Impact of Shading on Occupant Comfort and Building ...

1

Impact of Shading on Occupant Comfort and Building Energy Demand Kelly Kalvelagea,*, Ulrike Passeb, Michael Dorneichc a

Human Computer Interaction, Center for Building Energy Research, Iowa State University, Ames, Iowa 50011, United States, [email protected] b

Department of Architecture, Center for Building Energy Research, Iowa State University, Ames, Iowa 50011, United States, [email protected]

c

Department of Industrial Manufacturing and Systems Engineering, Iowa state University, Ames, Iowa 50011, United States, [email protected] *Corresponding author: Tel.: 1 515 720 9636

Abstract This paper examines an adaptation shading strategy and its impact on building energy demand and occupant thermal and visual comforts in the future climate. The unpredictability of exterior conditions, including global climate, the surrounding built environment, and pedestrian activity, makes it difficult to account for changing microclimate conditions. Therefore, it is becoming increasingly important to examine mitigation and adaptation strategies focused on reducing the solar impact on buildings to improve occupant thermal and visual comforts as well as reducing building energy demand. Using the canyon air temperature (CAT) microclimate model and EnergyPlus whole building simulation software, a medium office building located downtown Phoenix, Arizona USA was examined as an example test case. Results indicate that more research in the relationship between energy demand, thermal comfort models, and visual comfort is required to ensure high building performance and high human comfort and satisfaction are achieved. Keywords: Future typical meteorological year, Thermal comfort, Visual comfort, Energy demand, Shading strategy, Canyon air temperature model 1

Introduction This paper examines an interior adaptation shading strategy and its impact on building energy

demand and occupant thermal and visual comforts in a future typical meteorological year (FTMY) [1] climate through simulation. In the same way terrain alters global climate to produce regional climates, local urban geometry, activities, people, and vegetation impact a building’s microclimate conditions [2,3]. Figure 1 outlines this coupling that influences microclimate conditions. Buildings need to be designed to operate in both current and FTMY climates to ensure occupant comfort and high building performance [46]. The uncertain future climate, when coupled with unregulated urban built environment conditions, creates operational challenges for buildings in the future.

2

Global Climate

Regional Climate

Terrain

Urban Geometry

Microclimate

Figure 1. Climate Spatial Scales. The global scale is altered with terrain to produce the regional scale climate. Regional climate is altered by urban geometry to produce the micro scale climate for a building site.

Designers need to know how a building’s microclimate will change, and not just the average global temperature increase [7]. Previous research has shown that a microclimate impacts an urban building differently than a rural building [8]. Urban physics influence wind patterns [9]; geometry shades neighboring buildings [10]; facade materials reflect or absorb solar radiation [11,12]. These impacts influence building energy demand and contribute to human comfort, sometimes negatively, such as when surface reflections create glare [13,14]. Urban physic impacts are typically evaluated in terms of building energy demand [15-17] and thermal comfort [18-20]. However, human comfort parameters are based on more than just air temperature, and include such things as visual, thermal, and acoustical comfort [21]. Visual comfort originates from exterior conditions, and changes in the exterior conditions greatly affect interior conditions. Visual discomfort can also be associated with direct hazards, such as impeding vision in a stairway [22]. Therefore, to ensure satisfied occupants in the future microclimate and to satisfy the purpose behind having a building, thermal and visual comfort must be considered [23-24]. But because climate changes (global, regional and micro) are largely uncertain, it is necessary to incorporate features that can account for unknown future conditions. 1.1 Mitigation and Adaptation Strategies Since the future climate cannot be validated because there are no replicable events, we must rely upon average conditions to design buildings to perform into the future [25,26]. To reduce the magnitude and the variable effects of global and microclimate changes, mitigation strategies have been deployed as an attempt to improve building energy demand and comfort conditions [27,28]. Mitigation is risk avoidance and aims to reduce greenhouse gases from the first introduction of the strategy. Mitigation strategies include high albedo surfaces [14], vegetation [29], and shading devices [30]. While research has indicated benefits of each mitigation strategy, mitigation efforts alone cannot account for extreme weather events, nor can they adequately account for a variety of conditions. Therefore, mitigation strategies should be coupled with adaptation strategies to lessen the impacts of extreme and variable weather events [31]. Adaptation is the adjustment of the built environment in response to actual or expected climatic events [27]. It is important for the strategy to include an adaptable component so existing buildings and mitigation strategies can adapt to future changing climate conditions. Adaptation and mitigation strategies are not a one-size-fits-all solution, however. The microclimate should be examined to provide the most appropriate strategy(s) and should be focused on improving human comfort, and not just reducing energy demand.

3 1.2 Climate specific strategies Phoenix, Arizona was selected for this study. Phoenix is located in climate zone 2B [32] and is characterized by mild winters coupled with hot and dry summers, leading to elevated nighttime temperatures [33] and elevated surfaces temperatures during the day [34]. In hot, arid regions with predominately cooling requirements, research has indicated thermal comfort may be the limiting factor to achieve high occupant satisfaction and reduced building energy demand rather than scarce water resources [21]. High albedo building surfaces, such as cool roofs, prevent overheating in buildings and decrease peak electricity demand by reflecting solar radiation and reducing sol-air temperature of building surfaces [13]. However, high albedo surfaces, such as reflective pavement, increase solar reflections, altering building energy exchanges with the urban canyon, contributing to increased energy demand [35,36]. Additionally, high albedo façade materials, such as mirror glazing, reflect solar radiation, causing unpleasant glare for street occupants and neighboring building occupants [37]. Also, to reduce localized solar absorption, all surfaces are required to be light colored [21]. Vegetation has been proven successful as a cooling strategy in arid climates by reducing air temperature through evapotranspiration cooling and shading surfaces [29]. However, surface shading is only effective if the vegetation is within close proximity of the surface to be shaded, in order to prevent the absorption of solar radiation during all hours of the day [38]. Additionally, urban form has a greater impact on daytime temperatures than landscaping [middle]. Shading devices prevent overheating in buildings by limiting excess solar heat gain in response to a seasonal change in solar altitude. Well-designed sun control reduces solar heat gain in summer and permits solar energy to enter the building in the winter [8]. Exterior devices function to reduce solar heat gains from reaching building surfaces [8]. However, they have limited ability to adapt to the changing climate or other unpleasant conditions that could arise from neighboring building surfaces. Interior devices reduce solar heat gains while improving daylighting quality and control glare [39]. While interior devices do have less impact on building cooling than exterior devices (because interior devices allow solar radiation into the building and absorb it), they have been shown to decrease energy demand and improve thermal conditions more than with no device at all [40]. Additionally, interior shading devices are cheap, easy to access, and can be automatic or manually controlled. They also provide occupants control of their space and can more readily adapt to a variety of changing conditions [41]. This study examines an interior roll shading adaptation strategy to mitigate future microclimate conditions. The purpose is to evaluate the introduction of a shading device to reduce building energy demand and improve occupant thermal and visual comforts. Results indicate evaluation in terms of set points being met, building energy demand, predicted mean vote (PMV), predicted percentage dissatisfied (PPD), adaptive comfort, and shading hours are required to ensure high building performance and human comfort and satisfaction are achieved.

4 2

Method As previously mentioned, an urban building is impacted differently by microclimate conditions than the

same building affected by only global conditions. To adequately examine the impact of the future climate on an urban building and human comforts, the process outlined in Figure 2 was followed.

TMY3

Regional Weather Data

FTMY

Material Properties Moisture Availability

Canyon Air Temperature (CAT) Model

Energy Demand

Microclimate Weather Data

Site Geometry

Anthropogenic Heat

EnergyPlus

Thermal Comfort

Shade Properties Medium Office Building Typology Shade Schedule

Visual Comfort

Figure 2. Simulation Process. FTMY and TMY3 weather data is converted through a microclimate model along with material properties, moisture availability, site geometry and anthropogenic heat to produce a site specific microclimate. The microclimate weather data is combined with a medium office building typology and run through EnergyPlus. The output results of energy demand, thermal comfort and visual comfort were evaluated.

2.1 Weather Data This study uses constructed future typical meteorological year (FTMY) data for Phoenix, Arizona created using the method described by Patton [1] and analyzed by Kalvelage, Passe, Rabideau, and Takle [42]. This method evaluates TMY3 data [43] recorded at the Phoenix Sky Harbor International Airport for total sky cover, dry-bulb temperature, dew-point temperature, relative humidity, absolute humidity, pressure, and wind speed. Through a combination of regional climate models (RCMs) and global climate models (GCMs), three future climate scenarios were generated, representing low, moderate, and high change scenarios for Phoenix. Table 1 shows the RCM and GCM combinations. Table 1. FTMY Climate Model Scenario Combinations. The combination of regional a and global b climate models that represent the low, moderate, and high change scenarios for Phoenix, Arizona. Low Change

Moderate Change

High Change

MM5I_CCSM

RCM3_GFDL

CRCM_CCSM

a

Regional climate models: CRCM-Canadian Regional Climate Model. MM5I-MM5-PSU/NCAR Mesoscale Model. RCM3-Regional Climate Model, version 3. b Global climate models: CCSM-Community Climate System Model. GFDL-Geophysical Fluid Dynamics Laboratory GCM.

5 2.2 Microclimate model To generate the microclimate conditions for downtown Phoenix, the canyon air temperature (CAT) model created by Erell and Williamson [44] was selected for a number of reasons. First, the timeseries meteorological parameters are those measured at standard weather stations. Second, the model accounts for urban geometry and materials on radiant exchange, air flow, stored energy, and sensible heat fluxes. Lastly, the model works best in areas with homogeneous building density, topology, and ground cover, all of which describe downtown Phoenix. While the model falls short regarding moisture content influences, downtown Phoenix is predominately paved surfaces and lacks influence from large vegetated areas [21] and large bodies of water in the same way as the CAT model calibration city of Adelaide. The input data for the CAT model includes weather data and site specific information regarding building heights, street width, albedo and thermal surface properties, moisture availability and anthropogenic heat sources. Each of the three FTMY change scenarios and the TMY3 datasets were modeled. Site information was extracted from Burian, Velugubantla, and Brown [45] that summarizes the built environment conditions representative of downtown Phoenix including land use, land cover, building density and use, and street width. 2.3 Building Typology Commercial buildings represent just under one-fifth of U.S. energy consumption, with office space, retail space, and educational facilities representing about half of commercial sector energy consumption [46]. The Department of Energy (DOE) commercial reference building typologies [47] were evaluated against the morphological characteristics defined by Burian et al. [45] for an area centered on downtown Phoenix. Existing building typologies are representative of the 1980’s to 2000’s building construction for Phoenix and standards that do not meet the ASHRAE [48] energy standard [49]. Building parameters include area, height, and use. Additionally, understanding offices represent 17% of total commercial floor space and 19% of the primary energy consumption [46], the existing construction medium office building was selected. Detailed construction information for the medium office building typology is displayed in Table 2. The medium office building typology was modified from the default version using the variables shown in Table 3 to create two building sub-types, called “no shade” (NS) and “shade” (S). The difference between the two sub-types is the method of shade activation. As previous research as indicated, occupants will interact with shades based on visual and thermal comfort [39,50]. Since glare has a major impact on visual comfort [51], the maximum allowable discomfort glare index (DGI) was used to determine visual comfort conditions. The maximum allowable DGI is defined as when the presence of glare would be expected to affect the efficiency and performance of a given task [22]. Therefore, in the shaded sub-type, the shade was activated (in the down position) when either the maximum allowable discomfort glare index exceeded 22 (recommended value for offices) during all occupied hours or the

6 solar incidence on the window’s exterior surface exceeded 95 W/m2 during cooling hours only. In the nonshaded sub-type, the shade activation was set to always off (or always in the up position). Table 2. Medium Office Building Typology Specifications. Design, construction, and equipment parameters for the DOE commercial reference medium office building typology [49]. Category Variable Value Design

Construction

Equipment

Area

4,982 m2 (53,628 ft2)

Aspect Ratio (height to width)

1.5

Floors

3 stories

Floor to Floor Height

3.96 m (13 ft.)

Floor to Ceiling Height

2.74 m (9 ft.)

Glazing Fraction

0.33

Roof

Insulation entirely above deck

Walls

Steel Frame

Window Solar Heat Gain Coefficient

0.25

Heating

Furnace

Cooling

Packaged air conditioning unit (PACU)

Air Distribution

Multi-zone variable air volume (MZ VAV)

Schedule

Starting point ASHRAE 1989 [49]

Table 3. Simulation Variables. The modified default variables for the medium office building sub-types no shade and shade. Category

Variable

Shade properties

Fabric

Semi-Open Weave Fabric

Shade Openness

10%

Solar Transmission

0.16

Daylight Controls

Value

Solar Reflectance

0.35

Visual Transmission

0.18

Visual Reflectance

0.35

Infrared Hemispherical Emissivity

0.84

Infrared Transmission

0.11

Thickness

.000381 m

Conductivity

0.9 W/m-K

Daylight Lux

50,000 lux

Fraction of Zone Controlled by First Reference Point

1

Reference Point Location

X: 1.5 m Y: 1.5 m Z: 0.8 m

Solar incidence

95 W/m

Discomfort Glare Index (DGI)

22 (default)

Glare Index Angle

15

Shade activation

Solar incidence exceeded during

2

o

cooling hours or DGI exceeded during all occupied hours

7 To activate glare control, daylighting controls were required to be introduced in each zone. Since daylighting is outside the scope of this study, the daylight lux level was set at an extremely high level of 50,000 lux to force lights to remain on during building operation. While this study does not distinguish between solar incidence and glare for shade activation, it is assumed that increased levels of solar incidence would produce uncomfortable visual conditions either through interior objects or from a building’s microclimate. Additionally, glare is assumed to be diffuse and to affect all occupants equally. While previous research has identified that occupants tend to set and forget [39], this study does not account for occupants leaving the shade down or up as occupant preference will vary and as a means to provide a control measure. 2.4 Whole Building Simulation Parallel building calculations were conducted using EnergyPlus [52]. A combination of both sub-types of the medium office building typology with the TMY3, CAT modified TMY3, and CAT modified FTMY weather files were used. The simulation combinations are shown in Table 4. For simulations 5 and 6, each of the three change scenarios (low, moderate, and high) were averaged for simplification of result presentation. Table 4. Simulation. The weather file and building typology pairing for each simulation. Data sources: TMY3 [43] and FTMY [1]. Simulation Number - Name

Weather File

Building Sub-Type

1 – TMY_NS

TMY3

No Shade

2 – TMY_S

TMY3

Shade

3 – CAT_TMY_NS

CAT modified TMY3

No Shade

4 – CAT_TMY_S

CAT modified TMY3

Shade

5 – CAT_FTMY_NS

CAT modified FTMY Low Change

No Shade

CAT modified FTMY Moderate Change CAT modified FTMY High Change 6 – CAT_FTMY_S

CAT modified FTMY Low Change

Shade

CAT modified FTMY Moderate Change CAT modified FTMY High Change

3

Results and Discussion The results of this investigation indicate the importance of examining building energy demand and

human comforts under future climate predictions (FTMY) and the impact of urban effects (CAT). Results are presented for building energy demand (heating, cooling, and total demand), peak energy demand (heating and cooling), set points being met (heating and cooling), PMV, PPD, adaptive comfort, and number of shading hours.

8 3.1 Energy demand Figure 3 shows the CAT_TMY_NS, CAT_TMY_S, CAT_FTMY_NS, and CAT_FTMY_S results as an increase or decrease in energy demand when compared to the TMY_NS. Consistent with previous research, energy demand results show a slight decrease in heating energy demand and significant increase in cooling energy demand for both the urbanized and predicted future climate [17,42,53]. This change results in an overall increase in total energy demand for the Phoenix downtown area. Comparing CAT_TMY to TMY indicates that urban physics will further increase the global climate’s impact on building energy demand. Additionally, examining future urban physics (CAT_FTMY) will increase significantly more over the TMY and CAT_TMY which indicates if the TMY data is used to design buildings for the future Phoenix microclimate, a significant and unwanted increase in energy demand will result. Examining shaded and non-shaded sub-types indicates that in both the CAT_TMY and CAT_FTMY conditions, the shaded sub-type indicates cooling energy demand is increasing approximately 14% less than the nonshaded sub-type. The non-shaded sub-type shows a greater decrease in heating energy demand. This indicates the shading device was activated during the winter hours due to glare control rather than intense solar incidence. Striking a balance between allowing winter solar radiation to penetrate the building while controlling glare would be beneficial in Phoenix. 120 100

Energy (MWh)

80 60

CAT_TMY_NS

40

CAT_TMY_S

20

CAT_FTMY_NS CAT_FTMY_S

0 ‐20 ‐40 Heating

Cooling

Total

Figure 3. Heating, Cooling and Total Building Energy Demand (MWh). All simulations when compared to the TMY3 (NS) show a decrease in heating energy demand and an increase in cooling energy demand produces an overall increase in building energy demand for Phoenix for both the non-shaded (NS) and shaded (S) building sub-types.

Peak energy demand is used in the sizing of mechanical equipment. If the equipment is too small, it will not be able to overcome both the internal and external loads to produce the desired interior comfort conditions. Over-sizing causes the equipment to turn on and off more frequently, creating inefficiencies in the system and shortening equipment life. Figure 4 shows the peak heating demand decreasing for the CAT and FTMY climates with the non-shaded sub-type peak demands lower than the shaded. These results indicate that the collection of solar radiation during the day aids in reducing heating demand peaks.

9 Peak cooling demand, shown in Figure 5, indicates the peak cooling demand increasing with the shaded sub-type peak lower than the corresponding non-shaded sub-type. However, examining the CAT_TMY_S compared to the TMY_NS shows a decrease in the peak cooling demand while the CAT_TMY_NS increases. This indicates an interior shade can be successful in reducing solar gains and control glare causes by urban physics in the current climate as well as in the FTMY climate. 250

Energy (KWh)

200

150

100

50

0 TMY_NS

CAT_TMY_NS

CAT_TMY_S

CAT_FTMY_NS

CAT_FTMY_S

Figure 4. Peak Heating Energy Demand (KWh). Non-shaded sub-types have a lower heating demand peak than shaded sub-types. In both sub-types, the heating peak is decreasing.

250

Energy (KWh)

200

150

100

50

0 TMY_NS

CAT_TMY_NS

CAT_TMY_S

CAT_FTMY_NS

CAT_FTMY_S

Figure 5. Peak Cooling Energy Demand (KWh). Non-shaded sub-types have a higher cooling demand peak than shaded sub-types. In both sub-types, the cooling peak is increasing.

The heating and cooling set points are designed to be within an acceptable range of conventional human thermal comfort according to the ASHRAE standard 55-2004 during the schedule’s defined occupied time. Data generated, therefore, reflects the hours the set point is not met, which is defined as not being within 0.31oC (0.56oF) of the set point temperature. The results for the number of occupied hours the set point is not met for both heating and cooling are shown in Figure 6. The results are

10 displayed as an increase or decrease in the number of hours the set point is not being met in comparison with TMY_NS. 250

Time (Hours)

200 150

CAT_TMY_NS CAT_TMY_S

100

CAT_FTMY_NS CAT_FTMY_S

50 0 Heating

Cooling

‐50 Figure 6. Time (Hours) Set Point Not Met During Occupied Hours. The number of hours the set point is not within o o 0.31 C (0.56 F) for heating and cooling displayed as an increase or decrease when compare to TMY_NS. While the heating set points are indicated to trend toward being met, the number of cooling hours the set point not being met is indicated to substantially increase in the future microclimate.

The heating set point, while already being frequently met, is decreasing in both the urbanized (CAT) and future (FTMY) scenarios. Understanding that the heating set point is being met more frequently and the heating peak load is decreasing, it can be inferred current furnace equipment sizing should be adequate for future microclimate conditions. Additionally, the corresponding shaded and non-shaded subtypes, for both CAT_TMY and CAT_FTMY, have relatively the same number of hours the heating set point is not met, which can indicate the hours result from the unit recovering from overnight setback temperatures rather than the inability of equipment to maintain conditions further supporting the notion of adequate equipment sizing. The cooling occupied set point remains unmet for all simulations with a dramatic increase in hours for the CAT_FTMY. In the CAT_TMY, the shaded sub-type has slightly fewer hours the set point is not being met than the non-shaded sub-type. This indicates solar gains influence the increase in hours the set point is not being met, but do not determine it. The CAT_FTMY shaded sub-type follows the same trend. However, the difference between shaded and non-shaded sub-types is greater than that of the CAT_TMY shaded and non-shaded difference. This indicates that the shading device will have a greater impact in achieving cooling set points in the future microclimate. While the shading device does aid in the set point hours being achieved, the numbers are still climbing at a staggering rate, and therefore, further examination of solar incidence on all building surfaces should be considered. Results from energy demand, peak energy demand and set points not being met indicate the shading device can have a positive impact on energy demand in both urbanized and future climates. However, only examining one of these aspects, such as the set points not being met, can indicate otherwise.

11 Furthermore, examining only the impact of the future microclimate on building energy demand can provide little insight into the human comfort conditions. 3.2 Thermal Comfort The predicted mean vote (PMV) can be used to understand occupant thermal sensations. The recommended limits for the PMV are -0.5 < PMV < +0.5. The results shown in Figure 7 indicate all simulations are tolerable and on the slightly cool side. The results indicate a trend towards zero, or thermal neutrality, with the non-shaded sub-types approaching more quickly than the shaded sub-types. The shaded sub-types being on the cooler side could be from the shade blocking solar radiation in the winter months. Additionally, the CAT_FTMY conditions are indicated to be more comfortable than both the CAT_TMY and TMY. The thermal sensation being on the cool side while the number of hours the cooling set point not being met could indicate a slightly warmer set point could improve interior conditions while decreasing energy demand.

0

TMY_NS

CAT_TMY_NS

CAT_TMY_S

CAT_FTMY_NS

CAT_FTMY_S

PMV

‐0.1

‐0.2

‐0.3

Figure 7. Predicted Mean Vote (PMV). Occupant thermal comfort is indicated to trend toward thermal neutrality from slightly cool.

A second model to evaluate thermal comfort is the predicted percentage of dissatisfied (PPD) occupants. The PPD is useful to understand the satisfaction level of the occupants rather than just thermal sensation. ASHRAE standard 55-2010 requires that at least 80% of the occupants are satisfied. Figure 8 displays the PPD results for each simulation. Each result is within the 20% dissatisfied range with the trend moving towards zero. The non-shaded sub-types have fewer people dissatisfied; however, the number of occupants dissatisfied remains is relatively the same across all simulations. This indicates a revision in what is considered satisfied should be considered in the way of warmer temperatures.

12

12

Percentage Disatisfied

10 8 6 4 2 0 TMY_NS

CAT_TMY_NS

CAT_TMY_S

CAT_FTMY_NS

CAT_FTMY_S

Figure 8. Predicted Percentage Dissatisfied (PPD). The number of occupants predicted to be dissatisfied is indicated to decrease. However, the introduction of the shading device appears to yield slightly more dissatisfied than the corresponding non-shaded sub-type.

A third model to evaluate thermal comfort is an adaptive model. An adaptive model is based on the idea that outdoor climate influences indoor comfort because humans can adapt to different temperatures during different times of the year. Figure 9 shows the hours considered uncomfortable or the occupied hours not meeting adaptive comfort limits relevant for each person. The results indicate a significant departure in the number of hours considered uncomfortable between non-shaded and shaded sub-types. While the CAT_TMY_NS decreases slightly in the number of hours uncomfortable, the CAT_FTMY_NS hours increase slightly higher than the TMY_NS levels resulting in an upward trend for the non-shaded sub-type in the future microclimate. The shaded sub-types, however, show a significant decrease in the number of hours considered uncomfortable with the CAT_FTMY_S hours having the fewest. 1200

Time (Hours)

1000 800 600 400 200 0

TMY_NS

CAT_TMY_NS

CAT_TMY_S

CAT_FTMY_NS

CAT_FTMY_S

Figure 9. Adaptive Comfort - Time (Hours) Considered Uncomfortable. The number of occupied hours considered uncomfortable out of 8,750 hours is indicated to decrease significantly with the introduction of a simple roll shade.

13 Using the adaptive model, other thermal parameters can be modified to achieve comfort. The low humidity levels in Phoenix allow for a slightly elevated air temperature, which could explain for the improved conditions across models, but between shaded and non-shaded sub-types. This indicates the shade not only prevents solar radiation from increasing energy demand, but also prevents unwanted solar radiation from causing the occupants to feel warm. The adaptive model also assumes humans in a certain climate will adapt to those conditions. This means that humans in Phoenix will tolerate warmer conditions than someone in Minneapolis, Minnesota. Therefore, while the results of this study indicate improved comfort conditions for an office, these results may not be indicative of more transient building typologies, such as transportation hubs, in Phoenix. 3.3 Visual Comfort For this study, visual comfort is evaluated through the deployment of the shading device. It is assumed the deployment of the shading device improves visual conditions (i.e. glare control). The shade was activated if the maximum DGI was exceeded during all occupied hours or the solar incidence on the window exterior surface exceeded during building cooling hours. The number of hours the shade is closed can indicate the solar impact in the future microclimate. Figure 10 shows the average number of hours the shade is activated over the course of a year’s 8760 hours. The TMY_S scenario has the fewest number of hours the shade is activated and the CAT_FTMY_S scenario having the most. The increasing number of shade activated hours indicates solar incidence intensity and duration or glare probabilities will increase due to the microclimate and continue to increase into the future microclimate. 4000

Time (Hours)

3955

3900 3888

3818 3800

TMY_S

CAT_TMY_S

CAT_FTMY_S

Figure 10. Hours Shade Activated. The number of hours the shade is activated is increasing which indicates an increase in solar incidence on the building surfaces.

4

Conclusion This paper examined an adaptive interior roll shading strategy for a medium office building in

Phoenix, Arizona through a combination of future climate predictions (FTMY), CAT model, and EnergyPlus. The strategy was evaluated for its impact on building energy demand (heating, cooling, and total demand), hours set points not met (heating and cooling), peak energy demand (heating and cooling), PMV, PPD, adaptive thermal comfort, and shade activation hours. The results indicate it is

14 important to evaluate the impact of future microclimate adaptation strategies on more than just energy demand. While an interior shading device has little impact on building energy demand when compared to the magnitude of change between TMY, CAT_TMY and CAT_FTMY alone, the device has positive implications relating to human comfort. Additionally, in terms of evaluating human comforts, examining more than one comfort model is recommended. In the case of thermal comfort, the PMV and PPD can be used to target thermal sensations and satisfaction while the adaptive model can provide increased flexibility for building design and operations across differing climates. Visual comfort should be evaluated in both daylit and non-daylit buildings regarding the impact of the future microclimate. The future climate and the microclimate are uncertain and ever changing. Therefore, it is important to provide occupants control of the spaces they occupy. While this study assumes the shading device will be activated solely based on solar incidence and glare and then deactivated when the stimulus has been removed, they provide occupants control, and they contributes to a reduction in energy demand and increased human comfort. Acknowledgments We wish to thank Evyatar Erell for the guidance and use of the canyon air temperature (CAT) microclimate model. Specific funding to conduct this work and foster collaboration with Evyatar has been provided by the Institute for Physical Research and Technology (IPRT). References [1] Patton, S. L. (2013). Development of a future typical meteorlogical year with application to building energy use. (Master of Science Thesis), Iowa State University, Ames, Iowa. (Paper 13635) [2] Dimoudi, A., Kantzioura, A., Zoras, S., Pallas, C., & Kosmopoulos, P. (2013). Investigation of urban microclimate parameters in an urban center. Energy and Buildings, 64(0), 1-9. doi: http://dx.doi.org/10.1016/j.enbuild.2013.04.014 [3] Grimmond, C. S. B., Blackett, M., Best, M. J., Barlow, J., Baik, J. J., Belcher, S. E., . . . Zhang, N. (2010). The International Urban Energy Balance Models Comparison Project: First Results from Phase 1. Journal of Applied Meteorology and Climatology, 49(6), 1268-1292. doi: 10.1175/2010JAMC2354.1 [4] de Wilde, P., & Coley, D. (2012). The implications of a changing climate for buildings. Building and Environment, 55(0), 1-7. doi: http://dx.doi.org/10.1016/j.buildenv.2012.03.014 [5] Georgescu, M., Morefield, P. E., Bierwagen, B. G., & Weaver, C. P. (2014). Urban adaptation can roll back warming of emerging megapolitan regions. Proceedings of the National Academy of Sciences. doi: 10.1073/pnas.1322280111 [6] Shaviv, E. (1984). Climate and building design - tradition, research and design tools. Energy and Buildings, 7(1), 55-69. doi: http://dx.doi.org/10.1016/0378-7788(84)90045-8 [7] Schiermeier, Q. (2010). The real holes in climate science. Nature, 463(7279), 284-287. doi: 10.1038/463284a [8] Erell, E., Pearlmutter, D., & Williamson, T. (2011). Urban microclimate: Designing the spaces between buildings. London: Earthscan. [9] Moonen, P., Defraeye, T., Dorer, V., Blocken, B., & Carmeliet, J. (2012). Urban Physics: Effect of the micro-climate on comfort, health and energy demand. Frontiers of Architectural Research, 1(3), 197-228. doi: http://dx.doi.org/10.1016/j.foar.2012.05.002 [10] Middel, A., Häb, K., Brazel, A. J., Martin, C. A., & Guhathakurta, S. (2014). Impact of urban form and design on mid-afternoon microclimate in Phoenix Local Climate Zones. Landscape and Urban Planning, 122(0), 16-28. doi: http://dx.doi.org/10.1016/j.landurbplan.2013.11.004

15 [11] Allegrini, J., Kampf, J., Dorer, V., & Carmeliet, J. (2013). Modelling the urban microclimate and its influence on building energy demands of an urban neighbourhood. Proceedings of the CISBAT, Lausanne, Switzerland, 4-6 September 2013. [12] Santamouris, M., Papanikolaou, N., Livada, I., Koronakis, I., Georgakis, C., Argiriou, A., & Assimakopoulos, D. N. (2001). On the impact of urban climate on the energy consumption of buildings. Solar Energy, 70(3), 201-216. doi: http://dx.doi.org/10.1016/S0038-092X(00)00095-5 [13] Takebayashi, H., & Moriyama, M. (2007). Surface heat budget on green roof and high reflection roof for mitigation of urban heat island. Building and Environment, 42(8), 2971-2979. doi: http://dx.doi.org/10.1016/j.buildenv.2006.06.017 [14] Erell, E., Boneh, D., Pearlmutter, D., & Bar-Kutiel, P. (2013). Effect of high-albedo materials on pedestrian thermal sensation in urban street canyons in hot climates. Proceedings of the 29th Conference on Sustainable Architecture for a Renewable Future - PLEA2013, Munich, Germany, 10-12 September 2013. [15] Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40(3), 394-398. doi: http://dx.doi.org/10.1016/j.enbuild.2007.03.007 [16] Allegrini, J., Dorer, V., & Carmeliet, J. (2012). Influence of the urban microclimate in street canyons on the energy demand for space cooling and heating of buildings. Energy and Buildings, 55(0), 823-832. doi: http://dx.doi.org/10.1016/j.enbuild.2012.10.013 [17] Li, D. H. W., Yang, L., & Lam, J. C. (2012). Impact of climate change on energy use in the built environment in different climate zones – A review. Energy, 42(1), 103-112. doi: http://dx.doi.org/10.1016/j.energy.2012.03.044 [18] Atmaca, I., Kaynakli, O., & Yigit, A. (2007). Effects of radiant temperature on thermal comfort. Building and Environment, 42(9), 3210-3220. doi: http://dx.doi.org/10.1016/j.buildenv.2006.08.009 [19] Kwok, A. G., & Rajkovich, N. B. (2010). Addressing climate change in comfort standards. Building and Environment, 45(1), 18-22. doi: http://dx.doi.org/10.1016/j.buildenv.2009.02.005 [20] Djongyang, N., Tchinda, R., & Njomo, D. (2010). Thermal comfort: A review paper. Renewable and Sustainable Energy Reviews, 14(9), 2626-2640. doi: http://dx.doi.org/10.1016/j.rser.2010.07.040 [21] Emmanuel, R., & Fernando, H. (2007). Urban heat islands in humid and arid climates: role of urban form and thermal properties in Colombo, Sri Lanka and Phoenix, USA. Climate Research, 34(3), 241-251. doi: 10.3354/cr00694 [22] Hopkinson, R. G. (1972). Glare from daylighting in buildings. Applied Ergonomics, 3(4), 206-215. doi: http://dx.doi.org/10.1016/0003-6870(72)90102-0 [23] Kalvelage, K., & Dorneich, M. (In press). A user-centered approach to human-building interactions. Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting, Chicago, IL, 27-31 Oct In press. [24] Roetzel, A., Tsangrassoulis, A., & Dietrich, U. (2014). Impact of building design and occupancy on office comfort and energy performance in different climates. Building and Environment, 71(0), 165-175. doi: http://dx.doi.org/10.1016/j.buildenv.2013.10.001 [25] Guan, L. (2009). Preparation of future weather data to study the impact of climate change on buildings. Building and Environment, 44(4), 793-800. doi: http://dx.doi.org/10.1016/j.buildenv.2008.05.021 [26] Loibl, W., Tötzer, T., Köstl, M., Züger, H., & Knoflacher, M. (2011). Modelling Micro-climate Characteristics for Urban Planning and Building Design. In J. Hřebíček, G. Schimak & R. Denzer (Eds.), Environmental Software Systems. Frameworks of eEnvironment (Vol. 359, pp. 605-617): Springer Berlin Heidelberg. [27] Larsen, L., Rajkovich, N., Leighton, C., McCoy, K., Calhoun, K., Mallen, E., . . . Kwok, A. (2011). Green Building and Climate Resilience: Understanding Impacts and Preparing for Changing Conditions: University of Michigan; U.S. Green Building Council. [28] Gaitani, N., Mihalakakou, G., & Santamouris, M. (2007). On the use of bioclimatic architecture principles in order to improve thermal comfort conditions in outdoor spaces. Building and Environment, 42(1), 317-324. doi: http://dx.doi.org/10.1016/j.buildenv.2005.08.018 [29] Shashua-Bar, L., Pearlmutter, D., & Erell, E. (2011). The influence of trees and grass on outdoor thermal comfort in a hot-arid environment. International Journal of Climatology, 31, 1498-1506. doi: 10.1002/joc.2177

16 [30] Susca, T., Gaffin, S. R., & Dell’Osso, G. R. (2011). Positive effects of vegetation: Urban heat island and green roofs. Environmental Pollution, 159(8–9), 2119-2126. doi: http://dx.doi.org/10.1016/j.envpol.2011.03.007 [31] Adger, W. N., Agrawala, S., Mirza, M. M. Q., Conde, C., O’Brien, K., Pulhin, J., . . . Takahashi, K. (2007). Assessment of adaptation practices, options, constraints and capacity. Climate change, 717-743. [32] Climate Zones. (2000). [33] Brazel, A., Selover, N., Vose, R., & Heisler, G. (2000). The tale of two climates‹Baltimore and Phoenix urban LTER sites. Climate Research, 15(2), 123-135. doi: 10.3354/cr015123 [34] Jenerette, G. D., Harlan, S. L., Stefanov, W. L., & Martin, C. A. (2011). Ecosystem services and urban heat riskscape moderation: water, green spaces, and social inequality in Phoenix, USA. Ecological Applications, 21(7), 2637-2651. doi: 10.1890/10-1493.1 [35] Yaghoobian, N., & Kleissl, J. (2012). Effect of reflective pavements on building energy use. Urban Climate, 2, 25-42. doi: http://dx.doi.org/10.1016/j.uclim.2012.09.002 [36] Sailor, D., Erell, E., Kang, D., & Botham, D. (2012). 399: Building energy use implications of groundlevel albedo modification. Proceedings of the 8th International conference on Urban Climates, Dublin, Ireland, 6-10 August 2012. [37] Duell, M., & Webb, S. (2013, 3 Sept). Now the Walkie Talkie buidling is melting BICYCLES: Dazzling light reflected from giant London skyscraper scorches bike seats (and you can even fry an egg). Mail Online. [38] Erell, E. (2008). The application of urban climate research in the design of cities. Advances in Building Energy Research, 2, 95-121. doi: 10.3763/aber.2008.0204 [39] Van Den Wymelenberg, K. (2012). Patterns of occupant interaction with window blinds: A literature review. Energy and Buildings, 51(0), 165-176. doi: http://dx.doi.org/10.1016/j.enbuild.2012.05.008 [40] Alzoubi, H. H., & Al-Zoubi, A. H. (2010). Assessment of building façade performance in terms of daylighting and the associated energy consumption in architectural spaces: Vertical and horizontal shading devices for southern exposure facades. Energy Conversion and Management, 51(8), 1592-1599. doi: http://dx.doi.org/10.1016/j.enconman.2009.08.039 [41] Galasiu, A. D., & Veitch, J. A. (2006). Occupant preferences and satisfaction with the luminous environment and control systems in daylit offices: a literature review. Energy and Buildings, 38(7), 728-742. doi: http://dx.doi.org/10.1016/j.enbuild.2006.03.001 [42] Kalvelage, K., Passe, U., Rabideau, S., & Takle, E. S. (2014). Changing climate: The effects on energy demand and human comfort. Energy and Buildings, 76(0), 373-380. doi: http://dx.doi.org/10.1016/j.enbuild.2014.03.009 [43] National Renewable Energy Laboratory. (2012). National Solar Radiation Database 1991-2010 [Data file]. Retrieved from http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2010/#doc [44] Erell, E., & Williamson, T. (2006). Simulating air temperature in an urban street canyon in all weather conditions using measured data at a reference meteorological station. International Journal of Climatology, 26, 1671-1694. doi: 10.1002/joc.1328 [45] Burian, S., Velugubantla, S., & Brown, M. (2002). Morphological analyses using 3D building databases: Phoenix, Arizona: LA-UR-02-6726, Los Alamos National Laboratory, Los Alamos, New Mexico. [46] D&R International Ltd. (2012). 2011 Buildings energy data book. Silver Spring, Maryland: D&R International, Ltd. [47] U.S. Department of Energy. (2012). Commercial Reference Buidlings [Building Models]. Retrieved from: http://www1.eere.energy.gov/buildings/commercial/ref buildings.html. [48] Standard 55-2004: Thermal environmental conditions for human occupancy. (2004). Atlanta, Georgia: ASHRAE. [49] Deru, M., Field, K., Studer, D., Benne, K., Griffith, B., Torcellini, P., . . . Crawley, D. (2011). U.S. Department of Energy commercial reference building models of the national building stock (NREL/TP-5500-46861), Golden, Colorado: National Renewable Energy Laboratory. Retrieved from http://www.osti.gov/bridge [50] Zhang, Y., & Barrett, P. (2012). Factors influencing occupants’ blind-control behaviour in a naturally ventilated office building. Building and Environment, 54(0), 137-147. doi: http://dx.doi.org/10.1016/j.buildenv.2012.02.016

17 [51] Nazzal, A. A. (2005). A new evaluation method for daylight discomfort glare. International Journal of Industrial Ergonomics, 35(4), 295-306. doi: http://dx.doi.org/10.1016/j.ergon.2004.08.010 [52] U.S. Department of Energy. (2013). EnergyPlus (Version 8.1.0.009) [Computer software]. Retrieved from www.energyplus.gov [53] Crawley, D. (2008). Estimating the impacts of climate change and urbanization on building performance. Journal of Building Performance Simulation, 1(2), 91-115. doi: 10.1080/19401490802182079