Tree and neighboring buildings shading effects on the thermal performance of a house in a warm sub-humid climate

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1 BUILD SIMUL DOI /s Tree and neighboring buildings shading effects on the thermal performance of a house in a warm sub-humid climate E. Simá 1 ( ), M. A. Chagolla-Aranda 1, G. Huelsz 2, R. Tovar 2, G. Alvarez 1 1. Centro Nacional de Investigación y Desarrollo Tecnológico-CENIDET-DGEST-SEP, Mechanical Engineering Department, Palmira S/N, Cuernavaca, Mor , México 2. Instituto de Energías Renovables, Universidad Nacional Autónoma de México, A.P. 34 Temixco Centro, Temixco, Mor , México Research Article Abstract This work quantifies the effect of the tree shading and the effect of neighboring buildings shading on the thermal performance of a non-air-conditioned house in a warm sub-humid climate. Experimental measurements and simulations using EnergyPlus of the unoccupied house were conducted from April to December 2011 with tree shading and from January to April 2012 without tree shading. The simulations, that also considered the neighboring shading, were validated with the experimental results. To assess the effect of the tree shading, specific simulations were carried out with and without the tree for the weather conditions of December 2011 and April In addition, to assess the effect of neighboring building shading, simulations with and without neighboring buildings were conducted in April 2012, both without the tree. The main effects of the tree shading and of the neighboring buildings shading are the reduction of indoor air and envelope surface temperatures. It was shown that not taking into account the neighboring buildings shading gives a difference in average indoor air temperature up to 2.3 C. The effect of the tree shading is greater in April (the warmest month) than in December (the coldest month). As indoor air temperatures in the coldest month are in the comfort range for the tree shading case, it is concluded that evergreen trees are adequate for this climate. Keywords tree, neighboring buildings, shading, thermal performance, warm sub-humid climate Article History Received: 20 February 2015 Revised: 8 July 2015 Accepted: 10 July 2015 Tsinghua University Press and Springer-Verlag Berlin Heidelberg Introduction Shading from trees and adjacent structures on buildings can modify their thermal performance. Shading on buildings in hot and warm climates or in the warm and hot seasons is beneficial, it improves the thermal comfort when the buildings are not air-conditioned and reduces energy consumption for cooling when they are air-conditioned. Nevertheless, shading can be negative for cold climates or during the cold season. Although potentially significant, the impact of trees and neighboring structures on the thermal performance of buildings is often neglected in building energy analysis. Many studies have been conducted to assess the effect of shading by neighboring buildings and trees on the energy consumption of buildings, but none of them have studied the thermal performance of a non-air-conditioned building. In 1990, Meier (1990) presented a literature review on the strategic landscaping and air-conditioning savings where studies on tree shading effect were included. In what follows, the studies are classified into methods to simulate the external shading on the building envelope, studies on the effect of external shading on energy consumption of individual buildings, and studies of the impact of the external shading on energy consumption in an entire community or city. Methods to simulate the shading on walls and roofs of the building envelope are reported for shading produced by neighboring buildings (Frank et al. 1981; Hwang et al. 2011) and by trees (Berry et al. 2013; Gómez-Muñoz et al. 2010; Raeissi and Taheri 1999; Sattler et al. 1987). The effect of neighboring buildings shading on the energy consumption of individual buildings has been studied using building energy simulations with the DOE program (Lam 2000), EnergyPlus (Li and Wong 2007), and self-programs (Purdy and Beausoleil-Morrison 2001). Farrar-Nagy et al. Architecture and Human Behavior esima@cenidet.edu.mx

2 2 (2000) made experimental measurements and energy simulations using the DOE program, for a period of 12 days in the summer, of an unoccupied house in Tucson, Arizona, USA. They pointed out the importance of accurately modeling the shading due to neighboring buildings in order to reproduce their experimental results. Laband and Sophocleus (2009) studied the effect of tree shading on an individual building using experimental measurements of the energy consumption in two identical buildings, one with tree shading and the other without it. This study was performed during a summer in Beauregard, Alabama, USA, achieving energy savings of 62% due to tree shading during this period. Only the study of Akbari et al. (1997) has used both experimental measurements and building energy simulations to analyze the effect of tree shading. The authors quantified the effect on the energy consumption for cooling of two similar houses with and without tree shading in Sacramento, California, USA during a summer. They reported experimental energy savings of 30% for this period. The simulations, performed with DOE, underestimated the energy savings by as much as twofold. Most of the studies on the tree shading effect have used only building energy simulations. Raessi and Tahari (1998, 1999) used a program developed by them and incorporated a model of tree shading on the envelope. Hes et al. (2011) reported the first results of the use of the program IES-VE to calculate the cooling energy savings due to tree shading applying two approaches to model the shading effect, trees with different percentage coverage and the use of average hourly shading coefficients that have to be taken from experimental data. Higuchi and Udagawa (2007) using the program EESLISM developed by their group, studied the room temperature and energy consumption for an intermittently air-conditioned house, without shading and with shading of an evergreen tree and a deciduous tree. Almost all of the studies of the impact of shading in an entire community or city refer to tree shading and most of them used building energy simulations of prototype buildings with and without tree shading. These studies employed programs like DOE (Akbari and Taha 1992; Akbari et al. 2001; Akbari 2002; Konopacki and Akbari 2000) Micropas (Simpson and McPherson 1996, 1998). Using the program ESP-r, Nikoofard et al. (2011) simulated both the effect of trees and neighboring buildings shading for prototype buildings. Other studies that address the effect of tree shading at large scale have used statistical models that correlate electric energy use, and tree and building characteristics (Donovan and Butry 2009; Laverne and Lewis 1996; Pandit and Laband 2010; Simpson 2002). The results for 178 residences from the lookup tables given by one of these models (Simpson 2002) were compared with results of building energy simulations using Micropas (Simpson and McPherson 1998). The results matched within ±10%. The reduction of annual energy consumption for cooling produced by neighboring buildings shading reported in the studies is diverse due to climate and characteristics of the neighboring structures (orientation, height, and distance). This reduction ranged from 2% (Lam 2000) to 24% (Farrar- Nagy et al. 2000). Few studies reported the corresponding increase in energy for heating due to neighboring shading. The reported values ranged from negligible (Farrar-Nagy et al. 2000; Lam 2000) to 5% (Purdy and Beausoleil-Morrison 2001). The reduction of annual energy for cooling due to tree shading was reported to be from 10% to 40% (Raeissi and Taheri 1999). Also, few studies reported the increase on energy for heating due to tree shading. Higuchi and Udagawa (2007) reported that by using deciduous trees the increase in heating energy could be reduced with respect to using evergreen trees. For their simulations, the increase in heating energy is 8.5% for deciduous trees and 26% for evergreen trees. In the study of the combined effect of both trees and neighboring buildings shadings, Nikoofard et al. (2011) reported that the energy reduction for cooling is up to 90% and the increase on energy for heating is up to 10%. The objectives of the present work are to quantify the effect of the tree shading on the thermal performance of a non-air-conditioned house in a warm sub-humid climate, during the warmest and coldest month and to assess the effect of neighboring buildings shading. Experimental measurements and simulations using EnergyPlus of an unoccupied house with and without tree shading were conducted. Also, simulations were carried out with and without considering the neighboring buildings shading. 2 Case of study The house is located in Morelos State, Mexico, in the municipality of Emiliano Zapata, 18 50'43'' north latitude and 99 10'44'' west longitude, at an altitude of 1266 m above sea level. The climate in this region is characterized as warm sub-humid with average rainfall of 894 mm in summer and annual average temperatures of 22.4 C. As examples, Fig. 1 shows the daily maximum, average and minimum temperatures from April 2011 to April As it can be seen, in this latitude the warmest month was April and the coldest was December. The rectangles indicate the periods when measurements and simulations were performed. Figures 2 (a) (f) show the ambient temperature, horizontal global solar radiation, relative humidity and wind speed, for the weeks of April 14th 21st, December 24th 31st of 2011 and April 6th 13th, In the week of April 2011, ambient temperature oscillates between 16.1 C and 34.9 C, relative humidity and wind speed were in the ranges of 18% to 81% and 0.0 to 9.7 m/s, respectively. The highest horizontal global solar radiation was 1011 W/m 2 (April 18th). In the

3 3 Fig. 1 Maximum, average and minimum temperature from April 2011 to April 2012 week of December 24th 31st, ambient temperature oscillates between 13 C and 27 C, relative humidity and wind speeds were in the ranges of 25% to 80% and 0.0 to 4.0 m/s respectively. The highest horizontal global solar radiation was 830 W/m 2 at noon (December 24th). In the week of April 2012, ambient temperature oscillates between 16.9 C and 32.7 C, relative humidity and wind speed were in the ranges of 8 to 73% and 0.0 to 6.3 m/s, respectively and the highest horizontal global solar radiation was 1225 W/m 2 (April 7th). The test house is an unoccupied two-story house of total interior area of m 2 with a garden area of m 2. Figure 3(a) shows a schematic diagram of the housing development and points out the location of the test house Fig. 2 (a) Ambient temperature and horizontal solar radiation and (b) relative humidity and wind speed for the week of April 14th-21st, (c) Ambient temperature and horizontal solar radiation and (d) relative humidity and wind speed for the week of December 14th-31st, (e) Ambient temperature and global horizontal solar radiation and (f) relative humidity and wind speed for the week of April 6th-13th, 2012

4 4 Fig. 3 (a) Schematic diagram of the housing development and (b) image of the test house (c) test house built in Design Builder (red rectangle). The front facade of the test house is oriented 150 counter clockwise from the north. To the south and east of the house, there are neighboring buildings with similar characteristics. Adjacent to the house is a common green area that has a swimming pool. In this area there was a huge tree, 5 m away from the northwest side of the house. It was a perennial tree, named ficusmicrocarpa, taller than the house, 25 m high, with roughly 35 m shading diameter. Despite being a very tall tree, its main characteristic was that its foliage grew just 1 m from the ground, allowing shading almost from the base of the tree, so the shadow of the tree covered almost three sides of the test house and approximately 70% to 80% of the roof area. Figure 3 (b) shows a photograph of the test house with the tree. During the second semester of 2011 the tree began to lose its foliage, making it evident that it was infested by the borer worm. Unfortunately, the tree could not be saved with the applied treatment, and it had to be cut down in January This event, allowed the experimental study of the effect of the tree on the house. The neighboring buildings have the same geometric and architectural features as the test house. Opposite to the main facade there are six houses located 10 m away of the test house. On the east side there are three neighboring houses located at a distance of 5 m and to the west there are another six houses at 30 m. On the back side there are six houses at a distance of 15 m (they are part of another housing complex). The arrangement of the neighboring houses and the tree were introduced on the Design Builder software as block components, as shown in Fig. 3 (c). Detailed geometrical description of the test house and the two-story distribution of the house, divided into eight zones, are shown in Fig. 4. Table 1 shows the area, height and volume of each zone in the house. On the ground floor Fig. 4 Zone distribution of the house for the ground and first floors

5 5 there are the living dining room and kitchen (Z1), a halfbath (Z3) and a storage room (Z2). On the first floor there are two bedrooms (Z4 and Z5), two bathrooms (Z7 and Z8) and a study corridor room (Z6).The bedroom (Z4) and the study corridor (Z6) have a 10 tilted roof. Table 2 shows the materials of the envelope of the house and their thermophysical properties as referenced in the EnergyPlus libraries. The envelope colors are light yellow (paint) of absorptance of 0.54 and brown-gray (adobeconcrete natural color) of absorptance of 0.8. The openings of the test house are the following: the front facade has three windows and two doors, the main door is made of wood and the terrace door is made of glass with an aluminum frame; the back facade has five windows and two doors, one of the backdoors is made of wood and the other is made of glass with an aluminum frame. All windows have aluminum frames (without thermal break) and 3 mm clear glass. The technical description (material, area, and overall heat transfer coefficient U) of the facade openings is shown in Table 3. Table 1 Description of zones and dimensions Floor Zone Description Area (m 2 ) Height (m) Volume (m 3 ) P0 Z1 Living dining room kitchen P0 Z2 Storage room P0 Z3 Half-bath P1 Z4 Bedroom P1 Z5 Main bedroom P1 Z6 Study corridor P1 Z7 Main bathroom P1 Z8 Bathroom Table 2 Envelope materials, dimensions, and thermophysical properties Zone Material l (mm) Cp (J/(kg K)) λ (W/(m K)) ρ (kg/m 3 ) Tile Ground floor Mortar Concrete Tile First Mortar floor Concrete Plaster rendering Wall A Concrete brick Plaster rendering Wall B Concrete brick Plaster rendering Wall C Concrete brick Plaster rendering Plaster board Air space Tilted roof Concrete Air space Tile Plaster board Flat roof Air space Concrete Table 3 Description of the openings of the front and back facades Facade Zone Description Material Area (m 2 ) U (W/(m 2 K)) Front Back Z1 Main door Wood Z1 Window 1 Glass Z3 Window 2 Glass Z4 Window 3 Glass Z5 Terrace door Glass Z1 Back door Wood Z1 Back door Glass Z1 Window 4 Glass Z6 Window 5 Glass Z7 Window 6 Glass Z8 Window 7 Glass Measurements The test house was instrumented to measure the temperature of the indoor air and envelope wall and roof surfaces. Temperatures of indoor air were measured in four zones: Z1, Z4, Z5, and Z6. For each zone, a thermocouple was located 2.10 m from the floor and 0.20 m separated from an interior wall. Facade wall and roof surface temperatures were taken in zone Z4 in both indoor and outdoor surfaces, at the same position. Floor surface temperature was measured at zone Z1. All temperatures were measured with T-type 30 AWG thermocouples, which were connected to an acquisition system and the data was transmitted via internet. Ambient temperature, relative humidity, horizontal global solar radiation, wind speed, wind direction, barometric pressure, and rain were taken from a weather station located 6 km northwest from the house. House temperatures and weather data were recorded every 10 minutes during thirteen months. Experimental measurements were performed from April 1st 2011 to April 30th The minimum ambient temperature recorded was 9.2 C and the maximum was 35.6 C. The minimum relative humidity recorded was 5% and the maximum was 90%. Two months, April 2011 and April 2012, are reported here. All measurements were done with closed doors and windows and without a HVAC system.

6 6 4 Methodology The methodology of the study is described in Fig. 5. This methodology has four steps: First, climate conditions and data measurements are processed from April to December 2011 with Tree Shading (TS) and from January to April 2012 Without Tree Shading (WTS). Second, measurements are compared with the dynamic thermal simulations results for validation purposes. Specific results are used to be compared with the experimental cases: with Tree Shading (TS) for April 2011 and Without Tree Shading (WTS) for April, 2012 (bold letters), both considering the Neighboring buildings Shading (NS). Third, to evaluate the effect of tree shading, simulations are made to compare the cases TS and WTS, also considering the neighboring buildings shading (NS), for December 2011 and for April 2012 (shadow box). Fourth, to assess the effect of neglecting the neighboring shading, further simulations are performed in order to compare the cases with neighboring shading (NS) against the cases without neighboring shading (WNS), both for April 2012 without considering tree shading (WTS) (dashed box). The simulation model was performed using the Design Builder software, which is an interface to EnergyPlus that allows drawing the geometry and envelope features. The neighboring houses were created as block components and their influence was only considered for the shadowing calculations. To simulate the tree shading, the foliage was represented using block components with three flat planes that were perpendicular to each other, see Fig. 3 (c), projecting a shadow over the house similar to that of the tree. The arrangement of the planes was repeated until the shape of the tree was approached. Then, the climate conditions, test house location, properties of materials and building characteristics were input into the EnergyPlus program. The average monthly floor temperature measured on-site and infiltration of 0.7 air changes per hour were also introduced. As the house was uninhabited internal gains and ventilation were not considered (Chagolla et al. 2012). The parameters used for comparisons are monthly averages of the surface temperatures of the bedroom, and the monthly averages of air temperatures, decrement factors and lag times for four zones of the house Z1, Z4, Z5, and Z6; and adaptive comfort for zone Z4. The decrement factor and the lag time are two parameters used to evaluate the thermal performance of a whole building or a specific envelope system (Barrios et al. 2011). The decrement factor is used to measure the ability of the building to attenuate the amplitude of the sol air temperature oscillation to that of the indoor. The sol air temperature is defined as: the temperature which, under conditions of no direct solar radiation and no air motion, would cause the same heat transfer into a house as that caused by the interplay of all existing atmospheric conditions (ASHRAE 2009). Mathematically, the decrement factor is defined as the difference between the maximum and the minimum of the indoor air temperature, divided by the difference between the maximum and the minimum of the sol air temperature (Barrios et al. 2011; Sun et al. 2013). Thus, the smaller the decrement factor is, the more effective the building at suppressing sol air temperature swings. The lag time measures the ability of the building to delay the time at which the indoor temperature reaches its maximum respect to the sol air temperature. In general it is related to the ability of the building to accumulate thermal energy and to release it later. The lag time is defined as the delay of the maximum indoor air temperature with respect to the maximum of the sol air temperature (Barrios et al. 2011; Sun et al. 2013). The larger the lag time is, the more effective the building at delaying the indoor temperature maximum. The adaptive comfort method is used to calculate the thermal comfort range for a passive building which indicates the monthly comfort temperature range. It is calculated by the comfort temperature ± 2 C. The comfort temperature is determined by multiplying the monthly mean of the outdoor air temperature by a factor of 0.54 and adding 12.9 (Nicol 2004). 5 Experimental validation of the dynamic thermal simulations Fig. 5 Methodology of the study. Three simulations were developed for the case TS (considering NS) corresponding to April 2011, December 2011 and April 2012; two were developed for the case WTS (considering NS) (December 2011 and April 2012) and one for the case WNS (considering WTS) (April 2012). The cases TS and WTS (considering NS) were validated with experimental measurements The experimental validation of the dynamic thermal simulations is made comparing measured and simulated results for the bedroom, Z4. The comparison is made only for April 2011, with tree shading (TS), and for April 2012, without tree shading (WTS). In both simulations the neighboring buildings shading (NS) is considered.

7 7 Figure 6 presents a qualitative comparison between the measured and simulated temperatures for the week of April 14th 21st 2011, with TS. Figure 6(a) shows the measured and the simulated temperatures of the outdoor roof surface, T(Z4RO), and of the indoor roof surface, T(Z4RI). Figure 6(b) shows the measured and the simulated indoor air temperatures, T(Z4), the ambient temperature, Tamb, is included as a reference. Good agreement can be observed between the measured and the simulated temperatures. Table 4 shows the monthly average and standard deviation of measured and simulated temperatures of the outdoor roof surface, T(Z4RO), the indoor roof surface, T(Z4RI), the outdoor wall surface, T(Z4WO), the indoor wall surface, T(Z4WI), and the air temperature of zones Z1, Z4, Z5, and Z6, for April 2011 with TS. The maximum difference is 1.1 C (4.0%), for the outdoor wall surface T(Z4WO) and the zones with maximum differences are Z1 and Z5 with 0.1 C (0.4%). Figure 7 presents decrement factors (±0.08) and lag times (±5.4 min) calculated from measured and simulated air temperatures for zones Z1, Z4, Z5 and Z6 for April 2011, TS. Figure 7(a) shows that the decrement factors from the measured and simulated temperatures are the same within the standard deviations. Figure 7(b) shows that lag times from measured and simulated temperatures are almost the same for Z1 (in the ground floor) and those for Z4, Z5 and Table 4 Measured and simulated monthly average temperatures and standard deviations, with tree in April 2011 Zone Measured Simulated TS Difference Difference (%) T(Z4RO) 28.8 ± ± T(Z4RI) 29.0 ± ± T(Z4WO) 27.4 ± ± T(Z4WI) 28.9 ± ± T(Z1) 27.4 ± ± T(Z4) 29.5 ± ± T(Z5) 29.0 ± ± T(Z6) 28.7 ± ± Fig. 6 (a) Measured (-M) and simulated (-S) temperatures of outdoor, T(Z4RO), and indoor, T(Z4RI), roof surfaces in Z4; (b) ambient temperature (Tamb), measured (-M) and simulated (-S) air temperatures in Z4, T(Z4). For the week of April 14th- 21st 2011, TS Fig. 7 (a) Decrement factors of measured and simulated temperatures for zones Z1, Z4, Z5 and Z6 and (b) lag times of measured and predicted temperatures for zones Z1, Z4, Z5 and Z6 for April 2011, TS

8 8 Z6 (in the first floor) are overvalued by the simulation. Maximum difference is for Z6, where the simulations overvalue lag time in 46.2 min (3.2%). Figure 8 presents the qualitative comparison between the measured and simulated temperatures for the week of April 6th 13th 2012, WTS. Figure 8(a) shows the measured and simulated temperatures T(Z4RO) and T(Z4RI). Figure 8(b) shows the measured and simulated T(Z4), and Tamb. Also for this case, the agreement is good. Table 5 shows the monthly average and standard deviation of measured and simulated temperatures of the outdoor roof surface, T(Z4RO), the indoor roof surface, T(Z4RI), the outdoor wall surface, T(Z4WO), the indoor wall surface, T(Z4WI), and the air temperatures of zones Z1, Z4, Z5, and Z6, for April 2012, WTS. The maximum difference is 1.3 C (4.4%), for the outdoor wall surface T(Z4WO) and the zone with maximum difference is Z4 with 0.8 C (2.7%). Figure 9 presents the decrement factors (±0.08) and lag times (±5.4 min) calculated from measured and from simulated air temperatures for zones Z1, Z4, Z5 and Z6, for the case WTS in April Figure 9(a) shows that the decrement factors of the measured and simulated air temperatures are the same within the standard deviation. Figure 9(b) shows that lag times of measured and simulated air temperatures are almost the same for Z4 and Z1. The Table 5 Measured and simulated monthly average temperatures and standard deviations, WTS in April 2012 Zone Measured WTS Simulated Difference Difference (%) T(Z4RO) 31.4 ± ± T(Z4RI) 30.2 ± ± T(Z4WO) 29.6 ± ± T(Z4WI) 28.7 ± ± T(Z1) 27.6 ± ± T(Z4) 29.8 ± ± T(Z5) 30.8 ± ± T(Z6) 28.9 ± ± Fig. 8 (a) Measured (-M) and simulated (-S) temperature of outdoor, T(Z4RO), and indoor, T(Z4RI), roof surfaces in Z4; (b) ambient temperature (Tamb), measured (-M) and simulated (-S) air temperature in Z4, T(Z4). For the week of April 6th 13th, 2012, WTS Fig. 9 (a) Decrement factors of measured and simulated temperatures for zones Z1, Z4, Z5 and Z6 and (b) lag times of measured and predicted temperatures for zones Z1, Z4, Z5 and Z6 for April 2012, WTS

9 9 maximum difference is for Z6, simulations under evaluate the lag time in 36.6 min, 2.6%. Thus, measured and simulated averages temperatures and decrement factors are in a very good agreement, for both conditions. The measured and simulated lag times for the case WTS are also in very good agreement; for the case with TS the agreement is very good for the ground floor, but for the first floor zones the agreement is not so good, although differences are less to 48 min (3.3%). The adaptive comfort temperatures calculated were 26.9 C and 26.2 C for April 2011 and April 2012 respectively. Comfort temperatures ranges from 24.9 C to 28.9 C for April 2011 and 24.2 C to 28.2 C for April The numbers of comfort hours measured (-M) and simulated (-S) of zone Z4 for TS (April 2011) and WTS (April 2012) with neighboring buildings are presented in Table 6. From a total of 720 h, the comfort hours between the measured and simulated for TS and WTS were nearly the same; their differences are 6 h and 2 h respectively. For the TS case, 244 h were in comfort, that is 33.9%, while for WTS case only 98 h were in comfort, 13.6%; a difference of 146 hours (20.1%). This indicated that TS contributed with more than 100 h of comfort in zone Z4. 6 Tree shading effect In order to determine the effect of the tree shading, simulations are made for both conditions TS and WTS, considering the neighboring buildings shading (NS), for December 2011 and April Figure 10 presents the qualitative comparison between the temperatures obtained from simulations with TS and WTS, for the week of December 24th 31st 2011 and April 6th 13th Figure 10(a) presents the outdoor and indoor roof surface temperatures of Z4 with TS and WTS for the week of December 24th 31st In this week, outdoor and indoor surface temperatures of the roof with TS and WTS remain almost the same, so there is no effect of TS in December. Figure 10(b) presents the ambient Table 6 Measured and Simulated comfort hours for cases TS and WTS April (2011) April (2012) TS-M TS-S WTS-M WTS-S Comfort (h) Discomfort (h) Total Fig. 10 (a) and (b) Simulated (-S) temperature of outdoor, T(Z4RO), and indoor, T(Z4RI), roof surfaces in Z4 for December 24th-31th, 2011; (c) and (d) ambient temperature (Tamb), and simulated (-S) air temperature of Z4, T(Z4) for April 6th-13th, 2012; with TS and WTS

10 10 temperature, Tamb, and the air temperature of Z4 with TS and WTS for the same week, it shows that Tamb peaks are 1 C lower than the ones of indoor air temperature of TZ4, but air temperatures of TZ4 with TS and WTS are almost the same. Figure 10(c) presents the outdoor and indoor roof surface temperatures of Z4 with TS and WTS for the week of April 6th 13th 2012, the outdoor roof surface temperature reaches a maximum of 63 C and the indoor 34.6 C. Considering the TS case, the maximum outdoor roof surface temperature is 55.9 C and the indoor is 32.0 C, a decrease of 7.1 C and 2.6 C, respectively, compared to the WTS case. Figure 10(d) presents the Tamb and the air temperature of Z4 with TS and WTS for the same week, it shows the effect of the tree shading on the indoor air temperature, the maximum indoor air temperature without tree shading is 33.1 C and minimum of 25.6 C; with tree shading, the maximum indoor air temperature is 31.5 C and minimum of 24.6 C, the maximum and minimum air temperatures decreased 1.6 C and 1.0 C, respectively. Thus the comparison of the roof and air temperatures with TS and WTS in December shows that the tree shading in this cold week has almost no effect in zone 4. In the cold week, the indoor air temperature remains comfortable. Table 7 shows the monthly average and standard deviation of simulated temperatures for the cases TS and WTS for April and December 2011 and April As expected, all average temperatures are larger in the WTS case, compared to the corresponding value of the TS case. Differences are greater for April (warmest month) than for December (the coldest month). For both months, the maximum difference for air temperature is for zone 5, which is in the first floor and closer to the tree. The maximum difference between the warmest month and the coldest month is also for the air temperature of zone 5. As indoor air temperatures in the coldest month are in the comfort range for the TS case, it is concluded that evergreen trees are adequate for this climate. Figure 11 shows the decrement factors (±0.09) and lag times (±5.4 min) of zones Z1, Z4, Z5, and Z6 for the cases TS and WTS for April and December Figure 11(a) shows decrement factors with TS and WTS are always larger in December than the ones in April. With TS decrement factors are always lower than WTS. Largest decrement factor is in Z5 above 0.62, WTS in December. In April 2011, the decrement factors of zones Z1, Z4, and Z6 for both cases, TS and WTS, are almost the same. For zone Z5, that received more shadow from the tree, the decrement factor with TS is smaller than the case WTS; the difference is Figure 11(b) shows that the effect of the TS on the lag time of a zone depends on the month and the hours of the day when the tree shaded the zone. In April 2011, lag time of Z5 for TS is almost the same of that of WTS; however lag times of the other zones for TS are lower than those of WTS, maximum difference of 36 min is for Z4. In December, the lag times of the zones are always lower than the ones in April; lag times of Z5 and Z6 for TS and WTS are almost the same, however lag times of Z1 and Z4 for TS are slightly lower compared to WTS, the higher lag times are for zones Z6 and Z1 (ground floor) and the lower is for Z5, as expected. The adaptive comfort temperatures calculated for December 2011 were 23.9 C and the comfort temperature range were 21.9 C to 25.9 C. For April 2011 and 2012 the comfort temperature ranges were the same as in the previous section. Table 8 presents the number of comfort hours in zone Z4 for December 2011 and April 2012, for TS and WTS with neighboring buildings; and for April 2012 for NS and WNS. For December 2011, for TS and WTS, nearly 52% of the 744 h were within the comfort temperature; however, the TS contribution to the comfort hours is minimal (4 h). For April 2012, the TS case, 360 h (50%) were within the comfort range, but for WTS case, only 100 h (13.9%) were in the comfort zone. Thus, the TS contributed 260 h of comfort for zone Z4. Table 7 Simulated monthly average temperatures and standard deviations, for the cases TS and WTS in April and December, 2011 and April 2012 Zone TS (Apr) TS (Dec) WTS (Dec) Diff. TSApr TSDec Diff. (WTS TS)Dec TS (Apr) WTS (Apr) T(Z4RO) 29.2 ± ± ± ± ± T(Z4RI) 28.8 ± ± ± ± ± T(Z4WO) 28.5 ± ± ± ± ± T(Z4WI) 29.2 ± ± ± ± ± T(Z1) 27.3 ± ± ± ± ± T(Z4) 29.5 ± ± ± ± ± T(Z5) 29.1 ± ± ± ± ± T(Z6) 28.7 ± ± ± ± ± Diff.

11 11 Fig. 11 (a) Decrement factors and (b) lag times for zones Z1, Z4, Z5 and Z6, with TS and WTS for April 2011 and December 2011 Table 8 Simulated comfort hours for cases TS, WTS, NS and WNS December (2011) April (2012) April (2012) TS-S WTS-S TS-S WTS-S NS-S WNS-S Comfort (h) Discomfort (h) Total Neighboring buildings shading effect In order to determine the effect of the shading by neighboring buildings on the indoor temperatures, simulations were made with and without neighboring buildings shading, both cases without tree shading (WTS), using the weather conditions of April Figure 12(a) presents the simulated outdoor wall surface temperature, T(Z4WO), that of the indoor wall surface, T(Z4WI), with neighboring buildings shading (NS) and without neighboring buildings shading (WNS), for the week of April 6th 13th For the WNS case, the maximum outdoor wall surface temperature is 41.2 C and that of the indoor wall surface is 34.9 C. For the NS case, the maximum outdoor wall surface temperature is 39.3 C and that of the indoor surface is 33.6 C; their differences are Fig. 12 (a) Simulated (-S) outdoor wall surface temperature T(Z4WO) and indoor wall surface temperature T(Z4WI) of the bedroom (Z4), and (b) ambient temperature (Tamb) and simulated (-S) air temperature of Z4, T(Z4), for the cases with neighboring buildings (NS) and without neighboring buildings (WNS), for April 6th 13th C (4.6%) for outdoor surface and 1.3 C (3.7%) for indoor surface, respectively. Figure 12(b) shows the effect of the neighboring buildings on the indoor air temperature of Z4. Without neighboring buildings the maximum indoor air temperature is 35.5 C and the minimum is 26.7 C. With neighboring buildings, maximum indoor air temperature is 33.1 C and minimum is 25.6 C. The reduction of the maximum indoor air temperature due to neighboring buildings shading is 2.4 C. Table 9 shows the monthly average and standard deviation of simulated temperatures for the cases NS and WNS for April As expected, all average temperatures are larger for the case WNS; except for the zone Z5 that have no change. Among surface temperatures, the maximum difference, 0.8 C, is for the indoor wall surface of Z4. The zone with greater influence of the NS building is Z6, with a reduction in air temperature of 2.3 C. Figure 13 shows the decrement factors and lag times for the air temperature of zones Z1, Z4, Z5, and Z6 for the cases with NS and WNS shading. Figure 13(a) shows that NS reduces the decrement factor for all zones, maximum

12 12 Table 9 Simulated monthly average temperatures and standard deviations, for the cases NS and WNS April 2012 Zone NS WNS Difference Difference (%) T(Z4RO) 32.0 ± ± T(Z4RI) 29.9 ± ± T(Z4WO) 28.3 ± ± T(Z4WI) 28.4 ± ± T(Z1) 27.0 ± ± T(Z4) 29.0 ± ± T(Z5) 30.0 ± ± T(Z6) 28.2 ± ± Fig. 13 (a) Decrement factors of air temperatures for zones Z1, Z4, Z5 and Z6 and (b) lag times of air temperatures for zones Z1, Z4, Z5 and Z6 with and without neighboring buildings, for April 2012 reduction is 0.14 in Z5, and the minimum is 0.08 in Z1. As it can be seen in Fig. 13(b), NS slightly increases the lag time for all zones, except for Z4, the maximum difference, 23.4 min, is for Z6. Regarding to the number of hours within the comfort temperatures with NS and WNS, Table 8 shows that when WNS are considered, 10.4% of the time (75 h) are within the comfort; however when NS are considered, 13.9% of the time are within the comfort temperatures; indicating that the NS contributed only 3.5% (25 h). 8 Conclusions As far as the authors know, this is the first study on the effect of tree shading and that of the neighboring buildings shading on the thermal performance of a non air conditioned building. The study has been performed in an unoccupied house in a warm sub humid climate using dynamic thermal simulations. Previous studies have evaluated the effect on air conditioned buildings. Experimental data were used to validate the dynamic thermal simulations. The comparison is made for two conditions with tree shading and without tree shading, for these simulations the neighboring buildings shading was considered. Measured and simulated temperatures and decrement factors are in a very good agreement for both conditions, differences are up to 1.3 C (4.4%) and 0.01 C (4.3%), respectively. The measured and simulated lag times for the case without tree shading are also in very good agreement; differences are up to 13.8 min (1.0%). For the case with tree shading, the agreement is also very good for the ground floor, but for the first floor zones the agreement is not so good, although differences are less to 48 min (3.3%). Although the great difficult to properly simulate the actual effect of a tree, these results are good compared with the greater difference reported in the only study that compares experimental and simulation results for the energy saving on cooling loads due to the tree shading (Akbari et al. 1997). The main effect of the tree shading in the warmest month is the reduction of the indoor ambient air temperature and the envelope surface temperatures of all house zones. The average air temperature reduction for the different zones goes from 0.5 C to 3.0 C. A reduction of 3.0 C in the zone Z5 can be significant for the thermal comfort of the occupants. The effect of the tree shading is greater for April (warmest month) than for December (the coldest month). As indoor air temperatures in the coldest month are in the comfort range for the tree shading case, it is concluded that evergreen trees are adequate for this climate. The neighboring buildings shading decreases the temperatures and the decrement factors, and slightly increases the lag times in almost all zones. The average air temperature reduction for the different zones goes from 0.0 C to 2.3 C. Thus, as Farrar-Nagy et al. (2000) have concluded for an air conditioned house, it is important to consider the neighboring buildings shading in thermal simulations. The periods of adaptive comfort temperatures indicated that for the warmest month, tree shading is important as it contributes with 50% of the time with comfort temperatures; however in the coolest month, there is no difference on

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