Solar shading control strategy for office buildings in cold climate

Energy and Buildings 118 (2016) 316–328

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Solar shading control strategy for office buildings in cold climate Line Karlsen a,∗ , Per Heiselberg b , Ida Bryn a , Hicham Johra b a Oslo and Akershus University College of Applied Science, Faculty of Technology, Art and Design, Civil Engineering and Energy Technology, PB 4 St. Olavs plass, NO-0130 Oslo, Norway b Aalborg University, Division of Architectural Engineering, Department of Civil Engineering Sofiendalsvej 11, DK-9200 Aalborg SV, Denmark

a r t i c l e

i n f o

Article history: Received 13 November 2015 Received in revised form 1 February 2016 Accepted 5 March 2016 Available online 9 March 2016 Keywords: Solar shading Control strategy Integrated design

a b s t r a c t The objective of the present study was to develop a solar shading control strategy for venetian blinds applied on office buildings in cold climates in order to achieve acceptable energy use and indoor environmental performance. A control strategy based on a combination of internal and external shading develop within the Norwegian R&D project “Fasader i glass som holder hva vi lover” (“Glazed facades keeping what we promise”, translation by author) was extended with factors related to glare, daylight sufficiency and view to the outside. The study used full-scale experiments in a test room in Aalborg, Denmark, to verify the performance of the control strategy, and the study was further expanded with annual simulations of the office room at different locations. Results of the annual performance illustrated that the proposed control strategy would lead to satisfying compromises between the energy and indoor environmental performance. Generally, the investigation exemplifies the importance of doing integrated evaluations of energy use and thermal and visual comfort when making decisions regarding solar shading control strategies. © 2016 Elsevier B.V. All rights reserved.

1. Introduction

1.1. Control of dynamic solar shading

Modern office buildings often consist of a high proportion of glazing in the fac¸ade, which requires considerable attention during the building design with respect to its impact on occupant comfort as well as on energy demand for cooling, heating and lighting. Use of solar shading to control solar radiation through the glazed openings is usually essential in office buildings in order to obtain visual comfort, thermal comfort as well as a decreased energy use for cooling. Solar shading systems can be static or dynamic. Results from an investigation by Nielsen et al. [1] indicate that dynamic solar shading solutions function better than static solutions in the Danish climate. This is true both with respect to energy demand and reduction of overheating, as well as it allows for daylight supply and view to the outside when there is no need for solar shading. Winther [2] and Liu [3] also confirm the improved building performance by applying dynamic solar shading on different buildings in Denmark; they claim that use of intelligent dynamic facades are essential in achieving the high building performance required in the future.

From an energy point of view, automatic control should be applied on dynamic solar shading in office buildings, since research shows that users of the building do not tend to manually change the solar shading position for the short-term events in the outdoor weather conditions and the blind rate of change for manually systems is commonly rather low [4–6]. Results from one of our earlier studies indicate the importance of considering user-accepted solar shading control strategies during building design in order to be able to make realistic building performance predictions [7]. A number of researchers state the significance of making integrated evaluations of daylight, thermal comfort and energy use when selecting a solar shading system and control strategies since an appropriate solution might be a compromise between these aspects [1,8–12]. Research shows that people in indoor spaces generally like access to a window for daylight provision and outside view (e.g. [7,13–15]). Bakker et al. [16] emphasise that the balance between preventing glare and providing daylight to the room as well as a view to the outside are important issues in any solar shading control strategy. Venetian blinds are a flexible solar shading solution with adjustable slat angles where the view to the outside can be maintained at many slat positions, as well as the fact that the slats can change the direction of the incident light which makes it a good

∗ Corresponding author at: PB 4 St. Olavs plass, NO-0130 Oslo, Norway. E-mail addresses: [email protected] (L. Karlsen), [email protected] (P. Heiselberg), [email protected] (I. Bryn), [email protected] (H. Johra). http://dx.doi.org/10.1016/j.enbuild.2016.03.014 0378-7788/© 2016 Elsevier B.V. All rights reserved.

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solution for daylight control [17]. This may be the reason why Kirimtat et al. [18] found that venetian blinds are the most commonly studied shading device during the last decades. Several simulation studies have used venetian blinds with a cut-off strategy of the slats to achieve a balance between preventing glare and providing daylight supply as well as a view to the outside [19–22]. However, the cut-off strategy might be insufficient to avoid glare [7,21,22]. In order to provide sufficient glare-free daylight, Chan and Tzempelikos [21] suggest controlling the solar shading according to Daylight Glare Probability (DGP), either continuously controlled using real-time DGP simulation or pre-calculated correlations between transmitted illuminance and DGP. Yun et al. [23] also consider DGP as a control criterion for obtaining visual comfort within an office building. However, they conclude that this metric is impractical for calculating in real scenes and, therefore, suggest implementing vertical eye illuminance (Ev ) as a control criterion. Ev is the vertical illuminance at eye level of a seated person, approximately 1.2 m above floor level. Other researchers, e.g. [24,25], have also suggested control of vertical illuminance for achieving visual comfort. Solar irradiance is a simple and relatively common parameter used in solar shading control [4,5,12,26–28]. Van Den Wymelenberg [5] finds evidence in reviewed literature of a solar irradiance based blind control predictor used as a proxy for occupants’ interactions with window blinds. However, the literature suggests that there is a wide disparity among the irradiance values to use, ranging from approximately 100–450 W/m2 , and a variety of locations to detect the irradiance [5]. When trying to find the correlation between solar radiation and the occupants’ interactions with solar shading, it would be preferable to assess the transmitted solar radiation which is the condition experienced by occupants. However, O’Brien et al. [4] found that a significant part of the studies in the literature only considers external conditions, probably since it is easier to measure. Van Moeseke et al. [12] study the impact of control rules on the efficiency of shading devices for a two-person office room located in Belgium. They found that strategies based on both the external irradiance and the internal temperatures were more efficient to balance comfort and energy savings compared to strategies based on solar irradiation or internal temperature alone. Use of the combined criteria ensures better utilization of solar gains for heating during winter and may limit the time of closed mode and, thereby, increase the visual contact with the exterior as well as inlet of daylight. Use of solar shading during night time might have an insulating effect and, according to Grynning et al. [29], it may contribute to a slight reduction in the annual net energy demand in cold climates. According to Bryn [30], the potential improvement of the U-value of the window system when applying solar shading depends on the initial U-value of the window, the insulation and emissivity of the solar shading and the air tightness of the cavity between the window and the solar shading. Oleskowicz-Popiel and Sobczak [31] investigated the effect of roller blinds on heat losses through a double-glazing window during the heating season in Poland. With roller blinds tightly closed during the night hours, an insulated external roller blind and an internal textile roller blind contributed to about 45% and 33% energy saving respectively for an uncoated glazing and 44% and 29% respectively for a low-E coated glazing. The Norwegian R&D project “Glazed facades keeping what we promise” (FG project) evaluates different functions of solar shadings both with respect to daylight, thermal comfort and reduction in energy use to cooling and heating. The FG project developed a control algorithm which utilises a combination of internal and external solar shading, as proposed in Ref. [32], with the aim of improving thermal comfort as well as reducing the energy use, see Ref. [33]. The objective of the present study is to continue with the work conducted within the FG project by extending the control

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algorithm with factors relating to glare, daylight sufficiency and view based on findings in the literature in order to obtain a realistic control strategy that balances the aspects of thermal and visual indoor environmental performance and energy demand. Use of fullscale measurements in an experimental room located in Aalborg, Denmark, will verify the performance of the control strategy. 2. Control algorithm The control strategy is divided into two main parts: work hours and outside work hours. During the work hours, the main goal is to obtain occupant comfort. In this mode the control strategy focuses on avoiding glare and overheating while also, when possible, ensuring satisfactory daylight supply and view to the outside by utilizing the estimated cut-off angle of the slats in activated state, i.e. the angle where direct solar radiation is prevented while providing maximum view contact to the exterior. However, the minimum tilt angle of the slats is set to 15◦ in order to avoid negative cut-off angles in situations with large solar altitude angles and, thereby, avoiding view to the sky and high risk of glare [34]. An initial version of the proposed control strategy for work hours was applied in an earlier reported occupant survey, see Ref. [7]. Results from this survey indicated that view is an important factor for occupant comfort in a work environment and that the participants appreciated the view gained through tilted solar shading blind slats in activated position compared to closed slats in activated position. It was further found that the initial version of the control strategy was associated with relatively high occurrence of glare. Improvements made for the present proposal of the solar shading control strategy is that the tilt angle is step-wised increased in case the cut-off angle is insufficient in avoiding glare. Additionally, the set-point of vertical eye illuminance, which is used as an indication of glare, is lowered from 2000 lx to 1700 lx based on findings reported in Ref. [35]. Use of vertical eye illuminance as activation criteria and application of tilted blind slats in activated position are the main new features compared to the strategy presented in the FG-project. The cut-off angle is calculated according to Eq. (1) [36] where d is the profile angle of the sun, s is the spacing between the slats, w is the width of the slats, ˛ is the solar altitude angle and  is the solar surface azimuth. When activated, the blind shades the whole window and all the slats have the same tilt angle.



ˇcut−off = sin−1 cos(d) × d = tan−1

 tan ˛ 

s w



−d

cos ()

(1) (2)

Outside work hours, energy saving is the main focus, and the solar shading is utilized both as an insulating layer to reduce heat loss during cold periods as well as a protecting shield against excessive unwanted solar gains during cooling-dominated periods (Fig. 1). 3. Verification of solar shading control performance 3.1. Test facility In order to verify the performance of the control strategy, it is implemented in a full-scale test facility located at Aalborg University, Denmark (latitude 57.02◦ N, longitude 10.0◦ E), see Fig. 2. The test facility, named the Cube, has previously been used by Kalyanova [37] to investigate double-skin fac¸ades, by Winther [2] and Liu [3] to explore intelligent glazed facades and by Le Dréau [38] to investigate radiant and air-based heating and cooling systems. The set-up from Le Dréau has been kept and extended for the present survey. The following sections will give a short description of the test facility, for further details see Ref. [38] Part II.

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Fig. 1. Optimized control strategy with respect to visual and thermal comfort and energy use. * Cut-off angle, with minimum tilt angle of 15◦ . ** Cut-off angle, with minimum tilt angle of 15◦ and stepwise increase of 10◦ until Ev < 1700 lx. tair, room is room air temperature (◦ C), tset, heat/cool is set-point temperature for heating/cooling, Ev is vertical eye illuminance and Isun is vertical external solar irradiation.

Fig. 2. Photo of the cube with and without external solar shading activated and plan and section view of the facility.

3.1.1. Constructions The Cube has a south-oriented experimental room which is 2.76 m wide, 3.6 m deep and 2.70 m high. The experimental room consists of an insulated wooden construction covered internally by 110–160 mm expanded polystyrene (EPS). In order to increase the thermal mass of the room, panels composed of 30 mm extruded polystyrene and 13 mm plaster have been glued to the walls [38] and a 50 mm thick concrete tile floor has been added. Additionally, it is equipped with a few standard office furniture and equipment. Le Dréau [38] measured the infiltration between the test room and the outdoor to be less than 0.3 L/sm2 floor . A ventilated guarded zone surrounds all the enclosures of the experimental room, except the south fac¸ade, in order to minimize heat transfer through the construction, see Fig. 2. In the ventilated guarded zone, the temperature is kept constant. The south fac¸ade of the experimental room is equipped with a double layer glazing (2.76 m × 1.60 m) that constitutes the major part of the boundary of the test room towards the exterior, see

Fig. 2. The window is equipped with both an internal and external white 65 mm convex venetian blind with 60 mm spacing between the slats. Table 1 summarises thermal and optical properties for the window system.

3.1.2. Control of the indoor environment and internal heat gains Table 2 gives an overview of how the indoor environment and the internal gains are controlled in the Cube during the experiments (Figs. 3 and 4).

3.2. Weather data Vertical irradiance was measured on the facade before and after the glazing by use of two CMP21 pyranometers (accuracy ± 3% [38]). An additional CMP22 pyranometer was placed horizontally on the top of the roof of the experimental room in order to record the global radiation. The fraction of direct normal and diffuse hor-

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Table 1 Glazing and shading properties at reference conditions according to ISO 15099 [39] for various configurations. Glazing/shading configuration

Tilt angle (◦ )

U-value

g-value

Solar transmittance

Visible transmittance

Glazing Glazing w/external shading

– 15 80 15 80 80

1.23 1.12 1.05 1.14 1.09 0.94

0.36 0.29 0.04 0.34 0.26 0.02

0.31 0.22 0.01 0.23 0.02 0

0.65 0.49 0.02 0.52 0.06 0

Glazing w/internal shading Glazing w/external and internal shading

Table 2 Overview of how indoor environment and internal gains are controlled during the experiments. Category

Quantity

Comments and measurements accuracy

Occupants

1 thermal manikin (8–18 every day)

Lighting

Fluorescent ceiling light. Max 60 W (8–18 every day)

Ventilation

Supply air (CAV)

Temperature control

Heating

2.6 l/(s m2 ) (8–18 every day) 1.6 l/(s m2 ) (rest of the time) Electrical heater, capacity of 1200 W

Manikin controlled to maintain a skin temperature of 34 ◦ C Heat the manikin ± 1% [38,40] Control the skin temperature ± 0.2 K [38,40] Artificial lighting is added if daylight alone cannot supply minimum 300 lux at the horizontal work plane 1.5 m into the room. Artificial lighting is controlled to maintain 500 lux at the work plane according to the dimming characteristics given in Fig. 4. Illuminances are measured with cosine corrected Hagner SD1/SD2 detectors connected to a Hagner MCA-1600 Multi-Channel Amplifier with a basic accuracy of ±3%. Power use for artificial lighting is recorded with Norma D5255S power analyser, basic accuracy ±0.2% Air flow calculated based on pressure differences over an orifice plate before the inlet fan, ±7.5% [38] Heating power recorded with Norma D5255S power analyser, basic accuracy ±0.2%. Cooling power calculated as a function of water flow rate (±0.9 L/h [38]) and temperature difference between the forward and return water flow (±0.057 K, Pt-500 temperature sensors [38]) Heating and cooling is controlled according to air temperature measured by a silver-coated type K thermocouples (± 0.1◦ C) protected by a mechanically ventilated silver-shield, see Ref. [41] A vertical illuminance sensor placed at the east sidewall 1.2 m into the room at height 1.2 m above the floor is used in combination with the correlation equation given in Fig. 3 as an approximation of vertical eye illuminance at the occupant position in order to indicate occurrence of glare Same sensor as used for room temperature control CMP21 pyranometer placed exterior next to the glazing, see Fig. 2 (accuracy ±3% [38])

Internal gains

Solar shading control

Cooling

Active chilled beam, capacity of approx. 500 W

Illuminance

–

Temperature Irradiance

– –

Fig. 3. Right: Correlation between vertical eye illuminance at occupant position and vertical illuminance at the sensor location at the east sidewall. Left: Photo of the location of the two sensors, indicated by red circles. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

izontal radiation was calculated by use of the Skartveit-Olseth model [42]. Outdoor air temperature was measured with a silver-coated type K thermocouple (accuracy ± 0.1◦ C) shielded from direct solar radiation, placed at the north fac¸ade of the Cube, see Fig. 2. Data of wind velocity and relative humidity of the outdoor air was collected from the Danish Meteorological Institute [43] for the location

of Aalborg Airport, approximately 13 km north-west of the experiment location. 3.3. Simulation model A simulation model of the Cube is constructed within the sophisticated dynamic simulation program IDA Indoor Climate and

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3.5. Results and discussion—verification of solar shading control performance The following sections consider the performance of the solar shading control strategy with respect to power demand for heating and cooling as well as the thermal environment. Comparison of measured and simulated daylight supply to the room under the proposed control strategy has showed satisfying results which is presented in an earlier publication, see Ref. [46].

Fig. 4. Dimming characteristics of the luminaire in the cube with respect to the light sensor.

Energy 4.7 (IDA ICE)[44]. The analysis uses a detailed zone model with a full Stefan-Boltzman longwave radiation model, which makes it possible to calculate position dependent operative temperatures. The calculation of operative temperature also includes the effect of direct solar radiation [45]. The detailed window model in IDA ICE is applied in the simulation where the thermal window and shading performance are modelled according to ISO 15099:2003 [39]. The daylight contribution from the window opening is calculated with the daylight features in IDA ICE based on Radiance three-phase calculations [46]. In the first part of the analysis where measurements and simulations are compared, records of heat gains from the thermal manikin and the artificial lighting during the experiment are used as input in the simulation model. This is done in order to focus on the solar shading control performance and to avoid the internal gains causing divergence between measured and simulated heating and cooling demand.

3.4. Data analysis Measured and simulated building performance is compared, and statistical analyses are carried out to evaluate if the simulation model is able to reproduce the measurements with reasonable accuracy. The coefficient of determination (R2 ) is used to indicate how well the simulation result fits a linear regression line. The mean bias errors (MBE), mean absolute errors (MAE) and the root mean squared errors (RMSE) are used to describe the errors of the simulation compared to the measurements; their definitions are given in Eq. (3)–(5) [47]. The MBE indicates the tendency of one data series to be larger/smaller than the other [48] while the MAE indicated the average error magnitude [47]. The RMSE indicates how far one data series “fluctuates” around the other [48]. The statistical analyses are performed in MS Excel. 1 (xi − yi ) N N

MBE =

(3)

i=1

1 |xi − yi | N N

MAE =

(4)

i=1

  N 1 RMSE =  (xi − yi )2 N

i=1

(5)

3.5.1. Winter performance In order to verify the performance of the control strategy during heating season, measurements in the Cube were conducted during January 2015 and compared with simulation results. Due to a relatively warm period in Aalborg at this time, the set-points for heating and cooling were set to 32 ◦ C and 35 ◦ C respectively in order to trigger a heating demand. Fig. 5 shows the resulting heating power during the measurements and simulations for the period of 17.01.2015–23.01.2015. Fig. 5(a) compares measured and simulated heating power for a situation to which the proposed optimized shading control is applied. Additionally, the figure illustrates by use of simulations how the heating demand would have been with only external solar shading and without night shading. (b) visualizes the correlation between the measured and simulated heating power under the optimized shading control strategy. The simulation results reproduce the measurements rather well and the coefficient of determination (R2 ) is equal to 0.94. Fig. 5(a) and (b) show that some severe deviations occur at certain situations during daytime where the simulation underdetermines the heating demand. The reason for this is mainly the differences in measured and modelled vertical irradiances at the fac¸ade and is most probably attributed to the inaccuracy in the Skartvet–Olseth model under intermediate sky conditions. On January 22 for example, the external vertical irradiation on the south fac¸ade is overestimated with a maximum of 90 W/m2 compared to the measurements, see Fig. 6(a), which further leads to overestimation of the solar gain into the building in the simulation and, consequently, an underestimation of heating demand, which corresponds with the results in Fig. 5(a). Based on the simulation results, it is further evident that use of internal and external solar shading as an insulating layer outside work hours may have a certain energy saving potential in cold climates during the heating season. Additionally, using internal solar shading as glare protection and letting heat enter the room during periods with a heating demand contributes to reduce energy for heating at daytime, see 17.01.2015. Comparison of air temperatures and operative temperatures at the occupant position also shows that the simulations are capable of reproducing the measurements with a reasonable level of accuracy. The maximum deviations between measured and simulated air and operative temperatures are within ±0.8 ◦ C and ±1.2 ◦ C respectively, and in similarity to the heating power predictions, the largest differences also occur during periods where the solar gain predictions deviate, see Fig. 6. The MBE given in Table 3 of −0.04 ◦ C and 0 ◦ C for air temperature and operative temperature respectively, indicate that there is no clear tendency of either measurements or simulations to be larger or smaller than the other. MAE of 0.06 ◦ C and 0.10 ◦ C for air temperature and operative temperature respectively, indicate that the average error magnitudes are within the measurement accuracy level of ±0.1 ◦ C (thermocouple type K). Further, RMSE of 0.19 and 0.20 ◦ C for air temperature and operative temperature indicates that the distance between the measured and simulated data series is rather small. Additionally it can be mentioned that the deviation between the measured and simulated air temperatures lies within the accuracy level of the air

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Fig. 5. (a) Comparison of measured and simulated heating uses during the period of 17.01.2015–23.01.2015. (b) Correlation between measured and simulated heating uses under the proposed optimized control strategy.

Fig. 6. (a) Comparison of measured and simulated total vertical irradiance at the south fac¸ade during the period of 17.01.2015–23.01.2015. (b) Comparison of measured and simulated operative temperature at the occupant position.

Table 3 Mean bias errors, mean absolute errors and root mean square error of the simulated air and operative temperatures (◦ C) for winter and summer performances in the cube. MBE (◦ C)

Winter performance Summer performance

MAE (◦ C)

RMSE (◦ C)

Tair

Top

Tair

Top

Tair

Top

−0.04 0.08

0 −0.04

0.06 0.11

0.10 0.17

0.19 0.15

0.20 0.20

temperature sensor (thermocouple type K) of ±0.1 ◦ C during 88% of the time. 3.5.2. Summer performance For the summer season, measurements in the Cube were conducted during July 2014. During this period, the set-points for heating and cooling were fixed to 21 ◦ C and 25 ◦ C respectively. Fig. 7 illustrates the resulting chill beam cooling power for the measurements and simulations during the period of 25.07.2014–30.07.2014. Fig. 7(a) compares measured and simulated cooling power for a situation to which the proposed optimized shading control is applied. Additionally, the figure illustrates by use of simulations how the cooling demand would have been if only internal solar shading were applied. It is obvious that much higher amounts of heat would have entered the room and needed to be removed by mechanical cooling if internal blinds were applied compared to use of external blinds. The reader should additionally be aware that the experimental room is equipped with a selective sun control glazing and that the difference in cooling power demand between use of internal and external solar shading would have been even larger with use of an unselective glazing. Fig. 7(b) visualizes the correlation between the measured and simulated

cooling power under the optimized shading control strategy. Similar to the heating comparisons, the simulation results reproduce the measurements rather well with a coefficient of determination (R2 ) of 0.94. It should also be pointed out that the simulation results are within the measurement accuracy level at all times during the analysed period (±0.9 L/h for the flow meters and ±0.057 K for the Pt-500 temperature sensors). The maximum disparity between measured and simulated air and operative temperatures are within ±0.4 ◦ C. The MAE and RMSE given in Table 3 of 0.11 and 0.15 ◦ C for air temperature and 0.17 and 0.20 ◦ C for operative temperature are assessed to be within acceptable ranges. 4. Annual performance solar shading control The previous sections illustrate that IDA ICE is able to reproduce both heating and cooling season situations with reasonable accuracy and that the simulation model is well calibrated, which makes it interesting to expand the investigation and explore the annual performance of the control strategy at different geographical locations. The expanded investigation considers the locations Aalborg (57.0◦ N, 10.0◦ E), Oslo (59.9◦ N, 10.7◦ E) and Røros (66.6◦ N, 11.4◦ E).

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Fig. 7. (a) Comparison of measured and simulated cooling use during the period of 25.07.2014–30.07.2014. (b) Correlation between measured and simulated cooling use under the proposed optimized control strategy.

Fig. 8. Annual duration of outside air dry-bulb temperature (left) and total radiation on the south facade (right) for the locations Aalborg, Oslo and Røros from the climate data used in the annual simulations.

In order to give the reader an impression of the climate at the three locations, Fig. 8 visualizes the duration of outside air dry-bulb temperature and total radiation on the south fac¸ade. As anticipated, both Oslo and Røros have colder winters than Aalborg. However, Oslo has periods during the summer with slightly higher temperatures than Aalborg. Further, what is interesting is the fact that the total radiation on the south fac¸ade is at times higher for both Oslo and Røros compared to Aalborg, which probably can be attributed both the weather conditions as well as lower sun position in the sky at higher latitudes. The construction of the simulation model is kept unaltered from the investigation in the previous sections, while Table 4 summarises the HVAC set-points as well as internal gains used in the annual simulations. In order to evaluate the annual performance of the control strategy in Fig. 1, it is necessary to compare it to some reference. The authors decided to use the annual performance of a simple control strategy commonly used in building design annotated Control 100 W/m2 external, as well as the annual performance when there is no solar shading in use as references. Additionally, the authors evaluated how the annual performance of the proposed control strategy would be with either only internal or external solar shading. Table 5 summarises a short description of the investigated solar shading controls. 4.1. Results and discussion—annual performance solar shading control Figs. 9–17 present results of the annual performance of the solar shading control strategies outlined in Table 5 with respect to energy use and indoor environment for the locations of Aal-

borg, Oslo and Røros. Thermal comfort is evaluated according to operative temperature and operative temperatures in the range of 19–26 ◦ C is assessed as acceptable based on recommendations given in the guidance to the Norwegian building code [49]. Daylight supply is evaluated according to spatial daylight autonomy 300/50% (sDA300/50% ), which is defined as the percentage of analysis area that achieves the illumination threshold of 300 lux for 50% of the analysis period. According to the Illuminanting Engineering Society of North America (IES); sDA300/50% ≥55% has to be met in order for a space to be nominally acceptable daylit [50]. Risk of glare is indicated by use of vertical eye illuminance, since research has reported statistically significant correlations between this measure and occupants sensation of glare [24,35,51,52]. The percentage of occupied time with solar shading activated is given as a simplified inverse indication of view similar to the proposal by Reinhart and Wienold [53], the reader should be aware that this is a simplified measure and the quality of view is not taken into consideration. Since identical venetian blinds are used both for internal and external shading, there are only neglectable differences in the daylight results for the optimized control, detailed control external and detailed control internal. These results are, therefore, presented together under the label ‘optimized/detailed control’. Some of the same tendencies are seen for all three locations. With respect to energy use, the optimized control strategy contributes to the lowest specific energy use among the control strategies investigated at all locations. However, for the locations of Aalborg and Oslo, there is only a small reduction of 2–3 kWh/m2 net energy demand for the optimized control compared to the detailed control with only external shading. This may be explained by the fact that the cooling demand may be relatively dominating even for office rooms at these locations, which traditionally have been con-

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Table 4 Set-points for the HVAC systems and overview of the internal gains used in the annual analysis. Category

Input value

Set-point heating/cooling Internal gains

21◦ C/25◦ C (15 September–15 May) 19◦ C/25◦ C (15 May–15 September) Occupants

Equipment Lighting Supply air (CAV)

Ventilation

Supply air temperature Heat exchanger

1 occupant (7–19 weekdays) Activity level: 1 met Clothing level: 0.85 ± 0.25 clo 50 W (7–19 weekdays) Max 60 W, controlled to maintain 500 lux at the work plane according to the dimming characteristics given in Fig. 4 (7–19 weekdays) 2.6 l/(s m2 ) (7–19 weekdays) 1.6 l/(s m2 ) (rest of the time) Outdoor compensated (18 ◦ C at −20 ◦ C, 16 ◦ C at 25 ◦ C) 80%

Table 5 Overview of the simulated solar shading controls. Solar shading control

Description of solar shading control

Optimized control Detailed control external Detailed control internal Control 100 W/m2 external

According to the control algorithm in Fig. 1 According to the control algorithm in Fig. 1, but only with use of external solar shading According to the control algorithm in Fig. 1, but only with use of internal solar shading The solar shading is activated when the external vertical irradiance at the fac¸ade exceeds 100 W/m2 . In activated position the slats are closed with a slat angle of 80◦ , in practice totally closed No solar shading applied

No solar shading

Fig. 9. (a) Comparison of annual energy demand for heating, cooling and lighting for different solar shading controls for the location of Aalborg. (b) Duration curves of the operative temperature with inclusion of direct sun at the occupant position for the different solar shading control strategies for the location of Aalborg.

Fig. 10. Spatial distribution of daylight autonomy 300 lx for the different solar shading control strategies for the location of Aalborg.

sidered to be heating-dominated climates, see No solar shading in Fig. 9a and Fig. 12a. This corresponds with earlier findings by Grynning et al. [54]. The benefit of applying a combination of internal and external solar shading is more prominent when assessing the more heating-dominated climate of Røros where there is a reduction in the net energy demand of approximately 10 kWh/m2 compared to

only using external shading with the detailed control strategy. Yet, investment cost may be rather significant when installing two sets of shading with automatic control. However, cost-benefit analysis is outside the scoop of this present investigation. By examine Figs. 9–17, it is evident that use of solar shading is required at all investigated locations. When there is no solar shad-

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Fig. 11. Analysis of the annual Ev at the occupant position with a view direction towards the south-west corner of the room according to Fig. 2 for the location of Aalborg. The dark red areas indicate hours with an Ev level above 2100 lx (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.).

Fig. 12. (a) Comparison of annual energy demand for heating, cooling and lighting for different solar shading controls for the location of Oslo. (b) Duration curves of the operative temperature at the occupant position with inclusion of direct sun for the different solar shading control strategies for the location of Oslo.

Fig. 13. Spatial distribution of daylight autonomy 300 lx for the different solar shading control strategies for the location of Oslo.

ing applied, the daylight sufficiency is as anticipated high with a spatial daylight autonomy of 300 lux for 50% of the occupied time (DA300 50% ) of 100% at all locations. Yet, glare at the occupant position is a concern during significant parts of the occupied time of the year; see Figs. 10, 13 and 16. Additionally, the thermal com-

fort is unacceptable with an operative temperature at the occupant position above 26◦ C for 20%-30% of the occupied time; see Figs. 8b, 11b and 14b which shows the duration of operative temperature at the occupant position during the occupied time. Further, it can be mentioned that the major part of occupied time with operative

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Fig. 14. Analysis of the annual Ev at the occupant position with a view direction towards the south-west corner of the room according to Fig. 2 for the location of Oslo. The dark red areas indicate hours with an Ev level above 2100 lx (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.).

Fig. 15. (a) Comparison of annual energy demand for heating, cooling and lighting for different solar shading controls for the location of Røros. (b) Duration curves of the operative temperature with inclusion of direct sun at the occupant position for the different solar shading control strategies for the location of Røros.

Fig. 16. Spatial distribution of daylight autonomy 300 lx for the different solar shading control strategies for the location of Røros.

temperatures above 26 ◦ C occurs during intermediate season at all three locations when no solar shading is applied and the solar altitude is low. If considering the Control 100 W/m2 external shading strategy, this approach leads to a slight decrease in net energy demand for

the location of Oslo compared to no solar shading, while it actually causes a minor increase in net energy demand for Aalborg and Røros due to increased lighting and heating demand. This is the control strategy that keeps the temperature duration at its lowest level for all considered locations. It is also the most efficient control

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Fig. 17. Analysis of the annual Ev at the occupant position with a view direction towards the south-west corner of the room according to Fig. 2 for the location of Røros. The dark red areas indicate hours with an Ev level above 2100 lx (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.).

Table 6 Summary of percentage of occupied time with activated solar shading and the percentage of time with activated solar shading with slat angle <45◦ . For control 100 W/m2 the tilt angle is fixed at 80◦ in activated mode. Percentage of time in activated mode with slat angle <45◦

Percentage of occupied time with solar shading activated

Aalborg Oslo Røros

Optimized control

Control 100 W/m

61 40 45

57 57 60

strategy to avoid a high level of vertical illuminance at the eye and, thereby, avoidance of risk of glare. However, with this control strategy the solar shading is activated with closed slats for 57–60% of the occupied time for all three investigated locations, which means that the view to the outside is substantially reduced, see Table 6. Figs. 9, 12 and 15 further indicate that the daylight sufficiency is unacceptable low at all locations with DA300 50% of 0%. It is apparent that the optimized solar shading control strategy or the detailed control strategy with only external shading is the best compromise between energy use and indoor environment for all three considered locations. This is the control strategy with the lowest net energy demand, a thermal comfort within acceptable ranges as well as a highly acceptable daylight sufficiency with DA300 50% of 100% at all three locations. With this control strategy, the solar shading is activated for 61%, 40% and 45% of the occupied time for the location of Aalborg, Oslo and Røros respectively. In activated state the slat angle is less than 45◦ during significant parts of the time; see Table 6. When the slats are tilted less than 45◦ , this gives a certain contact to the outside through the space between the slats. It should be noted that there are times when the vertical eye illuminance at the occupant position exceeds the threshold of 1700 lx even for this control strategy, which indicates that there is not an ideal correlation between vertical illuminance at the sensor placement and the occupant position at all times. It is even seen that vertical eye illuminance above 2000 lx occur at more than a few occasions, levels which have been associated with high probability of perception of glare [24,51,52]. This illustrates the challenge with sensor placement, and it also proves the importance of arranging for manual override of the solar shading as well as maybe flexibility of the occupants’ viewing direction in order for the occupant to be able to obtain an acceptable visual work environment at all times;

2

Optimized control

Control 100 W/m2

72 84 88

N/A N/A N/A

strategies which have been pointed out in earlier literature as well [7,16,35,55,56]. 5. Conclusion The objective of the present study was to develop a realistic solar shading control strategy for office buildings in cold climate with focus on low energy use and high thermal and visual indoor environmental performance. A control strategy based on a combination of internal and external shading developed within the Norwegian R&D FG project was extended with factors related to glare, daylight sufficiency and view to the outside. This was done by implementing vertical illuminance at eye level as closure criterion in order to reduce the risk of glare and by a modified cut-off strategy for the slat angle of the venetian blind in order to supply a certain amount of daylight to the room and view to the outside. Acceptable thermal and visual indoor environmental performance was the primary focus of the control strategy during occupied hours in order to ensure a robust control with limited overrules, since it is expected that occupants may take action if discomfort occurs; actions which may have negative impact on the building energy performance. Full-scale measurements showed promising performance of the proposed solar shading control strategy for both winter and summer conditions. The study investigated the annual performance of the solar shading strategy by using simulations for the locations Aalborg, Oslo and Røros and comparing these to the performance of the office room without solar shading and with a simple control strategy activated according to external irradiation. Generally, the investigation exemplifies the importance of doing integrated evaluations of energy use and thermal and visual comfort when making decisions regarding solar shading control strategies. The

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results of the annual performance illustrated that the proposed control strategy would be the best compromise between energy use and thermal and visual indoor environmental performance. Still, for moderate cold climates, the application of the proposed control strategy with only external shading might be the preferred alternative since investment cost of two sets of solar shading with automatic control might be unprofitable when considering the lifetime of the components. As for more extreme cold climates, the energy and indoor environmental performance analyses should be accompanied with a cost-benefit analysis when making decisions of installing only external or a combination of external and internal solar shading systems. Further, sensor placement for vertical illuminance might be a challenge since there is no ideal correlation between illuminance at two positions in the room at all times during a year. Even with activated solar shading and controlled tilt angle to avoid vertical illuminance >1700 lx at the sensor placement, the vertical eye illuminance at the occupant position might exceed this threshold which could be associated with a risk of glare. Still, it is assessed that the solar shading performance is acceptable since an earlier study indicates that a certain amount of glare might be accepted by the occupants as long as there is a view to the outside [7]. However, users should have the opportunity to overrule the automatic glare control within an office environment or have the flexibility to change viewing direction in order to be able to maintain an acceptable visual environment at all times. It is important that the designers arrange for such possibilities. Acknowledgement This paper is based on research conducted in a PhD project, project No. 26202, at Oslo and Akershus University College of Applied Science. Major thanks are directed towards Mingzhe Liu, Jérôme Le Dréau and Rasmus Lund Jensen at Aalborg University for their cooperation, assistance and guidance throughout the planning and execution of the measurements. We also thank the technicians at Aalborg University for all their assistance in the test facility. The analytical work of this study is a part of the R&D FG project, and we are grateful towards the project work group for useful discussions and input. References [1] M.V. Nielsen, S. Svendsen, L.B. Jensen, Quantifying the potential of automated dynamic solar shading in office buildings through integrated simulations of energy and daylight, Solar Energy 85 (5) (2011) 757–768. [2] F.V. Winther, Intelligent glazed facades—an experimental study, in: PhD, DCE Thesis No. 43, Department of civil engineering, Aalborg University, 2013. [3] M. Liu, Modelling and control of intelligent glazed facade, in: PhD Thesis, Aalborg University, Aalborg University Press, 2014. [4] W. O’Brien, K. Kapsis, A.K. Athienitis, Manually-operated window shade patterns in office buildings: a critical review, Build. Environ. 60 (0) (2013) 319–338. [5] K. Van Den Wymelenberg, Patterns of occupant interaction with window blinds: a literature review, Energy Build. 51 (0) (2012) 165–176. [6] P. Correia da Silva, V. Leal, M. Andersen, Occupants interaction with electric lighting and shading systems in real single-occupied offices: results from a monitoring campaign, Build. Environ. 64 (0) (2013) 152–168. [7] L. Karlsen, P. Heiselberg, I. Bryn, Occupant satisfaction with two blind control strategies: slats closed and slats in cut-off position, Solar Energy 115 (0) (2015) 166–179. [8] F. Gugliermetti, F. Bisegna, Daylighting with external shading devices: design and simulation algorithms, Build. Environ. 41 (2) (2006) 136–149. [9] A. Tzempelikos, M. Bessoudo, A.K. Athienitis, R. Zmeureanu, Indoor thermal environmental conditions near glazed facades with shading devices—part II: thermal comfort simulation and impact of glazing and shading properties, Build. Environ. 45 (11) (2010) 2517–2525. [10] M. David, M. Donn, F. Garde, A. Lenoir, Assessment of the thermal and visual efficiency of solar shades, Build. Environ. 46 (7) (2011) 1489–1496. [11] T.E. Kuhn, Solar control: comparsion of two new systems with the state of the art on the basis of a new general evaluation method for facades with venetian blinds or other solar control systems, Energy Build. 38 (6) (2006) 661–672.

327

[12] G. van Moeseke, I. Bruyère, A. De Herde, Impact of control rules on the efficiency of shading devices and free cooling for office buildings, Build. Environ. 42 (2) (2007) 784–793. [13] M. Bodart, A. Deneyer, Analyse of the survey on the office workers’ interest in windows, IEA 31, Subtask A, working document (2005). [14] B.L. Collins, Review of the psychological reaction to windows, Light. Res. Technol. 8 (2) (1976) 80–88. [15] J. Christoffersen, E. Petersen, K. Johnsen, O. Valbjørn, S. Hygge, SBi rapport 318: Vinduer og dagslys-en feltundersøgelse i kontorbygninger, Statens Byggeforskningsinstitut, SBI, ISBN. 8756310293. (1999). [16] L.G. Bakker, E.C.M. Hoes-van Oeffelen, R.C.G.M. Loonen, J.L.M. Hensen, User satisfaction and interaction with automated dynamic facades: A pilot study, Build. Environ. 78 (0) (2014) 44–52. [17] M.-C. Dubois, Solar shading for low energy use and daylight quality in offices. Simulations, measurements and design tools, in: PhD Thesis, Department of Construction and Architecture, Lund University, Lund, 2001, ISSN 1103-4467. [18] A. Kirimtat, B.K. Koyunbaba, I. Chatzikonstantinou, S. Sariyildiz, Review of simulation modeling for shading devices in buildings, Renewable Sustainable Energy Rev. 53 (2016) 23–49. [19] S. Zhang, D. Birru, An open-loop venetian blind control to avoid direct sunlight and enhance daylight utilization, Solar Energy 86 (3) (2012) 860–866. [20] M.G. Gomes, A.J. Santos, A.M. Rodrigues, Solar and visible optical properties of glazing systems with venetian blinds: numerical, experimental and blind control study, Build. Environ. 71 (0) (2014) 47–59. [21] Y.-C. Chan, A. Tzempelikos, Efficient venetian blind control strategies considering daylight utilization and glare protection, Solar Energy 98 (Part C(0)) (2013) 241–254. [22] J. Wienold, F. Frontini, S. Herkel, S. Mende, Climate based simulation of different shading device systems for comfort and energy demand, in: Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sydney, Australia, 2011. [23] G. Yun, K.C. Yoon, K.S. Kim, The influence of shading control strategies on the visual comfort and energy demand of office buildings, Energy Build. 84 (2014) 70–85. [24] K. Van Den Wymelenberg, M. Inanici, A critical investigation of common lighting design metrics for predicting human visual comfort in offices with daylight, LEUKOS 10 (3) (2014) 145–164. [25] I. Konstantzos, A. Tzempelikos, Y.-C. Chan, Experimental and simulation analysis of daylight glare probability in offices with dynamic window shades, Build. Environ. 87 (0) (2015) 244–254. [26] C.F. Reinhart, K. Voss, Monitoring manual control of electric lighting and blinds, Light. Res. Technol. 35 (3) (2003) 243–260. [27] A. Tzempelikos, A.K. Athienitis, The impact of shading design and control on building cooling and lighting demand, Solar Energy 81 (3) (2007) 369–382. [28] A. Atzeri, F. Cappelletti, A. Gasparella, Internal versus external shading devices performance in office buildings, Energy Procedia 45 (2014) 463–472. [29] S. Grynning, B. Time, B. Matusiak, Solar shading control strategies in cold climates—heating, cooling demand and daylight availability in office spaces, Solar Energy 107 (2014) 182–194. [30] SINTEF Byggforsk Kunnskapssenter, http://bks.byggforsk.no/DocumentView. aspx?sectionId=2&documentId=2930. [31] C. Oleskowicz-Popiel, M. Sobczak, Effect of the roller blinds on heat losses through a double-glazing window during heating season in Central Europe, Energy Build. 73 (0) (2014) 48–58. [32] P.J. Littlefair, J. Ortiz, C. Das Bhaumik, The energy effects of controlling solar shading. Somfy.com, 2016 https://www.somfysystems.com/file.cfm/ Controlling-Solar-Shading.pdf?contentID=278064. [33] I. Bryn, A. Bjørnulf, S. Gedsø, L. Karlsen, Glass i fasader og solskjerming, 2014, Language: Norwegian http://www.erichsen-horgen.no/resources/Utgittveileder-Glass-i-fasader-og-Solskjerming.pdf. [34] H. Bülow-Hübe, Solavskärmning och dagsljuslänkning, Lunds Tekniska Högskola, Lund, 2007, Language: Swedish, EBD-R-07/15 http://belok.se/ download/projektarkiv/2007 2%20Dagsljus%20och%20solavskarmning.pdf. [35] L. Karlsen, P. Heiselberg, I. Bryn, H. Johra, Verification of simple illuminance based measures for indication of discomfort glare from windows, Build. Environ. 92 (2015) 615–626. [36] B. O’Neill, A. Tzempelikos, A. Athienitis, Daylight and luminaire control in a perimeter zone using an automatedvenetian blind, in: 2nd Canadian Solar Building Conference, Calgary, Canada, 2007. [37] O. Kalyanova, Double-skin facade—modelling and experimental investigations of thermal performance, in: PhD Thesis, Department of Civil Engineering, Aalborg University, 2008. [38] J. Le Dréau, Energy flow and thermal comfort in buildings—comparison of radiant and air-based heating & cooling systems, DCE Thesis No. 57, in: PhD Thesis, Department of Civil Engineering, Aalborg University, 2013. [39] Standardization, I.T.I.O.f., ISO 15099:2003(E) Thermal performance of windows, doors and shading devices—Detailed calculation. 2003: Switzerland. [40] P. Teknik, Thermal Manikins for Scientific and Industrial Use, 2015 http://ptteknik.dk/details. [41] O. Kalyanova, P. Heiselberg, Experimental Set-up and Full-scale measurements, in: The Cube’ in Technical Report, Aalborg University Aalborg University, DCE Technical Report No. 034, 2008. [42] A. Skartveit, J.A. Olseth, M.E. Tuft, An hourly diffuse fraction model with correction for variability and surface albedo, Solar Energy 63 (3) (1998) 173–183.

328

L. Karlsen et al. / Energy and Buildings 118 (2016) 316–328

[43] DMI, DMI—vejr, klima og hav—borgevejr, 2015 http://www.dmi.dk/vejr/ maalinger/borgervejr/. [44] Equa, IDA Indoor Climate and Energy, Equa Simulation Technology Group, 2011. [45] L. Karlsen, G. Grozman, P. Heiselberg, I. Bryn, Operative temperature and thermal comfort in the sun—implementation and verification of a model for IDA ICE, in: Indoor Air, Hong Kong,China, 2014. [46] L. Karlsen, G. Grozman, P. Heiselberg, I. Bryn, Integrated design of daylight, thermal comfort and energy demand with use of IDA ICE, in: Passivhus Norden—Sustainable Cities and Buildings, Copenhagen, Denmark, 2015. [47] C.J. Willmott, K. Matsuura, Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Clim. Res. 30 (2005) 79–82. [48] C. Reinhart, P.-F. Breton, Experimental Validation of Autodesk® 3ds Max® Design and Daysim 3. 0, LEUKOS 6 (1) (2009) 7–35. [49] DiBK, Veiledning om tekniske krav til byggverk, in HO-2/2011, in: D.f. byggkvalitet (ed), 2012: http://byggeregler.dibk.no/dxp/content/ tekniskekrav/. [50] IES, The Daylight Metrics Committee, Approved Method: IES Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE), Illuminating Engineering Society of North America: IES, New York, USA, 2012.

[51] J. Wienold, J. Christoffersen, Evaluation methods and development of a new glare prediction model for daylight environments with the use of CCD cameras, Energy Build. 38 (7) (2006) 743–757. [52] K. Konis, Predicting visual comfort in side-lit open-plan core zones: results of a field study pairing high dynamic range images with subjective responses, Energy Build. 77 (0) (2014) 67–79. [53] C.F. Reinhart, J. Wienold, The daylighting dashboard—a simulation-based design analysis for daylit spaces, Build. Environ. 46 (2) (2011) 386–396. [54] S. Grynning, A. Gustavsen, B. Time, Solar shading systems and thermal performance of windows in Nordic climates, in: 9th Nordic Symposium on Building Physics, Tampere University of Technology, Finland, 2011. [55] S.Y. Lee, J.L. Brand, Effects of control over office workspace on perceptions of the work environment and work outcomes, J. Environ. Psychol. 25 (3) (2005) 323–333. [56] J.A. Jakubiec, C. Reinhart, The Adaptive zone—a concept for assessing glare throughout daylit spaces, in: Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sidney, 2011.