the size of the opening is decisive at the back of the room, where the dependence on electric lighting is higher than at the front. Regarding window position, analyzed in Figure 8, windows in an upper position provide a higher performance of daylight autonomy in the middle and at the back of the room. Therefore, the energy savings in electric lighting can be quantified as almost 10% in the middle of the room and near 30% at the back of the room. The fourth trial analyzes the variations in reflectance of the inner surfaces of the room and their influence on energy savings in electric lighting. From this it can be concluded that the reflectance of the inner surfaces of the room does not affect the measurement of daylight autonomy in the area close to the window. However, the daylight autonomy values are directly proportional to the variation of the reflectance index at the back of the room, considering the largest window. This ratio decreases in the case of smaller windows. 21 Two additional calculations were developed to confirm the findings of this research. The first of these trials studies the variation of window orientation. As can be deduced from Figure 10 and 11, the orientation of windows does not affect energy savings when the window-to-façade ratio is equal to or higher than 40%, considering an illuminance threshold of 250 lux. However, in the case of smaller windows and the analysis of the area close to the back of the room, the south-facing window produces triple daylight autonomy to that facing north. Moreover, east- or west-facing openings produce an increase in daylight autonomy values close to twice that of north-facing windows. The second study corresponds to the analysis of energy savings considering several weather locations. As seen in Figure 13, the relative difference of daylight autonomy according to different locations tends to converge in the zone near the façade, that is to say, room location does not affect energy savings in the area close to the window. Moreover, location is decisive in quantifying the daylight autonomy at the back of the room. Following the methodology described, the measurement of daylight autonomy values can increase up to four times depending on different latitudes and weather conditions. 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