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Optimal electrical circuiting layout and desk location for daylighting controlled spaces
Energy and Buildings 51 (2012) 122–130
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Optimal electrical circuiting layout and desk location for daylighting controlled spaces Donghyun Seo a , Lyool Park b , Pyeongchan Ihm c,∗ , Moncef Krarti d a
Building Energy Center of Energy Efficiency Research Division, Korea Institute of Energy Research, Daejeon, Republic of Korea Department of Building System Engineering, Dong-eui University, Busan, Republic of Korea c Department of Architectural Engineering, Dong-A University, Busan, Republic of Korea d Civil Environmental, and Architectural Engineering Department, University of Colorado, Boulder, CO 80309, United States b
a r t i c l e
i n f o
Article history: Received 6 April 2011 Received in revised form 26 January 2012 Accepted 19 April 2012 Keywords: Optimal lighting and daylight-based lighting control Optimal light circuiting Optimal desk location
a b s t r a c t A new electrical lighting and daylighting simulation analysis environment is developed to help designers assess optimal design configurations and operating strategies for electrical lighting fixtures in order to reduce energy use. In this paper, two applications of the simulation environment are presented to optimize the electrical lighting circuiting layouts design and the location of desks within daylight spaces. The results from these applications illustrate how the simulation environment and optimal daylight-base lighting controls can help building and lighting engineers and green building consultants improve the design of lighting and daylighting systems in order to construct and operate high energy performance buildings. © 2012 Elsevier B.V. All rights reserved.
1. Introduction To improve daylight-based lighting control simulation schemes in detailed building energy simulation tools and electric lighting controller performance in real buildings, a novel concept of daylight-based lighting controls has been proposed [1,2]. In depth review of electric lighting and daylight-based lighting controls and their modeling techniques in detailed building energy simulation tools are provided by Seo [1]. As an alternative to conventional daylight-based lighting control, Seo et al. [3] have developed an optimal daylight-based lighting control strategy. The new optimal control strategy is implemented and tested with a simulation environment that includes EnergyPlus, a detailed building energy analysis tool, and GenOpt, a tool with a suite of optimization techniques [4]. In particular, it was found when using actual lamp performance data, conventional daylight-based lighting control simulation models in detailed energy simulation tools could over-estimate electrical lighting energy savings [3]. The optimal daylight-based lighting control strategy could be utilized to model real lighting design options such as various lighting circuiting layout configurations and their performance. This feature of the new daylight-based lighting control module enables lighting system designers to evaluate the impact of designed lamps
∗ Corresponding author. E-mail address: [email protected] (P. Ihm). 0378-7788/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.enbuild.2012.04.020
and lighting circuiting alternatives on building energy consumption. By using actual lamp performance data, detailed building energy use analysis of daylight-based lighting controls can be assessed. Moreover, the optimization tool enables designers to select best desk placements for given configuration of lighting fixtures as well as to optimally place the lighting fixtures for the desired desk locations. In this paper, two applications of the optimal daylight-based lighting control module are described and evaluated using the simulation environment. These applications show how building and lighting designers can utilize the new tool to improve their lighting and daylighting system design in order to reduce energy use in buildings. The two applications include determining: (1) the optimal lighting circuiting configuration that is the most energy efficient and (2) the optimal desk location in a space with a pre-defined lighting design to achieve minimal electrical lighting energy use. The analysis details for each application are presented. Then, selected results are described and discussed for both applications. 2. Literature review Estimation of electric lighting and overall energy savings due to daylight-based lighting controls is difficult due to the complex interactions between electrical and natural lighting, solar heat gain through fenestration, and human factors. Field measurement [5–7]
D. Seo et al. / Energy and Buildings 51 (2012) 122–130
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and simulation studies [8–11] have shown that up to 60% of electric lighting energy savings can be achieved using properly designed daylight-based lighting control systems. However, only 10% of US commercial buildings actually utilize daylighting control systems although almost 50% of the buildings have energy-efficient lamps and ballasts [12]. Poor performance of actual daylight-based lighting controllers leading to significantly lower energy savings than predicted by energy simulation models is one of the common reasons of not installing these control systems. In addition, commonly used detailed building energy simulation tools are not capable of adequately modeling the actual performance of current daylightbased lighting control systems. Some of the problems identified in existing energy simulation tools to model daylight-based lighting control systems include: (1) temporal response variations of actual daylight systems are faster than simulation time-steps [13,14], (2) simplified outdoor illuminance and sky illuminance models in building energy simulation tools, (3) limited fenestration features that can be modeled [7,15], (4) human factors such variations in occupant behavior and lighting quality perception [16,17], (5) simplified indoor illuminance calculation models [18–21], and (6) limited electric lighting control schemes [3].
the shortcomings of the existing control models, a novel daylightbased lighting controller is integrated within EnergyPlus. The new controller considers the actual lamp performance to estimate the spatial illuminance distribution due to the electrical lighting system within an indoor space. Moreover, the new controller can determine the optimal settings for each fixture or lighting circuit within the space to maintain desired indoor illuminance levels at specific reference points such as workplaces (Seo et al. [3]). Eq. (2) describes the basic concept of the optimal daylight-based lighting controller for m workplaces and n lamps (i.e., electrical lighting fixtures or circuits). The indoor illuminance values for the m workplaces, Yai can be estimated based on outdoor illuminance levels and daylight factors. The required supplemental illuminance levels for the m workplaces, Yri , from the n lamps (or circuits) are determined as the differences between Yai values and the illuminance set-points desired for the workplaces. The first term of right hand side of Eq. (2) is introduced to control the lamps in detail. Using the actual performance data and the operation status for all the lamps, represented by the matrices A and LF, an error function, e, is introduced to select the best solutions that are able to maintain the illuminance levels on the workplaces at or close to the set-points as expressed by Eq. (2):
3. New optimal daylight-based lighting controller
⎡
A brief description of conventional and optimal daylight-based lighting controls is provided in this section. A more detailed description of the simulation environment of the tool is provided by Seo et al. [3]. 3.1. Lighting controls in building energy simulation tools The approaches of conventional building energy simulation tools to calculate outdoor illuminance, characterize building fenestration systems including shading devices, estimate indoor illuminance, and control electrical lighting systems are reviewed and described by Seo et al. [3]. For instance, lighting control strategies used in DOE-2 [18] and EnergyPlus [22] are modeled using the lighting power output fraction, fL , obtained from the difference between illuminance set-point and daylight illuminance at a reference location (typically, the workplane), iL , as expressed by Eq. (1):
fL (iL ) = max 0,
Iset (iL ) − IDL (iL ) Iset (iL )
(1)
where Iset is the illuminance set-point at iL ; IDL is the daylight illuminance at the reference location at iL .Eq. (1) assumes that the electrical lighting system operated at full input power produces an illuminance equal to Iset at the reference location. The fractional electrical lighting input power, fP , corresponding to fL is then calculated. The relationship between fP and fL depends on the lighting control type such as step control and dimming control. This basic operational scheme is also used in developing an optimal daylight-based lighting control strategy. However, the limitation of the number of reference point per zone imposed by the conventional daylight-based lighting control models is removed by the proposed optimal daylight-based lighting control strategies. For instance, only two reference points by zone (i.e., iL = 2) are allowed in both DOE-2 and EnergyPlus when modeling daylight-based lighting controls. 3.2. Optimized daylight-based lighting control concept Conventional daylight-based lighting control simulation tools are unable to model the actual electrical lighting system design, performance, and operation at various reference points. To remedy
e1
⎤ ⎡
⎢ ⎥ ⎢ e2 ⎥ ⎢ ⎥ ⎢ ··· ⎥ ⎣ ⎦ em
A1,1
⎢ ⎢ A2,1 ⎢ ⎢ ··· ⎣ Am,1
A1,2
···
A2,2
···
···
···
Am,2
···
⎤ ⎡ LF1 ⎤ ⎥ ⎢ LF2 ⎥ ⎥ A2,n ⎥ ⎢ ⎥ ⎥ ⎢ − ⎢ ⎥ .. ⎥ ⎥ ⎢ ··· ⎦ ⎣ . ⎦
A1,n
Am,n
LFn
⎡
Yr1
⎤
⎢ ⎥ ⎢ Yr2 ⎥ ⎢ ⎥ ⎢ ··· ⎥ ⎣ ⎦
(2)
Yrm
where ei represents the difference in the actual illuminance level provided by the electrical lighting fixtures (based on its operating settings) and the desired illuminance level from these fixtures (to complement daylighting to reach the setpoint level) for each workplace i. Aij is a matrix of m by n with Aij being the illuminance level provided by the lamp j on the workplace i. LFi is a vector of a dimension n with LFi being the status of the lamp i (LFi = 0 if the lamp i is switched off and LFi = 1 if the lamp is switched fully on). The possible values of LFi depend on the type of daylight-based lighting controls including on/off, stepped, and dimming controls. Yri is a vector of a dimension m with Yr,i being the supplemental illuminance level required from the electrical lighting system associated with the workplace i to reach the desired illuminance setpoint for the workplace i. Yr,i = max(0, Ys,i − Ya,i )
(2-a)
with Ys,i is the illuminance set point for the workplace i; Ya,i is the daylight illuminance level reaching the workplace i, which can be calculated by IO (outdoor illuminance) multiplied by DFi (the daylight factor associated with the workplace i).
⎡
Ya1
⎤
⎢ ⎥ ⎢ Ya2 ⎥ ⎢ ⎥ ⎢ · · · ⎥ = IO ⎣ ⎦ Yam
⎡
DF1
⎤
⎢ ⎥ ⎢ DF2 ⎥ ⎢ ⎥ ⎢ ··· ⎥ ⎣ ⎦
(2-b)
DFm
where IO is the outdoor illuminance; DFi is the daylight factor associated with workplace i. The objective of controlling individual lighting fixtures (or circuits) is to maximize the energy savings associated with the electrical lighting system, while maintaining the desired illuminance levels in the workplaces. Therefore, finding a solution to minimize the magnitude of the error function, E, defined by Eq. (3) is required. Since individual error ei could be negative (lower than set-point) value or positive (higher than set-point) value, the
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Table 1 Indoor space features. Building general Dimension Window-1 (west facing) Window-2 (west facing) Window-3 (west facing) WWR of the west facing wall Interior finish Interior wall finish Ceiling Floor finish Glazing (double pane low-e tinted w/6 mm air layer)
Table 2 Model input summary for evaluating the impact of circuiting layouts. 36 ft × 14 ft (11 m × 4.3 m) 9 ft × 4 ft (2.7 m × 1.2 m) 3 ft × 4 ft (0.8 m × 1.2 m) 8 ft × 4 ft (2.4 m × 1.2 m) 18.5%
Location Run period Number of control zone Lamp data used
9 ft (3 m) ceiling height 36 sf (3.3 m2 ) 12 sf (0.9 m2 ) 32 sf (2.9 m2 )
Optimized daylight-based lighting control
Golden, CO 19–25 of December 1–4 Layout 1, layout 2, layout 3, layout 4, layout 5 Dimming: GPSHJMS Glare control with interior blind
Paint field eggshell Suspended acoustical tile Carpet broadloom Solar transmittance
(Reflectance = 0.7) (Reflectance = 0.7)
Solar reflectance Visible transmittance
0.14 0.72
Lighting fixtures 11 EA AF1/18TRT/18 W/277V/clear/recessed SX-X-1T5HO-96W-OPEN 7 EA
Gotham, 3 m height
There are 18 lighting fixtures consisting of 11 recessed spot light and 7 fluorescent lamps. The indoor shading devices deploy when the glare index is over 20 or when the insolation level on the shading devices is over 200 W/m2 . The highest glare index is then selected from all the glare indices to deploy shading devices to maintain the glare index below 20. The illuminance set point at the reference locations is set to 500 lx.
Finelite, 2.4 m height
4. Application I – optimal lighting circuiting layout
(Reflectance = 0.12) 0.26
Daylight-based lighting control 13/609 LPD [W/m2 ]/total W Schedule On for 8 am to 17 pm Deploy when Glare index > 20 or Blind control insolation > 200 W/m2 Indoor illuminance 500 lx (set point)
squared sum of each ei is used for the minimized the total error E associate to all workplaces. This indicates that this optimization process will not only minimize visual comfort measured by the insufficiency in illuminance levels but also to minimize excessive electric lighting use.
m
1
E = min
ei
(3)
i=1
3.3. Optimal daylight-based lighting controller module and space model A daylight-based lighting optimization control module suitable for EnergyPlus is developed to model optimal daylight-based lighting control strategies. The module utilizes GenOpt to carry out the optimization analysis. Fig. 1 shows the developed module structure flowchart and an EnergyPlus screenshot that calls the module. A detailed description of the optimal lighting control module and its performance validation as well as comparative analysis against conventional daylight-based lighting control strategies with measured illuminance data can be found in Seo et al. [3]. 3.4. Indoor space model A conference room, part of an office building located in Golden, CO, is the indoor space model considered for the analysis conducted in this paper. Electric lighting design specifications such as lamp type, performance, and position are acquired from construction drawings. The lighting fixtures illuminance performance data are obtained from the AGI32 simulation tool [23]. Table 1 summarizes the input variables used for both EnergyPlus and AGI32 including space geometric dimensions, finishing material properties, lighting fixtures, and daylight-based lighting controls [3]. Fig. 2 shows a perspective 3-D view of the conference room space model as well as the position of the lighting fixtures and daylighting photosensors (i.e., reference points). The west oriented windows are made up of tinted double low-e glazing with indoor shading blinds that can be automatically controlled and operated.
To illustrate the application of the lighting and daylighting simulation environment to optimize the design of lighting circuiting layouts in buildings, a space with five possible lighting circuit layout options are considered as illustrated in Fig. 3. The input parameters required for the optimization simulation tool are summarized in Table 2. The optimization scheme used in dimming control is the General Pattern Search with Hooke–Jeeves and Multiple Start points (GPSHJMS) found to be the most efficient and accurate optimization method [3]. The lighting circuiting options in Fig. 3 indicate how the lamps are electrically wired. Each lighting circuit has its own controller. For instance, the lamps in circuiting layout 1 are all wired to the same circuit and are connected to one controller. Thus, the entire space is one lighting zone controlled by one controller/photosensor system in circuiting layout 1. In circuiting layout 4, there are three lighting circuits associated to 3 lighting zones. Each lighting zone is controlled by one controller. It should be noted that each lamp illuminance output affects lighting level in all three zones. For the analysis carried out in this section, it is assumed that the photosensor (i.e., the reference point) in each lighting zone is located in the middle of the zone as illustrated in Fig. 3. 4.1. Comparative analysis of lighting energy use For the space model outlined in Fig. 3 and Table 2, the electrical lighting energy use associated with various circuiting layout options is estimated with four window orientations. Fig. 4 presents average value of hourly optimal lighting output fraction found for the four window orientations considered in the analysis (south, north, west, and east). As expected, the results of Fig. 4 show that higher lighting energy savings are achieved when the windows are south facing and significantly lower savings are obtained when the windows are north facing. East and west oriented windows provide similar level of lighting energy savings which are between those obtained for south and north orientations. In terms of circuiting options, layout 2 generally does not produce higher lighting energy savings relative to layout 1, while layout 5 yields the highest lighting energy savings. In case of layout 2, the distance between two reference points is typically small so the expected savings are rather small even though horizontal circuiting on windows are commonly known to be an efficient switching method. However, layout 4 provides significant energy savings because the vertically zoned lighting circuit allowed higher flexibility in controlling various lighting fixtures.
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Fig. 1. Flowchart and integration within EnergyPlus of the optimization lighting controller module.
Fig. 2. 3-D South-West view and location of lighting fixtures (recessed spot lights: 1–5 and 13–18; fluorescent lamps: 6–12).
Table 3 compares lighting energy use savings obtained using optimized daylight-based lighting controls with various window orientations and circuiting layouts for a building space located in Golden, CO. As indicated in Fig. 4, the lighting energy savings due to daylight-based lighting controls are the highest (60.5%) in layout 5 when the windows are south facing. When the windows are north facing, lighting energy savings ranging from 0.2 to 36.1% are obtained depending on the circuiting layouts. For east and west orientated windows, lighting energy savings of 16.8–46.8% can be achieved. The results outlined in Table 3 indicate that layout 5 (with four independently controlled circuits/zones) provides the most energy savings up to 36% relative to layout 1 (with only 1
circuit/zone). For layout 2, the lighting energy savings relative to layout 1 range from 1.3 to 5.9% depending on the orientation of the windows. It should be noted that daylight-based lighting control performance for layouts 4 and 5 are similar for east and west orientations. However, layout 5 outperforms layout 4 for both south and north orientations. When the space is located in Tampa, FL, similar results found for Golden, CO are obtained as outlined in Table 4. Based on the results of a series of parametric analyses summarized in Tables 3 and 4, one can conclude that more circuiting zones (4 in case of circuiting layout 5) would yield higher lighting energy use savings. The common assumption that lighting zoning horizontally with perimeter
Table 3 Summary of parametric simulation results for 4 window orientations and 5 circuiting layouts (Golden, CO).
Table 4 Summary of parametric simulation results for 4 window orientations and 5 circuiting layouts (Tampa, FL).
Layout 1
Layout 2
Layout 3
Layout 4
Layout 5
(a) Total lighting energy use (kWh) for December 19–25 31.1 29.3 27.4 23.1 22.7 West 42.5 41.7 39.4 31.2 27.2 North 35.5 35.0 31.2 26.1 25.3 East 24.8 23.5 20.6 19.0 16.8 South (b) Lighting energy saving relative to no daylight-based lighting control 27.1% 31.3% 35.7% 45.9% 46.8% West 0.2% 2.2% 7.5% 26.7% 36.1% North 16.8% 17.8% 26.7% 38.8% 40.7% East 41.9% 44.9% 51.7% 55.4% 60.5% South (c) Lighting energy saving relative to circuiting layout 1 – 5.9% 11.8% 25.8% 27.1% West 2.1% 7.4% 26.6% 36.0% North – – 1.3% 11.9% 26.5% 28.7% East 5.3% 17.0% 23.3% 32.1% South –
Layout 1
Layout 2
Layout 3
Layout 4
Layout 5
(a) Total lighting energy use (kWh) for December 19–25 36.0 34.6 32.9 26.3 25.8 West 41.2 39.6 36.5 31.1 25.1 North 38.3 37.1 34.5 27.1 26.7 East 25.3 24.2 21.4 17.2 13.9 South (b) Lighting energy saving relative to no daylight-based lighting control 15.5% 18.7% 22.9% 38.3% 39.4% West 3.4% 6.9% 14.3% 27.0% 41.1% North 10.2% 12.9% 19.0% 36.3% 37.3% East 40.7% 43.3% 49.8% 59.7% 67.4% South (c) Lighting energy saving relative to circuiting layout 1 – 3.8% 8.7% 27.0% 28.2% West 3.7% 11.2% 24.4% 39.0% North – – 3.1% 9.8% 29.1% 30.2% East 4.4% 15.3% 32.0% 45.0% South –
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Fig. 3. Lighting circuiting layout options with location of reference point (
1.2
Layout1
Layout2
Layout3
Layout4
Layout5
1 0.8 0.6 0.4 0.2 0
8
9
1.4 Lighng Output Fracon
Lighng Output Fracon
1.4
1.2
Layout1
Layout2
Layout3
Layout4
Layout5
1 0.8 0.6 0.4 0.2 0
10 11 12 13 14 15 16 17 Dec 21 (Hour)
8
9
10 11 12 13 14 15 16 17 Dec 21 (Hour)
(a) East 1.2
(b) North
Layout1
Layout2
Layout3
Layout4
Layout5
1 0.8 0.6 0.4 0.2 0
8
9
1.4 Lighng Output Fracon
Lighng Output Fracon
1.4
10 11 12 13 14 15 16 17 Dec 21 (Hour)
(c) South
).
1.2
Layout1
Layout2
Layout3
Layout4
Layout5
1 0.8 0.6 0.4 0.2 0
8
9
10 11 12 13 14 15 16 17 Dec 21 (Hour)
(d) West
Fig. 4. Lighting output fraction associated with five circuiting layout options and four window orientations during December 21 in Golden, CO.
D. Seo et al. / Energy and Buildings 51 (2012) 122–130
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y = 5.5) satisfy the set-point requirement of 500-lx and the illuminance distribution is more uniform than that obtained from only natural lighting as shown in Fig. 3(a). Fig. 3(c) indicates that if the space can have several lighting control zones, the indoor illuminance distribution can be improved to be more uniform while the illuminance levels in most zones are close to set-point of illuminance.
4.3. Summary The impact of lighting circuiting layout on lighting energy use by optimal daylight-based lighting control is estimated with several lighting circuiting layout options. The new lighting control method enables designers and engineers to verify the impact of various lighting design and circuits with daylight-based lighting control. The lighting circuiting layouts test results show that even when the space is located along the perimeter areas dividing lighting circuiting zones vertically as outlined in circuiting layouts 3 or 4 relative to the exterior wall can result in higher energy savings with optimal daylight-based lighting controls.
5. Application II – optimal desk location 5.1. Problem formulation
Fig. 5. Indoor illuminance distribution (lx) of six grid points within the space (x and y indicate distance in meter from right-lower origin point of the space in Fig. 3).
(i.e., circuiting layout 2) would save more lighting energy use than vertical zoning (i.e., circuiting layout 3) does not always apply when optimal lighting control is considered. 4.2. Lighting control quality evaluation One additional benefit of the optimal daylight-based lighting control method is the ability to calculate indoor illuminance level by combining the contributions from natural lighting and from artificial lighting. This combined lighting level can be used for closeloop feedback daylight-based lighting control. For any time step, the indoor daylight illuminance can be estimated from EnergyPlus illuminance map data. Using the daylight-based lighting control optimization algorithm, the lighting output fraction provided by each lamp can be obtained. Therefore, if the illuminance data of all lamps are determined for any arbitrary point in the space, then the illuminance from the electrical lighting system can be derived for any location of the space using Eq. (3) as outlined in Seo et al. [3]. The indoor illuminance at each time step can be calculated by summing the illuminance levels obtained from natural lighting and those provided by the electrical lighting system. Fig. 5 illustrates distributions of summed typical daylighting and electrical lighting system illuminance levels of the space at 3:00 pm on December 21. The x and y grid points in Fig. 5 indicate the distances (in meter) from south-west bottom corner of the space. The illuminance levels from daylighting only, provided in Fig. 5(a), show an uneven distribution with high values near the windows. Fig. 3(b) presents the indoor illuminance distribution associated to both daylighting and electrical lighting as the results of using the optimized dimming lighting controls using circuiting layout 2. The illuminance levels at the reference points (x = 1.4, y = 5.5; x = 2.8,
This application of the optimal daylight-based lighting control simulation environment focuses on effectively selecting the best locations of desks (or workplaces) within a room to benefit from daylighting and reduce electrical lighting energy use. Two optimization sequences are carried out within the simulation environment to determine the desk locations and the optimal electrical lighting settings. Indeed, the first optimization is used to select the desk locations that minimize the lighting annual energy use for the entire space. Then, a second optimization determines the best settings for each lamp during each time step to minimize the lighting output. For a space with p possible desk locations and q desks to place in the space, the total possible desks layout options r can be calculated by Eq. (4) r=
p! q! (p − q)!
(4)
In an open office space, for example, there are 50 possible desk locations and 10 desks to be placed, then the total number of possible desk layout options is over ten millions. To illustrate this application, a small office space is considered with 6 possible desk locations with 2 desks to be placed. The space model details and the results of the optimization tool are outlined in the following sections.
5.2. Space model description For the same space model of Fig. 2 with lighting circuiting layout 2 of Fig. 3, it is assumed that two desks are to be placed in six possible desk location options. Therefore, a total of 15 possible desk layout options are feasible as noted by Eq. (4) with p = 6 and q = 2. Fig. 6 and Table 5 presents respectively, the possible 6 desk locations and the 15 desk layout options. For the analysis conducted in this section, it is assumed that the desks are located in the center of each location zones. Two orientations and two seasons are considered in the analysis performed in this section.
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Opon 1:
-1.2%
Opon 2:
-10.1%
Opon 3:
-14 .7%
Opon 4:
2.9%
Opon 5:
-9.6%
Opon 6:
-1.1%
Opon 7:
-0.5%
Opon 8:
0.0%
Opon 9:
-0.7%
Opon 10:
-9.5%
Opon 11 :
-0.4%
Opon 12:
-20 .9%
Opon 13:
3.7%
Opon 14 :
-9.1%
Opon 15:
-0.1%
Fig. 6. Six possible desk locations. Table 5 Potential 15 desk layout options. Options Layout 1 Layout 2 Layout 3 Layout 4 Layout 5
Zone (1)–(2) (1)–(3) (1)–(4) (1)–(5) (1)–(6)
Options Layout 6 Layout 7 Layout 8 Layout 9 Layout 10
Zone (2)–(3) (2)–(4) (2)–(5) (2)–(6) (3)–(4)
Options Layout 11 Layout 12 Layout 13 Layout 14 Layout 15
Zone (3)–(5) (3)–(6) (4)–(5) (4)–(6) (5)–(6)
N Fig. 8. Lighting energy use for several desk placement options for west orientation and during summer (the dark spots indicate actual locations for 2 desks).
5.3. Comparison of lighting energy use 5.3.1. West orientation – winter A series of energy simulations using actual lamp performance data are carried out for the small office space of Fig. 6 located in Golden, CO when the windows are oriented west during winter (week of December 19–25). Fig. 7 illustrates the lighting energy savings expressed relative to electrical lighting energy use for the desk layout 8, which is the most common option used for daylightbased lighting control analysis (i.e., reference points assumed to be close to the windows). As expected, desk placement options 3, 12 and 5 (dotted line) where the desks are placed in the corner of the space consume significantly more lighting energy by up to 9.8% relative to layout 8. In the other hand, desk location layouts 13 and 6 are favorable desk location options with slightly higher or similar energy use savings than that of layout 8. 5.3.2. West orientation – summer A similar analysis carried in the previous section is performed for the same space when the windows are oriented west but during the summer (week of June 19–25). Fig. 8 summarizes the lighting energy savings relative to the desk layout 8. Desk placement layouts 12, 3 and 2 (dotted line) result in significantly more lighting energy
use by up to 20.9% relative to layout 8. However, layouts 13 and 4 provide higher energy savings for the daylight-based lighting controls compared to desk location layout 8. Taking into account the results obtained for both seasons, placing the two desks according to layout 13 results in the highest energy savings when optimized daylight-based lighting controls are implemented in a space with west oriented windows located in Golden, CO.
5.3.3. South orientation – winter The analysis is repeated for the same space of Fig. 4 but with the windows orientation to south. First, the analysis is performed for a winter week (December 19–25). Fig. 9 presents the lighting energy savings obtained for all possible layouts relative to the desk placement layout 8. In this case, layouts 1, 3, 4, 7 and 13 consume more lighting energy than layout 8 by up to 10.6%. Desk layouts 2, 5, 6, 9–12, 14, and 15 provide lighting energy use savings relative to layout 8.
Opon 1:
-1.2%
Opon 2:
-5.1%
Opon 3:
-9.8%
Opon 1:
-2.8%
Opon 2:
9.4%
Opon 3:
-10 .6%
Opon 4:
-0.1%
Opon 5:
-5.7%
Opon 6:
0.0%
Opon 4:
-5.5%
Opon 5:
7.3%
Opon 6:
17.7%
Opon 7:
-0.8%
Opon 8:
0.0%
Opon 9:
-0.9%
Opon 7:
-2.8%
Opon 8:
0.0%
Opon 9:
11.4%
Opon 10:
-5.6%
Opon 11 :
-0.8%
Opon 12:
-9.6%
Opon 10:
8.5%
Opon 11:
15.5%
Opon 12:
4.2%
Opon 13:
0.3%
Opon 14 :
-5.2%
Opon 15:
-0.6%
Opon 13:
-5.5%
Opon 14:
6.5%
N Fig. 7. Lighting energy use for several desk placement options for west orientation and during winter (the dark spots indicate actual locations for 2 desks).
Opon 15: 10.2%
N Fig. 9. Lighting energy use for several desk placement options for south orientation and during winter (the dark spots indicate actual locations for 2 desks).
D. Seo et al. / Energy and Buildings 51 (2012) 122–130
Opon 1:
-6.1%
Opon 2:
20.9%
Opon 3:
-13 .0%
Opon 4:
-4.9%
Opon 5:
21.7%
Opon 6:
27.2%
Opon 7:
-6.1%
Opon 8:
0.0%
Opon 9:
29.4%
Opon 10:
20.6%
Opon 11:
25.8%
Opon 12: 18.2%
Opon 13:
-4.9%
Opon 14:
21.8%
Opon 15: 28.7%
N Fig. 10. Lighting energy use for several desk placement options for south orientation and during summer (the dark spots indicate actual locations for 2 desks).
5.3.4. South orientation – summer A similar analysis carried in the previous section is performed for the same space when the windows are oriented south during summer (week of June 19–25). Fig. 10 illustrates lighting energy savings for all desk layouts relative to the desk placement layout 8. Desk layouts 1, 3, 4, 7, and 13 consume more lighting energy than layout 8 by up to 13.0%. Meanwhile, layouts 6, 9, 11, and 15 provide significantly higher energy savings. Desk placement layout 6 would be effective for both winter and summer seasons when the windows of the office space are oriented south and is therefore the recommended desk location option in terms of lighting energy saving potential for this orientation and season. 5.4. Summary In this section, the second application of the optimal daylightbased lighting control strategy and its implementation in wholebuilding simulation tool has been demonstrated. Specifically, it has been shown that the simulation tool with the optimal daylightbased lighting control is useful to help decision making on lighting design and interior space planning by providing detailed lighting and energy savings potential for various design options. Specifically, the simulation tool has identified the best desk locations among several potential options that provide the highest energy use savings when optimal daylight-based lighting controls are implemented in an office space. In particular, it is found that optimal desk locations are highly dependent on window orientation and season. However, desk placement options that are suitable throughout the year are feasible for a given window orientation. For instance, it is found in the analysis conducted and presented in this paper for a small office space located in Golden, CO, that the optimal desk location is in the east-north corner of the space for south window orientations considered in the analysis (layout 6 of Figs. 9 and 10 for south window orientation) and west-north corner of the space for west window orientation (layout 13 of Figs. 5 and 6 for west window orientation). 6. Conclusions A novel and powerful optimal daylight-based lighting controller has been developed, modeled, and evaluated. In this study, two applications have been demonstrated among the various potential usage of the whole-building energy simulation tool in which the
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optimal controller is integrated. In the first application, the simulation tool found the best circuiting layout option to minimize the energy use required for lighting. For the second application, the simulation tool was applied to find the optimal; desk location layout within a space that reduces the reliance on electrical lighting to maintain design indoor illuminance in work spaces. The developed optimal daylight-based lighting controller integrated in EnergyPlus, a whole-building energy simulation analysis program, can be used for several other applications. For instance, the simulation tool can be used to design window placement and select the best lighting fixtures for a given space based on the potential energy use savings associated with optimized daylight-based lighting control strategies outlined discussed in this paper. The main benefit of the optimization/simulation tool is its capability to consider actual lighting system performance and contribution to maintain desired indoor illuminance levels. As it has been shown in a companion paper by Seo et al. [3], the lack of this capability is the main reason that most building energy simulation programs overestimate the lighting energy use savings associated with daylight-based lighting controls. Acknowledgements This work is a part of Development of Core Technologies for Smart Green Building research funded by the KIER (Korea Institute of Energy Research) and KME (Korea government Ministry of Knowledge Economy) of South Korea. References [1] D. Seo, Development of a universal model for predicting hourly solar radiation – application: evaluation of an optimal daylighting controller, Ph.D. Dissertation, University of Colorado-Boulder, USA, 2010. [2] S. Beldi, M. Krarti, Genetic algorithm based daylight controller, in: Proceedings of the Inaugural US–EU–China, Thermophysics Conference UECTC-RE, Beijing, China, 2009. [3] D. Seo, P. Ihm, M. Krarti, Development of an optimal daylighting controller, Building and Environment 46 (5) (2011) 1011–1022. [4] M. Wetter, GenOpt® Generic Optimization program. User manual version 2.1.1. Technical report LBNL-54199. Building Technologies Program, Simulation Research Group, Lawrence Berkeley National Laboratory, 2008. [5] M.R. Atif, A.D. Galasiu, Energy performance of daylight-linked automatic lighting control systems in large atrium spaces: report on two field-monitored case studies, Energy and Buildings 35 (5) (2003) 441–461. [6] H.W. Danny, Li. Tony, N.T. Lam, S.L. Wong, Lighting and energy performance for an office using high frequency dimming controls, Energy Conversion and Management 47 (9–10) (2006) 1133–1145. [7] E.S. Lee, S.E. Selkowitz, The New York Times Headquarters daylighting mockup: monitored performance of the daylighting control system, Energy and Buildings, Special Issue on Daylighting Buildings 38 (7) (2006) 914–929. [8] B. Bjorn, H.H. Eilif, Energy savings in lighting installations by utilizing daylight, CADDET Energy Efficiency, Newsletter 1 (1997). [9] M. Jonathan, J.B. Patrick, C.H. Doug, The energy impact of daylighting, ASHRAE Journal 40 (5) (1998) 31–35. [10] D.H.W. Li, J.C. Lam, Evaluation of lighting performance in office buildings with daylighting controls, Energy and Buildings 33 (2001) 793–803. [11] K. Moncef, M.E. Paul, C.H. Timothy, A simplified method to estimate energy savings of artificial lighting use from daylighting, Building and Environment 40 (6) (2005) 747–754. [12] K. Moncef, Energy Audit of Building Systems: An Engineering Approach, 2nd edition, CRC Press and Francis and Taylor Group, Boston, MA, 2010. [13] J. Milan, Coupling building energy and lighting simulation, in: 5th International IBPSA Conference, vol. 2, Prague, 1997, pp. 313–319. [14] W. Oliver, L. Joachim, R. Christoph, T. Jens, Dynamic annual daylight simulations based on one hour and one-minute means of irradiance data, Solar Energy 72 (5) (2002) 385–395. [15] K.-L. Martin, An overview of daylighting systems, Solar Energy 73 (2) (2002) 77–82. [16] T. Inoue, T. Kawase, T. Ibamoto, S. Takakusa, Y. Matsuo, The development of an optimal control system for window shading devices based on investigations in office buildings, ASHRAE Transactions 104 (1988) 1034–1049. [17] C. Reinhart, K. Voss, Monitoring manual control of electric lighting and blinds, Lighting Research & Technology 35 (3) (2003) 243–260. [18] F.C. Winkelmann, S. Selkowitz, Daylighting simulation in the DOE-2 building energy analysis program, Energy and Buildings 8 (1985) 271–286. [19] P.R. Tregenza, I.M. Waters, Daylight coefficients, Lighting Research and Technology 15 (2) (1983) 65–72.
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[20] C.F. Reinhart, O. Walkenhorst, Dynamic RADIANCE-based daylight simulations for a full scale test office with outer venetian blinds, Energy and Buildings 33 (7) (2001) 683–6971. [21] C.F. Reinhart, M. Andersen, Development and validation of a radiance model for a translucent panel, Energy and Buildings 38 (7) (2006) 890–904.
[22] UIUC, LBNL, EnergyPlus Engineering Reference: the Reference to EnergyPlus Calculations, U.S. Department of Energy, 2009. [23] Dau Design and Consulting Inc., Validation of AGi32 against CIE 171:2006, Dau Design and Consulting, Inc., Vancouver, Canada, 2007.
Contents lists available at SciVerse ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Optimal electrical circuiting layout and desk location for daylighting controlled spaces Donghyun Seo a , Lyool Park b , Pyeongchan Ihm c,∗ , Moncef Krarti d a
Building Energy Center of Energy Efficiency Research Division, Korea Institute of Energy Research, Daejeon, Republic of Korea Department of Building System Engineering, Dong-eui University, Busan, Republic of Korea c Department of Architectural Engineering, Dong-A University, Busan, Republic of Korea d Civil Environmental, and Architectural Engineering Department, University of Colorado, Boulder, CO 80309, United States b
a r t i c l e
i n f o
Article history: Received 6 April 2011 Received in revised form 26 January 2012 Accepted 19 April 2012 Keywords: Optimal lighting and daylight-based lighting control Optimal light circuiting Optimal desk location
a b s t r a c t A new electrical lighting and daylighting simulation analysis environment is developed to help designers assess optimal design configurations and operating strategies for electrical lighting fixtures in order to reduce energy use. In this paper, two applications of the simulation environment are presented to optimize the electrical lighting circuiting layouts design and the location of desks within daylight spaces. The results from these applications illustrate how the simulation environment and optimal daylight-base lighting controls can help building and lighting engineers and green building consultants improve the design of lighting and daylighting systems in order to construct and operate high energy performance buildings. © 2012 Elsevier B.V. All rights reserved.
1. Introduction To improve daylight-based lighting control simulation schemes in detailed building energy simulation tools and electric lighting controller performance in real buildings, a novel concept of daylight-based lighting controls has been proposed [1,2]. In depth review of electric lighting and daylight-based lighting controls and their modeling techniques in detailed building energy simulation tools are provided by Seo [1]. As an alternative to conventional daylight-based lighting control, Seo et al. [3] have developed an optimal daylight-based lighting control strategy. The new optimal control strategy is implemented and tested with a simulation environment that includes EnergyPlus, a detailed building energy analysis tool, and GenOpt, a tool with a suite of optimization techniques [4]. In particular, it was found when using actual lamp performance data, conventional daylight-based lighting control simulation models in detailed energy simulation tools could over-estimate electrical lighting energy savings [3]. The optimal daylight-based lighting control strategy could be utilized to model real lighting design options such as various lighting circuiting layout configurations and their performance. This feature of the new daylight-based lighting control module enables lighting system designers to evaluate the impact of designed lamps
∗ Corresponding author. E-mail address: [email protected] (P. Ihm). 0378-7788/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.enbuild.2012.04.020
and lighting circuiting alternatives on building energy consumption. By using actual lamp performance data, detailed building energy use analysis of daylight-based lighting controls can be assessed. Moreover, the optimization tool enables designers to select best desk placements for given configuration of lighting fixtures as well as to optimally place the lighting fixtures for the desired desk locations. In this paper, two applications of the optimal daylight-based lighting control module are described and evaluated using the simulation environment. These applications show how building and lighting designers can utilize the new tool to improve their lighting and daylighting system design in order to reduce energy use in buildings. The two applications include determining: (1) the optimal lighting circuiting configuration that is the most energy efficient and (2) the optimal desk location in a space with a pre-defined lighting design to achieve minimal electrical lighting energy use. The analysis details for each application are presented. Then, selected results are described and discussed for both applications. 2. Literature review Estimation of electric lighting and overall energy savings due to daylight-based lighting controls is difficult due to the complex interactions between electrical and natural lighting, solar heat gain through fenestration, and human factors. Field measurement [5–7]
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and simulation studies [8–11] have shown that up to 60% of electric lighting energy savings can be achieved using properly designed daylight-based lighting control systems. However, only 10% of US commercial buildings actually utilize daylighting control systems although almost 50% of the buildings have energy-efficient lamps and ballasts [12]. Poor performance of actual daylight-based lighting controllers leading to significantly lower energy savings than predicted by energy simulation models is one of the common reasons of not installing these control systems. In addition, commonly used detailed building energy simulation tools are not capable of adequately modeling the actual performance of current daylightbased lighting control systems. Some of the problems identified in existing energy simulation tools to model daylight-based lighting control systems include: (1) temporal response variations of actual daylight systems are faster than simulation time-steps [13,14], (2) simplified outdoor illuminance and sky illuminance models in building energy simulation tools, (3) limited fenestration features that can be modeled [7,15], (4) human factors such variations in occupant behavior and lighting quality perception [16,17], (5) simplified indoor illuminance calculation models [18–21], and (6) limited electric lighting control schemes [3].
the shortcomings of the existing control models, a novel daylightbased lighting controller is integrated within EnergyPlus. The new controller considers the actual lamp performance to estimate the spatial illuminance distribution due to the electrical lighting system within an indoor space. Moreover, the new controller can determine the optimal settings for each fixture or lighting circuit within the space to maintain desired indoor illuminance levels at specific reference points such as workplaces (Seo et al. [3]). Eq. (2) describes the basic concept of the optimal daylight-based lighting controller for m workplaces and n lamps (i.e., electrical lighting fixtures or circuits). The indoor illuminance values for the m workplaces, Yai can be estimated based on outdoor illuminance levels and daylight factors. The required supplemental illuminance levels for the m workplaces, Yri , from the n lamps (or circuits) are determined as the differences between Yai values and the illuminance set-points desired for the workplaces. The first term of right hand side of Eq. (2) is introduced to control the lamps in detail. Using the actual performance data and the operation status for all the lamps, represented by the matrices A and LF, an error function, e, is introduced to select the best solutions that are able to maintain the illuminance levels on the workplaces at or close to the set-points as expressed by Eq. (2):
3. New optimal daylight-based lighting controller
⎡
A brief description of conventional and optimal daylight-based lighting controls is provided in this section. A more detailed description of the simulation environment of the tool is provided by Seo et al. [3]. 3.1. Lighting controls in building energy simulation tools The approaches of conventional building energy simulation tools to calculate outdoor illuminance, characterize building fenestration systems including shading devices, estimate indoor illuminance, and control electrical lighting systems are reviewed and described by Seo et al. [3]. For instance, lighting control strategies used in DOE-2 [18] and EnergyPlus [22] are modeled using the lighting power output fraction, fL , obtained from the difference between illuminance set-point and daylight illuminance at a reference location (typically, the workplane), iL , as expressed by Eq. (1):
fL (iL ) = max 0,
Iset (iL ) − IDL (iL ) Iset (iL )
(1)
where Iset is the illuminance set-point at iL ; IDL is the daylight illuminance at the reference location at iL .Eq. (1) assumes that the electrical lighting system operated at full input power produces an illuminance equal to Iset at the reference location. The fractional electrical lighting input power, fP , corresponding to fL is then calculated. The relationship between fP and fL depends on the lighting control type such as step control and dimming control. This basic operational scheme is also used in developing an optimal daylight-based lighting control strategy. However, the limitation of the number of reference point per zone imposed by the conventional daylight-based lighting control models is removed by the proposed optimal daylight-based lighting control strategies. For instance, only two reference points by zone (i.e., iL = 2) are allowed in both DOE-2 and EnergyPlus when modeling daylight-based lighting controls. 3.2. Optimized daylight-based lighting control concept Conventional daylight-based lighting control simulation tools are unable to model the actual electrical lighting system design, performance, and operation at various reference points. To remedy
e1
⎤ ⎡
⎢ ⎥ ⎢ e2 ⎥ ⎢ ⎥ ⎢ ··· ⎥ ⎣ ⎦ em
A1,1
⎢ ⎢ A2,1 ⎢ ⎢ ··· ⎣ Am,1
A1,2
···
A2,2
···
···
···
Am,2
···
⎤ ⎡ LF1 ⎤ ⎥ ⎢ LF2 ⎥ ⎥ A2,n ⎥ ⎢ ⎥ ⎥ ⎢ − ⎢ ⎥ .. ⎥ ⎥ ⎢ ··· ⎦ ⎣ . ⎦
A1,n
Am,n
LFn
⎡
Yr1
⎤
⎢ ⎥ ⎢ Yr2 ⎥ ⎢ ⎥ ⎢ ··· ⎥ ⎣ ⎦
(2)
Yrm
where ei represents the difference in the actual illuminance level provided by the electrical lighting fixtures (based on its operating settings) and the desired illuminance level from these fixtures (to complement daylighting to reach the setpoint level) for each workplace i. Aij is a matrix of m by n with Aij being the illuminance level provided by the lamp j on the workplace i. LFi is a vector of a dimension n with LFi being the status of the lamp i (LFi = 0 if the lamp i is switched off and LFi = 1 if the lamp is switched fully on). The possible values of LFi depend on the type of daylight-based lighting controls including on/off, stepped, and dimming controls. Yri is a vector of a dimension m with Yr,i being the supplemental illuminance level required from the electrical lighting system associated with the workplace i to reach the desired illuminance setpoint for the workplace i. Yr,i = max(0, Ys,i − Ya,i )
(2-a)
with Ys,i is the illuminance set point for the workplace i; Ya,i is the daylight illuminance level reaching the workplace i, which can be calculated by IO (outdoor illuminance) multiplied by DFi (the daylight factor associated with the workplace i).
⎡
Ya1
⎤
⎢ ⎥ ⎢ Ya2 ⎥ ⎢ ⎥ ⎢ · · · ⎥ = IO ⎣ ⎦ Yam
⎡
DF1
⎤
⎢ ⎥ ⎢ DF2 ⎥ ⎢ ⎥ ⎢ ··· ⎥ ⎣ ⎦
(2-b)
DFm
where IO is the outdoor illuminance; DFi is the daylight factor associated with workplace i. The objective of controlling individual lighting fixtures (or circuits) is to maximize the energy savings associated with the electrical lighting system, while maintaining the desired illuminance levels in the workplaces. Therefore, finding a solution to minimize the magnitude of the error function, E, defined by Eq. (3) is required. Since individual error ei could be negative (lower than set-point) value or positive (higher than set-point) value, the
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Table 1 Indoor space features. Building general Dimension Window-1 (west facing) Window-2 (west facing) Window-3 (west facing) WWR of the west facing wall Interior finish Interior wall finish Ceiling Floor finish Glazing (double pane low-e tinted w/6 mm air layer)
Table 2 Model input summary for evaluating the impact of circuiting layouts. 36 ft × 14 ft (11 m × 4.3 m) 9 ft × 4 ft (2.7 m × 1.2 m) 3 ft × 4 ft (0.8 m × 1.2 m) 8 ft × 4 ft (2.4 m × 1.2 m) 18.5%
Location Run period Number of control zone Lamp data used
9 ft (3 m) ceiling height 36 sf (3.3 m2 ) 12 sf (0.9 m2 ) 32 sf (2.9 m2 )
Optimized daylight-based lighting control
Golden, CO 19–25 of December 1–4 Layout 1, layout 2, layout 3, layout 4, layout 5 Dimming: GPSHJMS Glare control with interior blind
Paint field eggshell Suspended acoustical tile Carpet broadloom Solar transmittance
(Reflectance = 0.7) (Reflectance = 0.7)
Solar reflectance Visible transmittance
0.14 0.72
Lighting fixtures 11 EA AF1/18TRT/18 W/277V/clear/recessed SX-X-1T5HO-96W-OPEN 7 EA
Gotham, 3 m height
There are 18 lighting fixtures consisting of 11 recessed spot light and 7 fluorescent lamps. The indoor shading devices deploy when the glare index is over 20 or when the insolation level on the shading devices is over 200 W/m2 . The highest glare index is then selected from all the glare indices to deploy shading devices to maintain the glare index below 20. The illuminance set point at the reference locations is set to 500 lx.
Finelite, 2.4 m height
4. Application I – optimal lighting circuiting layout
(Reflectance = 0.12) 0.26
Daylight-based lighting control 13/609 LPD [W/m2 ]/total W Schedule On for 8 am to 17 pm Deploy when Glare index > 20 or Blind control insolation > 200 W/m2 Indoor illuminance 500 lx (set point)
squared sum of each ei is used for the minimized the total error E associate to all workplaces. This indicates that this optimization process will not only minimize visual comfort measured by the insufficiency in illuminance levels but also to minimize excessive electric lighting use.
m
1
E = min
ei
(3)
i=1
3.3. Optimal daylight-based lighting controller module and space model A daylight-based lighting optimization control module suitable for EnergyPlus is developed to model optimal daylight-based lighting control strategies. The module utilizes GenOpt to carry out the optimization analysis. Fig. 1 shows the developed module structure flowchart and an EnergyPlus screenshot that calls the module. A detailed description of the optimal lighting control module and its performance validation as well as comparative analysis against conventional daylight-based lighting control strategies with measured illuminance data can be found in Seo et al. [3]. 3.4. Indoor space model A conference room, part of an office building located in Golden, CO, is the indoor space model considered for the analysis conducted in this paper. Electric lighting design specifications such as lamp type, performance, and position are acquired from construction drawings. The lighting fixtures illuminance performance data are obtained from the AGI32 simulation tool [23]. Table 1 summarizes the input variables used for both EnergyPlus and AGI32 including space geometric dimensions, finishing material properties, lighting fixtures, and daylight-based lighting controls [3]. Fig. 2 shows a perspective 3-D view of the conference room space model as well as the position of the lighting fixtures and daylighting photosensors (i.e., reference points). The west oriented windows are made up of tinted double low-e glazing with indoor shading blinds that can be automatically controlled and operated.
To illustrate the application of the lighting and daylighting simulation environment to optimize the design of lighting circuiting layouts in buildings, a space with five possible lighting circuit layout options are considered as illustrated in Fig. 3. The input parameters required for the optimization simulation tool are summarized in Table 2. The optimization scheme used in dimming control is the General Pattern Search with Hooke–Jeeves and Multiple Start points (GPSHJMS) found to be the most efficient and accurate optimization method [3]. The lighting circuiting options in Fig. 3 indicate how the lamps are electrically wired. Each lighting circuit has its own controller. For instance, the lamps in circuiting layout 1 are all wired to the same circuit and are connected to one controller. Thus, the entire space is one lighting zone controlled by one controller/photosensor system in circuiting layout 1. In circuiting layout 4, there are three lighting circuits associated to 3 lighting zones. Each lighting zone is controlled by one controller. It should be noted that each lamp illuminance output affects lighting level in all three zones. For the analysis carried out in this section, it is assumed that the photosensor (i.e., the reference point) in each lighting zone is located in the middle of the zone as illustrated in Fig. 3. 4.1. Comparative analysis of lighting energy use For the space model outlined in Fig. 3 and Table 2, the electrical lighting energy use associated with various circuiting layout options is estimated with four window orientations. Fig. 4 presents average value of hourly optimal lighting output fraction found for the four window orientations considered in the analysis (south, north, west, and east). As expected, the results of Fig. 4 show that higher lighting energy savings are achieved when the windows are south facing and significantly lower savings are obtained when the windows are north facing. East and west oriented windows provide similar level of lighting energy savings which are between those obtained for south and north orientations. In terms of circuiting options, layout 2 generally does not produce higher lighting energy savings relative to layout 1, while layout 5 yields the highest lighting energy savings. In case of layout 2, the distance between two reference points is typically small so the expected savings are rather small even though horizontal circuiting on windows are commonly known to be an efficient switching method. However, layout 4 provides significant energy savings because the vertically zoned lighting circuit allowed higher flexibility in controlling various lighting fixtures.
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Fig. 1. Flowchart and integration within EnergyPlus of the optimization lighting controller module.
Fig. 2. 3-D South-West view and location of lighting fixtures (recessed spot lights: 1–5 and 13–18; fluorescent lamps: 6–12).
Table 3 compares lighting energy use savings obtained using optimized daylight-based lighting controls with various window orientations and circuiting layouts for a building space located in Golden, CO. As indicated in Fig. 4, the lighting energy savings due to daylight-based lighting controls are the highest (60.5%) in layout 5 when the windows are south facing. When the windows are north facing, lighting energy savings ranging from 0.2 to 36.1% are obtained depending on the circuiting layouts. For east and west orientated windows, lighting energy savings of 16.8–46.8% can be achieved. The results outlined in Table 3 indicate that layout 5 (with four independently controlled circuits/zones) provides the most energy savings up to 36% relative to layout 1 (with only 1
circuit/zone). For layout 2, the lighting energy savings relative to layout 1 range from 1.3 to 5.9% depending on the orientation of the windows. It should be noted that daylight-based lighting control performance for layouts 4 and 5 are similar for east and west orientations. However, layout 5 outperforms layout 4 for both south and north orientations. When the space is located in Tampa, FL, similar results found for Golden, CO are obtained as outlined in Table 4. Based on the results of a series of parametric analyses summarized in Tables 3 and 4, one can conclude that more circuiting zones (4 in case of circuiting layout 5) would yield higher lighting energy use savings. The common assumption that lighting zoning horizontally with perimeter
Table 3 Summary of parametric simulation results for 4 window orientations and 5 circuiting layouts (Golden, CO).
Table 4 Summary of parametric simulation results for 4 window orientations and 5 circuiting layouts (Tampa, FL).
Layout 1
Layout 2
Layout 3
Layout 4
Layout 5
(a) Total lighting energy use (kWh) for December 19–25 31.1 29.3 27.4 23.1 22.7 West 42.5 41.7 39.4 31.2 27.2 North 35.5 35.0 31.2 26.1 25.3 East 24.8 23.5 20.6 19.0 16.8 South (b) Lighting energy saving relative to no daylight-based lighting control 27.1% 31.3% 35.7% 45.9% 46.8% West 0.2% 2.2% 7.5% 26.7% 36.1% North 16.8% 17.8% 26.7% 38.8% 40.7% East 41.9% 44.9% 51.7% 55.4% 60.5% South (c) Lighting energy saving relative to circuiting layout 1 – 5.9% 11.8% 25.8% 27.1% West 2.1% 7.4% 26.6% 36.0% North – – 1.3% 11.9% 26.5% 28.7% East 5.3% 17.0% 23.3% 32.1% South –
Layout 1
Layout 2
Layout 3
Layout 4
Layout 5
(a) Total lighting energy use (kWh) for December 19–25 36.0 34.6 32.9 26.3 25.8 West 41.2 39.6 36.5 31.1 25.1 North 38.3 37.1 34.5 27.1 26.7 East 25.3 24.2 21.4 17.2 13.9 South (b) Lighting energy saving relative to no daylight-based lighting control 15.5% 18.7% 22.9% 38.3% 39.4% West 3.4% 6.9% 14.3% 27.0% 41.1% North 10.2% 12.9% 19.0% 36.3% 37.3% East 40.7% 43.3% 49.8% 59.7% 67.4% South (c) Lighting energy saving relative to circuiting layout 1 – 3.8% 8.7% 27.0% 28.2% West 3.7% 11.2% 24.4% 39.0% North – – 3.1% 9.8% 29.1% 30.2% East 4.4% 15.3% 32.0% 45.0% South –
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Fig. 3. Lighting circuiting layout options with location of reference point (
1.2
Layout1
Layout2
Layout3
Layout4
Layout5
1 0.8 0.6 0.4 0.2 0
8
9
1.4 Lighng Output Fracon
Lighng Output Fracon
1.4
1.2
Layout1
Layout2
Layout3
Layout4
Layout5
1 0.8 0.6 0.4 0.2 0
10 11 12 13 14 15 16 17 Dec 21 (Hour)
8
9
10 11 12 13 14 15 16 17 Dec 21 (Hour)
(a) East 1.2
(b) North
Layout1
Layout2
Layout3
Layout4
Layout5
1 0.8 0.6 0.4 0.2 0
8
9
1.4 Lighng Output Fracon
Lighng Output Fracon
1.4
10 11 12 13 14 15 16 17 Dec 21 (Hour)
(c) South
).
1.2
Layout1
Layout2
Layout3
Layout4
Layout5
1 0.8 0.6 0.4 0.2 0
8
9
10 11 12 13 14 15 16 17 Dec 21 (Hour)
(d) West
Fig. 4. Lighting output fraction associated with five circuiting layout options and four window orientations during December 21 in Golden, CO.
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y = 5.5) satisfy the set-point requirement of 500-lx and the illuminance distribution is more uniform than that obtained from only natural lighting as shown in Fig. 3(a). Fig. 3(c) indicates that if the space can have several lighting control zones, the indoor illuminance distribution can be improved to be more uniform while the illuminance levels in most zones are close to set-point of illuminance.
4.3. Summary The impact of lighting circuiting layout on lighting energy use by optimal daylight-based lighting control is estimated with several lighting circuiting layout options. The new lighting control method enables designers and engineers to verify the impact of various lighting design and circuits with daylight-based lighting control. The lighting circuiting layouts test results show that even when the space is located along the perimeter areas dividing lighting circuiting zones vertically as outlined in circuiting layouts 3 or 4 relative to the exterior wall can result in higher energy savings with optimal daylight-based lighting controls.
5. Application II – optimal desk location 5.1. Problem formulation
Fig. 5. Indoor illuminance distribution (lx) of six grid points within the space (x and y indicate distance in meter from right-lower origin point of the space in Fig. 3).
(i.e., circuiting layout 2) would save more lighting energy use than vertical zoning (i.e., circuiting layout 3) does not always apply when optimal lighting control is considered. 4.2. Lighting control quality evaluation One additional benefit of the optimal daylight-based lighting control method is the ability to calculate indoor illuminance level by combining the contributions from natural lighting and from artificial lighting. This combined lighting level can be used for closeloop feedback daylight-based lighting control. For any time step, the indoor daylight illuminance can be estimated from EnergyPlus illuminance map data. Using the daylight-based lighting control optimization algorithm, the lighting output fraction provided by each lamp can be obtained. Therefore, if the illuminance data of all lamps are determined for any arbitrary point in the space, then the illuminance from the electrical lighting system can be derived for any location of the space using Eq. (3) as outlined in Seo et al. [3]. The indoor illuminance at each time step can be calculated by summing the illuminance levels obtained from natural lighting and those provided by the electrical lighting system. Fig. 5 illustrates distributions of summed typical daylighting and electrical lighting system illuminance levels of the space at 3:00 pm on December 21. The x and y grid points in Fig. 5 indicate the distances (in meter) from south-west bottom corner of the space. The illuminance levels from daylighting only, provided in Fig. 5(a), show an uneven distribution with high values near the windows. Fig. 3(b) presents the indoor illuminance distribution associated to both daylighting and electrical lighting as the results of using the optimized dimming lighting controls using circuiting layout 2. The illuminance levels at the reference points (x = 1.4, y = 5.5; x = 2.8,
This application of the optimal daylight-based lighting control simulation environment focuses on effectively selecting the best locations of desks (or workplaces) within a room to benefit from daylighting and reduce electrical lighting energy use. Two optimization sequences are carried out within the simulation environment to determine the desk locations and the optimal electrical lighting settings. Indeed, the first optimization is used to select the desk locations that minimize the lighting annual energy use for the entire space. Then, a second optimization determines the best settings for each lamp during each time step to minimize the lighting output. For a space with p possible desk locations and q desks to place in the space, the total possible desks layout options r can be calculated by Eq. (4) r=
p! q! (p − q)!
(4)
In an open office space, for example, there are 50 possible desk locations and 10 desks to be placed, then the total number of possible desk layout options is over ten millions. To illustrate this application, a small office space is considered with 6 possible desk locations with 2 desks to be placed. The space model details and the results of the optimization tool are outlined in the following sections.
5.2. Space model description For the same space model of Fig. 2 with lighting circuiting layout 2 of Fig. 3, it is assumed that two desks are to be placed in six possible desk location options. Therefore, a total of 15 possible desk layout options are feasible as noted by Eq. (4) with p = 6 and q = 2. Fig. 6 and Table 5 presents respectively, the possible 6 desk locations and the 15 desk layout options. For the analysis conducted in this section, it is assumed that the desks are located in the center of each location zones. Two orientations and two seasons are considered in the analysis performed in this section.
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Opon 1:
-1.2%
Opon 2:
-10.1%
Opon 3:
-14 .7%
Opon 4:
2.9%
Opon 5:
-9.6%
Opon 6:
-1.1%
Opon 7:
-0.5%
Opon 8:
0.0%
Opon 9:
-0.7%
Opon 10:
-9.5%
Opon 11 :
-0.4%
Opon 12:
-20 .9%
Opon 13:
3.7%
Opon 14 :
-9.1%
Opon 15:
-0.1%
Fig. 6. Six possible desk locations. Table 5 Potential 15 desk layout options. Options Layout 1 Layout 2 Layout 3 Layout 4 Layout 5
Zone (1)–(2) (1)–(3) (1)–(4) (1)–(5) (1)–(6)
Options Layout 6 Layout 7 Layout 8 Layout 9 Layout 10
Zone (2)–(3) (2)–(4) (2)–(5) (2)–(6) (3)–(4)
Options Layout 11 Layout 12 Layout 13 Layout 14 Layout 15
Zone (3)–(5) (3)–(6) (4)–(5) (4)–(6) (5)–(6)
N Fig. 8. Lighting energy use for several desk placement options for west orientation and during summer (the dark spots indicate actual locations for 2 desks).
5.3. Comparison of lighting energy use 5.3.1. West orientation – winter A series of energy simulations using actual lamp performance data are carried out for the small office space of Fig. 6 located in Golden, CO when the windows are oriented west during winter (week of December 19–25). Fig. 7 illustrates the lighting energy savings expressed relative to electrical lighting energy use for the desk layout 8, which is the most common option used for daylightbased lighting control analysis (i.e., reference points assumed to be close to the windows). As expected, desk placement options 3, 12 and 5 (dotted line) where the desks are placed in the corner of the space consume significantly more lighting energy by up to 9.8% relative to layout 8. In the other hand, desk location layouts 13 and 6 are favorable desk location options with slightly higher or similar energy use savings than that of layout 8. 5.3.2. West orientation – summer A similar analysis carried in the previous section is performed for the same space when the windows are oriented west but during the summer (week of June 19–25). Fig. 8 summarizes the lighting energy savings relative to the desk layout 8. Desk placement layouts 12, 3 and 2 (dotted line) result in significantly more lighting energy
use by up to 20.9% relative to layout 8. However, layouts 13 and 4 provide higher energy savings for the daylight-based lighting controls compared to desk location layout 8. Taking into account the results obtained for both seasons, placing the two desks according to layout 13 results in the highest energy savings when optimized daylight-based lighting controls are implemented in a space with west oriented windows located in Golden, CO.
5.3.3. South orientation – winter The analysis is repeated for the same space of Fig. 4 but with the windows orientation to south. First, the analysis is performed for a winter week (December 19–25). Fig. 9 presents the lighting energy savings obtained for all possible layouts relative to the desk placement layout 8. In this case, layouts 1, 3, 4, 7 and 13 consume more lighting energy than layout 8 by up to 10.6%. Desk layouts 2, 5, 6, 9–12, 14, and 15 provide lighting energy use savings relative to layout 8.
Opon 1:
-1.2%
Opon 2:
-5.1%
Opon 3:
-9.8%
Opon 1:
-2.8%
Opon 2:
9.4%
Opon 3:
-10 .6%
Opon 4:
-0.1%
Opon 5:
-5.7%
Opon 6:
0.0%
Opon 4:
-5.5%
Opon 5:
7.3%
Opon 6:
17.7%
Opon 7:
-0.8%
Opon 8:
0.0%
Opon 9:
-0.9%
Opon 7:
-2.8%
Opon 8:
0.0%
Opon 9:
11.4%
Opon 10:
-5.6%
Opon 11 :
-0.8%
Opon 12:
-9.6%
Opon 10:
8.5%
Opon 11:
15.5%
Opon 12:
4.2%
Opon 13:
0.3%
Opon 14 :
-5.2%
Opon 15:
-0.6%
Opon 13:
-5.5%
Opon 14:
6.5%
N Fig. 7. Lighting energy use for several desk placement options for west orientation and during winter (the dark spots indicate actual locations for 2 desks).
Opon 15: 10.2%
N Fig. 9. Lighting energy use for several desk placement options for south orientation and during winter (the dark spots indicate actual locations for 2 desks).
D. Seo et al. / Energy and Buildings 51 (2012) 122–130
Opon 1:
-6.1%
Opon 2:
20.9%
Opon 3:
-13 .0%
Opon 4:
-4.9%
Opon 5:
21.7%
Opon 6:
27.2%
Opon 7:
-6.1%
Opon 8:
0.0%
Opon 9:
29.4%
Opon 10:
20.6%
Opon 11:
25.8%
Opon 12: 18.2%
Opon 13:
-4.9%
Opon 14:
21.8%
Opon 15: 28.7%
N Fig. 10. Lighting energy use for several desk placement options for south orientation and during summer (the dark spots indicate actual locations for 2 desks).
5.3.4. South orientation – summer A similar analysis carried in the previous section is performed for the same space when the windows are oriented south during summer (week of June 19–25). Fig. 10 illustrates lighting energy savings for all desk layouts relative to the desk placement layout 8. Desk layouts 1, 3, 4, 7, and 13 consume more lighting energy than layout 8 by up to 13.0%. Meanwhile, layouts 6, 9, 11, and 15 provide significantly higher energy savings. Desk placement layout 6 would be effective for both winter and summer seasons when the windows of the office space are oriented south and is therefore the recommended desk location option in terms of lighting energy saving potential for this orientation and season. 5.4. Summary In this section, the second application of the optimal daylightbased lighting control strategy and its implementation in wholebuilding simulation tool has been demonstrated. Specifically, it has been shown that the simulation tool with the optimal daylightbased lighting control is useful to help decision making on lighting design and interior space planning by providing detailed lighting and energy savings potential for various design options. Specifically, the simulation tool has identified the best desk locations among several potential options that provide the highest energy use savings when optimal daylight-based lighting controls are implemented in an office space. In particular, it is found that optimal desk locations are highly dependent on window orientation and season. However, desk placement options that are suitable throughout the year are feasible for a given window orientation. For instance, it is found in the analysis conducted and presented in this paper for a small office space located in Golden, CO, that the optimal desk location is in the east-north corner of the space for south window orientations considered in the analysis (layout 6 of Figs. 9 and 10 for south window orientation) and west-north corner of the space for west window orientation (layout 13 of Figs. 5 and 6 for west window orientation). 6. Conclusions A novel and powerful optimal daylight-based lighting controller has been developed, modeled, and evaluated. In this study, two applications have been demonstrated among the various potential usage of the whole-building energy simulation tool in which the
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optimal controller is integrated. In the first application, the simulation tool found the best circuiting layout option to minimize the energy use required for lighting. For the second application, the simulation tool was applied to find the optimal; desk location layout within a space that reduces the reliance on electrical lighting to maintain design indoor illuminance in work spaces. The developed optimal daylight-based lighting controller integrated in EnergyPlus, a whole-building energy simulation analysis program, can be used for several other applications. For instance, the simulation tool can be used to design window placement and select the best lighting fixtures for a given space based on the potential energy use savings associated with optimized daylight-based lighting control strategies outlined discussed in this paper. The main benefit of the optimization/simulation tool is its capability to consider actual lighting system performance and contribution to maintain desired indoor illuminance levels. As it has been shown in a companion paper by Seo et al. [3], the lack of this capability is the main reason that most building energy simulation programs overestimate the lighting energy use savings associated with daylight-based lighting controls. Acknowledgements This work is a part of Development of Core Technologies for Smart Green Building research funded by the KIER (Korea Institute of Energy Research) and KME (Korea government Ministry of Knowledge Economy) of South Korea. References [1] D. Seo, Development of a universal model for predicting hourly solar radiation – application: evaluation of an optimal daylighting controller, Ph.D. Dissertation, University of Colorado-Boulder, USA, 2010. [2] S. Beldi, M. Krarti, Genetic algorithm based daylight controller, in: Proceedings of the Inaugural US–EU–China, Thermophysics Conference UECTC-RE, Beijing, China, 2009. [3] D. Seo, P. Ihm, M. Krarti, Development of an optimal daylighting controller, Building and Environment 46 (5) (2011) 1011–1022. [4] M. Wetter, GenOpt® Generic Optimization program. User manual version 2.1.1. Technical report LBNL-54199. Building Technologies Program, Simulation Research Group, Lawrence Berkeley National Laboratory, 2008. [5] M.R. Atif, A.D. Galasiu, Energy performance of daylight-linked automatic lighting control systems in large atrium spaces: report on two field-monitored case studies, Energy and Buildings 35 (5) (2003) 441–461. [6] H.W. Danny, Li. Tony, N.T. Lam, S.L. Wong, Lighting and energy performance for an office using high frequency dimming controls, Energy Conversion and Management 47 (9–10) (2006) 1133–1145. [7] E.S. Lee, S.E. Selkowitz, The New York Times Headquarters daylighting mockup: monitored performance of the daylighting control system, Energy and Buildings, Special Issue on Daylighting Buildings 38 (7) (2006) 914–929. [8] B. Bjorn, H.H. Eilif, Energy savings in lighting installations by utilizing daylight, CADDET Energy Efficiency, Newsletter 1 (1997). [9] M. Jonathan, J.B. Patrick, C.H. Doug, The energy impact of daylighting, ASHRAE Journal 40 (5) (1998) 31–35. [10] D.H.W. Li, J.C. Lam, Evaluation of lighting performance in office buildings with daylighting controls, Energy and Buildings 33 (2001) 793–803. [11] K. Moncef, M.E. Paul, C.H. Timothy, A simplified method to estimate energy savings of artificial lighting use from daylighting, Building and Environment 40 (6) (2005) 747–754. [12] K. Moncef, Energy Audit of Building Systems: An Engineering Approach, 2nd edition, CRC Press and Francis and Taylor Group, Boston, MA, 2010. [13] J. Milan, Coupling building energy and lighting simulation, in: 5th International IBPSA Conference, vol. 2, Prague, 1997, pp. 313–319. [14] W. Oliver, L. Joachim, R. Christoph, T. Jens, Dynamic annual daylight simulations based on one hour and one-minute means of irradiance data, Solar Energy 72 (5) (2002) 385–395. [15] K.-L. Martin, An overview of daylighting systems, Solar Energy 73 (2) (2002) 77–82. [16] T. Inoue, T. Kawase, T. Ibamoto, S. Takakusa, Y. Matsuo, The development of an optimal control system for window shading devices based on investigations in office buildings, ASHRAE Transactions 104 (1988) 1034–1049. [17] C. Reinhart, K. Voss, Monitoring manual control of electric lighting and blinds, Lighting Research & Technology 35 (3) (2003) 243–260. [18] F.C. Winkelmann, S. Selkowitz, Daylighting simulation in the DOE-2 building energy analysis program, Energy and Buildings 8 (1985) 271–286. [19] P.R. Tregenza, I.M. Waters, Daylight coefficients, Lighting Research and Technology 15 (2) (1983) 65–72.
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