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An analysis of measured and simulated daylight illuminance and lighting savings in a daylit corridor
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Building and Environment 40 (2005) 973–982 www.elsevier.com/locate/buildenv
An analysis of measured and simulated daylight illuminance and lighting savings in a daylit corridor Danny H.W. Li, Ernest K.W. Tsang Building Energy Research Group, Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, SAR, China Received 18 March 2004; received in revised form 15 April 2004; accepted 10 September 2004
Abstract Lighting control integrated with daylighting is recognised as an important and useful strategy in energy-efficient building designs and operations. Prediction of the internal daylight levels is a key stage in daylighting designs. With the advances in computer technology, the computation of daylight illuminances can be conducted via lighting simulation program. This paper presents a study of the daylight coefficient (DC) approach using RADIANCE lighting software in simulating the indoor daylight illuminance of a corridor. The interior daylight illuminance data measured in the corridor were compared with the simulated results based on the computer software. It was found that the DC approach could give satisfactory results especially for the sun-shaded surface and sunfacing surface receiving a large amount of direct sunlight. Further, the daylight illuminance detected by the photosensor was also simulated in conjunction with measured daylight illuminance, dimming ratio and electric lighting power to predict the lighting energy savings. The findings suggested that the measured and predicted data showed a good agreement when large electric lighting savings resulted. The probable reasons causing the discrepancies were discussed. r 2004 Elsevier Ltd. All rights reserved. Keywords: Daylight coefficient; Dimming controls; Daylight illuminance; Electric lighting; Sky luminance
1. Introduction Artificial lighting is one of the major electricityconsuming items in many non-domestic buildings, constituting about 20–30% of the total building energy load [1]. Recently, there has been an increasing interest in incorporating daylight in architectural and building designs to save building energy consumption [2,3]. Many studies have revealed that proper daylighting controls have a strong potential for reducing energy demand in non-domestic buildings by exploiting daylight more effectively [4–6]. In circulation areas such as corridors, people expect the way ahead to be lit adequately. It has been reported that, in daylit corridors, photoelectric lighting controls can give excellent energy savings [7,8]. Corresponding author. Tel.: 852 2788 7063; fax: 852 2788 7612.
E-mail address: [email protected] (D.H.W. Li). 0360-1323/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.buildenv.2004.09.007
Lighting energy expenditures and their characteristics are important information for establishing certain norms in energy code and design guidelines [9]. This information can also be used by building owners and operators to develop energy conservation strategies and management programmes [10]. Determination of daylight illuminances in an interior space is a key stage in daylighting studies. Actual daylight illuminance of a room is mainly influenced by luminance levels and patterns of the sky in the direction of view of the window [11]. Daylight coefficient (DC) concept [12], which considers changes in the luminance of the sky elements, offers an effective way of computing indoor daylight illuminances under various sky conditions and solar positions. Daylight illuminance determination based on DC approach is, however, very complex. The advancements of computer development can reduce the time for illuminance calculations [13].
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A detailed simulation program would be useful in analysing daylighting schemes. A lighting simulation program, viz., RADIANCE, is being used by a number of researchers to compute interior illuminances based on the DC concept. In a previous work, we examined the DC approach via the RADIANCE program using data in a scale model and a classroom [14]. It was found that DC approach gave more accurate results than traditional daylight factor (DF) method, especially for internal spaces under non-overcast skies. Reinhart and Herkel [15] simulated the indoor illuminance distributions for two office geometries with six different RADIANCE-based daylight simulation methods including DC approach based on daylight data from the Freiburg test reference year (TRY). Mardaljevic [16] examined the DC approach using the RADIANCE computer package and measured data from the unfurnished office of the Building Research Establishment (BRE). This paper studies the accuracy of the RADIANCE daylighting simulation computer program. Simulated results including illuminance levels and electric lighting energy expenditures are compared against field measurements in a daylit corridor. Characteristics of findings and implications for daylighting designs are discussed.
2. Description of the corridor and the lighting control system A fully air-conditioned daylit corridor at City University of Hong Kong facing northwest (3201) was selected for this study [8,17]. The corridor is used for circulation to different classrooms and lecture theatres located on the fourth floor with the dimensions of 2.85 m (deep) 18.3 m (along the window) 2.65 m (height). The University is situated near residential areas and faces several building blocks and hillsides. Certain parts of the sky are thus obstructed by these external components. Fig. 1 shows the interior view of the corridor. There are totally 12 ceiling-mounted recessed fluorescent luminaires with standard diffusers evenly distributed along the corridor. Each luminaire consists of two 36 W fluorescent tubes with dimmable electronic ballasts, which can dim the lamp output smoothly and uniformly. The dimming range is from 100% to 1% of the lighting output. The maximum total lighting load is 864 W plus electronic ballast load. The lighting power density in the corridor is 16.6 W/m2. Measurement of the illuminance level of electric lighting was conducted at night when all the lights were on and no occupant walked along the corridor. The lighting levels for several points along midway of the whole corridor were recorded. The mean interior illuminance of the circulation area including the corridor was found to be around 450 lx.
Fig. 1. Interior view of the daylit corridor.
Initially, two photosensors were used to regulate and record the light intensity. The first sensor with measuring range from 0 to 2000 lx was mounted onto the ceiling located at the mid-way of the corridor. It detected both daylight and reflected light from corridor surfaces to provide a ‘close loop’ control dimming system. Since it is a narrow plan walkway (18.3 m long 2.85 m wide), a single-zone control is considered adequate. The lighting level received was sent to a dimming controller, which varied the light output of the fluorescent lamps accordingly via dimmable electronic ballasts. The second sensor with measuring illuminance level up to 16,000 lx was mounted next to the windows to record the amount of transmitted daylight. The measured data only gave an indication of the daylight availability for the corridor, and the sensor itself did not form any part of the dimming control system. Information on the amount of daylight available is essential to daylighting designs. In addition, it has been pointed out that an ‘open loop’ approach for communal areas such as the corridor can reduce the number of photocells required [18]. This would greatly reduce the time and effort required for installation, calibration and regular maintenance of these photocells. Measured parameters including dimming ratio of the fluorescent fittings, transmitted daylight availability (from second sensor) and electric lighting consumption were recorded. The measurement of electric lighting consumption was conducted by means of a single-phase AC energy meter. The meter displayed the readings in kWh with built-in software to capture the measured data from the energy meter and transmit these data to a microcomputer for storage and subsequent analysis. The light intensity detected by the first sensor was delivered to the controller for light output monitoring but the illuminance data were not displayed and stored. Later, another photosensor was installed next to the first sensor to record the illuminance readings. The third sensor with similar features to the first sensor was linked
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up with the same microcomputer for data logging and display.
3. Indoor illuminance prediction method In a photosensor-controlled dimming system, the illuminance at the reference point is detected by the first sensor, and the electric lights are subsequently dimmed based on this reading. Conventionally, the illuminance from natural sources is often determined in terms of DF with the calculations being based on the CIE overcast sky. However, daylight illuminances inside a room are not, in general, proportional to the external illuminance, but depend on the exact sky luminance distribution at that time. This is because a point in a room will receive direct light only from certain areas of the sky and the illuminance within a room is not equally sensitive to changes in the luminance of different parts of the sky. Interior daylight calculations using standard skies and DF would give poor estimates of actual illuminance [19]. As proposed in 1983, the concept of DC that considers the changes in the luminance of the sky elements, offers a more effective way for computing indoor daylight illuminances [12]. Previous research work on predicting daylight illuminance by computer simulation techniques revealed that DC approach gave more accurate results than DF method particularly for internal spaces near the window fac- ade under nonovercast skies [14]. For the present study, the DC approach was used to estimate the indoor illuminance. The DC, Dyf ; is expressed as [12] Dyf ¼
DE yf ; Lyf DSyf
(1)
where DE yf is the illuminance ultimately produced at a point in a room by a small sky element at elevation y and azimuth f (lx), Lyf the luminance of the element (cd/m2) and DSyf the angular size of the sky element (sr). The Dyf depends on the geometry of the room and its exterior environment, the reflectances of many surfaces and transmittance of the windows. It is, however, independent of sky luminance distribution because DE yf changes in proportion to Lyf : Hence, building characteristics and surrounding climatic conditions are separated. The total daylight illuminance, E, received at the point in the room is the integral of Eq. (1). For all, but the simplest cases (e.g. direct light from a uniform or the CIE standard overcast sky via glass with constant light transmission value on an unobstructed horizontal surface), it is not possible to compute analytically. Instead of integration, numerical techniques can be used such as X E¼ Dyf Lyf DS yf : (2) yf
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This gives the illuminance for a particular point as the sum of the products of D, L and S for each sky patch. Accordingly, the computation can be used to calculate the daylight illuminance for indoor spaces facing various orientations. Owing to the advancements of computer technology, there has been a steady increase in the use of computer simulation technique to evaluate daylighting schemes. A lighting simulation program called RADIANCE has been used by a number of researchers to compute interior illuminances. Measured sky luminance data can be used in RADIANCE simulations to compute the DC and hence the internal daylight illuminance [20]. The luminance of each sky patch was based on measured sky luminance patterns (i.e. discrete data values). To use the measured sky luminance data in a RADIANCE simulation, the data needed to be used as a pattern modifier (i.e., conversion of sky luminance data to RADIANCE format) to a constant luminance sky [21]. In this way, the measured sky luminance values were converted into regular grid forms using interpolation. For ‘out-of-range’ measurements (points close to solar position), an estimation of the sky brightness was made from a simple average of the brightness at nearby points. It has been reported that simple averaging methods would underestimate the sky brightness around the sun position. However, on bright sunny days, the direct solar contribution would dominate and such approximation would not cause a substantial loss of accuracy [22]. The other component of the model sky is the sun. The sun luminance was estimated based on the direct normal illuminance and solar disc angle. For the present analysis, the solar disc angle was taken to be 0.51. It has been reported that RADIANCE lighting simulation system could predict internal illuminance to high degree of accuracy for a range of realistic sky conditions. RADIANCE is a computer simulation package for simulating and visualizing lighting in and around architectural environments. It can examine advanced lighting regimes using the backward ray-tracing technique, in which the light is traced from the observer to the dominant light sources to calculate the luminances required for visualization. To estimate the working plane illuminance levels more accurately, we used RADIANCE program to calculate daylight illuminance values with the DC concept. Currently, RADIANCE is available in several versions [23]. In the present study, the analysis was based on the RADIANCEE version 3.5 developed at Lawrence Berkeley Laboratory running under UNIX workstation.
4. Measuring station Data measurement is regarded as the most effective and accurate method of setting up reliable databases.
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A measuring station was established at the City University of Hong Kong to record the daylight illuminances and sky luminance distributions in 1991. Initially, only horizontal surface measurements were made. In 1996, the measuring station was extended to record vertical outdoor illuminance on the four principle surfaces facing the north, east, south and west. The measuring station was upgraded in 1999 with the installation of a sky scanner to record the luminances of the whole sky patches [24]. The measuring station is inside a laboratory space on the top floor of the City University of Hong Kong, which is not located in a high-density urban area. The surrounding buildings and the University block are similar in height. All sensors were installed on the rooftop in a position relatively free from external obstructions and readily accessible for inspection and general cleaning. Data collection starts before sunrise and finishes after sunset. All measurements are referred to true solar time (TST). 4.1. Daylight Illuminance measurement The illuminance sensors (T-10 M) with an accuracy of 2%, manufactured and calibrated by Minolta of Japan,
were used for the outdoor illuminance measurements. Data-management software was used to capture the measured results from the main body adapter, which were fed into a microcomputer for storage. The measured data were displayed in real time for individual receptors and a measurement interval of 1 min was set. 4.2. Sky luminance measurement The sky luminance distribution was measured by means of a sky scanner (EKO MS-300LR), which was manufactured and calibrated by the EKO Company of Japan. It measures the luminance at 145 points (shown in Fig. 2) in the sky by scanning the sky dome. The full view angle of the scanner is 111, giving a sky coverage of approximately 68% [25]. The important parts of the sky scanner were housed in a weatherproof casing allowing continuous outdoor operation. Output data from the scanner were recorded on a microcomputer placed inside the laboratory space on the top floor. A Visual Basic computer program was used to capture and transmit the measured data. To safeguard the sensor, light was prevented from falling on it when the luminance was greater than 35 kcd/m2 by using an
Fig. 2. Measurement points for the sky scanner.
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automatic shutter. Each scanning period was about 4 min and measurements were taken every 10 min.
5. Field measurement and simulation setting To verify the accuracy of the RADIANCE program, on-site measured indoor illuminance data were compared with the simulated results. The illuminance testpoint was located in the mid-way of the corridor. Fig. 3 shows the layout plan. The interior illuminances were recorded by means of a hand-held illuminance sensor (T-10 M) manufactured and calibrated by Minolta of Japan. The time for each reading was carefully recorded so that the ‘synchronous’ measurements of the indoor illuminance and sky luminance data in our measuring station could be selected for simulation. The indoor illuminance data were collected every minute and averaged over 10-min intervals. The measurements were made at daytime between June and July 2003 with all electric lights off. We did some trials for several points and found that the results were in good agreement.
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Therefore, one point illuminance measurement would be acceptable. The simulation model consists of two parts: the corridor elements including external obstructions, which are comprised of opaque and transparent material, and the self-luminous source that represents the sky and the sun. The key simulation parameters for daylight illuminance determinations are the number of reflections and resolution of the inter-reflection calculation, which are referred to as the ambient parameters. A convergence test was conducted to obtain the settings for ambient parameters so that the accuracy and simulation time of the ray-tracing calculations in RADIANCE are reduced to an acceptable level. Table 1 shows the ambient parameter settings for the simulations. Geometrically, the model generated for simulation was close to the actual dimensions of the corridor. Internal components including lockers, window frames, and glazing panes were modelled as discrete elements. Reflectances were obtained by selecting the sample (with known reflectance value) that most closely resembles the colour and brightness of the surface [26]. The results were also checked with the measurements using luminance and illuminance meters [26]. The glazing transmittance, which is the ratio of illuminance behind the glazing and in front of the glazing, was obtained using the illuminance sensors under overcast sky conditions [27]. Externally, a circular ground plane with a common reflectivity of 0.2 and a radius of 500 m was set. The non-luminous external objects including hillsides and building blocks were modelled in RADIANCE. The physical dimensions were determined from a map and on-site measurement, and the reflectance values were obtained according to their colours and textures (similar to the approach for internal materials). Table 2 summaries the corridor details. It should be pointed out that there are several limitations using sky luminance data to predict interior daylight illuminance. Firstly, for measurement of the sky luminance distributions, the celestial hemisphere is split into 145 circular angular patches. The division of the sky can avoid any double counting, but it leads to uncovered regions of the sky. Secondly, the measured data are based on discrete results rather than continuous analytical functions. Sky luminances between adjoining measurement points may vary significantly. Thirdly, Table 1 Set of ambient parameters used for simulation
Fig. 3. Measurement point of the daylit corridor.
Ambient calculation
Setting
Ambient Ambient Ambient Ambient Ambient
1024 512 0.08 1000 5
division sampling accuracy resolution bounces
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Table 2 Parameters used for the corridor Parameter
Ceiling Door Floor Locker Internal wall Window frame External surface
Reflectance 0.789 0.2 0.311 0.497 0.774 0.3 0.246
Glazing
Transmittance 0.23
every scanning time is about 4 min and the measurements are taken every 10 min. Substantial variations in sky luminance may occur between each record. Also, the simple average of the luminance at nearby points to estimate the ‘out-of-range’ sky brightness measurements (points close to the solar position) may introduce data distortion [28].
6. Data analysis In interpreting sky conditions, climatic parameters such as sunshine hours (SH) were always used. The sun is considered to be shining when an object under its direct light casts a well-defined shadow. It seems obvious that more cloud results in less sunshine and vice versa. Since the corridor faced north-west, the window fac- ade would have received pure diffuse illuminance of a low level in the morning or under overcast sky conditions and certain amount of direct sunlight giving high illuminances in the afternoon under non-overcast skies. For a fair evaluation of RADIANCE program, the sky luminances should not vary substantially during the scan. The whole set of measured data was rejected when horizontal outdoor illuminance data showed a difference of 10% or more of the mean values.
based on DC approach settings. Fig. 4 presents the plots of field-measured and computer-simulated results when the data were of a diffuse nature. It can be observed that the simulated data are reasonably close to the measured values. In some cases, the predicted and measured illuminances overlapped. Without direct component, indoor illuminances were of low values, varying between 198 and 630 lx for the measured results, and ranging from 204 to 616 lx for the simulated data. The peak difference was about 160 lx corresponding to 36.8% of the measured value. Likewise, the illuminance data for the test point when the sun was ‘seen’ by the window wall (i.e. including direct sunlight) are shown in Fig 5. It can be seen that the indoor illuminance levels in the space are mostly underestimated. The measured illuminance was between 890 and 6940 lx, while the predicted illuminance varied between 720 and 6470 lx. In general, the accuracy for predicting the illuminance including direct sunlight was not as good as the accuracy for the test point with purely diffuse component. For large illuminance data (i.e. 4000 lx or more) consisting of mainly the direct component, the generated illuminances were in good agreement with the measured readings. When the illuminance results contained smaller amount of direct component (i.e. below 4000 lx), the simulated illuminances were far less than the measured values, resulting in an underpredicted maximum space illuminance of 60%. The findings support that the simple averaging methods for ‘out-of-range’ measurements caused data distortion and underestimated the indoor daylight illuminance. If the direct component was dominated, the interior illuminance could be accurately simulated. In order to closely examine the performance of the program, two widely used statistics, viz. mean bias error (MBE) and root mean square error (RMSE) were employed. The MBE provides information on the long-term performance of the models. The RMSE gives information on the short-term performance and
6.1. Simulated internal daylight illuminance In total, 57 sets of data were used for the analysis. In general, the data set can be classified according to whether the direct component penetrates into the corridor. Two categorisations, namely sunlit under non-overcast sky conditions (containing a certain amount of direct sunlight) and sun shaded or overcast skies (excluding the direct component) were used. Accordingly, 37 and 20 sets of illuminance data were classified sun shaded or overcast skies, and sun facing under non-overcast skies, respectively. The indoor daylight illuminances for the test point at various times and dates were simulated by RADIANCE software
Fig. 4. Measured and simulated indoor horizontal illuminance for sun-shaded surface or overcast skies.
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Fig. 5. Measured and simulated indoor horizontal illuminance for sun-facing surface under non-overcast skies.
indicates the scattering of data around the models. The MBE and RMSE for the test point with and without direct component were computed and are presented in Table 3. The statistical results indicate that the interior illuminances can be estimated to a satisfactory accuracy when the direct sunlight is excluded. The MBE and RMSE are 11.1 and 95.6 lx, respectively. These represent 2.5 and 21.4% of the mean measured interior illuminance. The illuminances exhibit relatively large MBE and RMSE results when the test point could ‘see’ the centre of the sun disk. The simulated daylight illuminances are often less than the measured values, resulting in large MBE and RMSE values. The MBE is 578 lx representing 20.6% of the mean-measured illuminance. The RMSE is 928 lx accounting for 33% of the average-measured value. 6.2. Modelling electric lighting savings The purpose of a daylight-linked dimming control system is to maintain the resulting workplane illuminances (daylight plus dimmed electric light) at the target illuminance level such that the system can provide the required light value with minimum electric use. However, many constraints and variables can affect the system performance. The correlation between daylight illuminance at a workplane point directly below the photosensor (Ewp) and the photosensor signal (Es) is a key factor to measure the performance of a daylight dimming system. In real installation cases, workplane illuminances would be deviated from the target level due to various ratios of Ewp/Es across sky types and lighting systems [29,30]. Rubinstein et al. [31] analysed these correlations by using scale model measurements. Choi and Mistrick [29,30] modelled photosensor signals and the resulting workplane illuminances using computer simulation tools. As the daylight responsive dimming system for a daylit corridor was monitored, it is possible
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to study such correlations in a real internal space with measured weather data. The light intensity in the corridor directly below the photosensor (Ewp) can be simulated by the RADIANCE software using DC approach with measured sky luminance and outdoor illuminance data. For a closeloop control system, the illuminance detected by the photosensors (first and third sensors) included both daylight and dimmed artificial light, but only the first sensor would send the signal to the dimming controller to dim the light output of the fluorescent lamps. It indicates that the measured dimming ratio can be used to predict the electric light level. The 100% dimming ratio reading represents savings of a standard on-off control (zero electric light output) while zero dimming ratio shows the system under full operation (target illuminance level). Dimming ratios between 1 and 99% indicate that the lighting control system is under dimming and the dimming ratios should be proportional to the electric light output. Accordingly, the daylight illuminance at the photosensor signal (Es) was determined based on the light intensity recorded by the third sensor and the corresponding measured dimming ratio. It was inevitable that there would be some period of missing data for various reasons such as power failure and instrument fault. In particular, due to the malfunctioning of the sky scanner, there was a major interruption in May 2002. Also, the sky scanner was sent to the manufacturer in August 2002 for repair, calibration and general inspection. Totally, 6-month data (February–April 2002 and June–August 2002) from 7:00 to 17:00 were used for analysis. The target illuminance was set to be 450 lx. However, between June and mid-July, the switching illuminance set points were varied between 200 and 400 lx for energy savings and switching frequency analysis. Ideally, the sensor illuminance should be exactly proportional to the working plane illuminance. A big difference between the two ratios indicates that the ceiling mounted sensor would not be able to maintain a constant illuminance on the workplane with various weather conditions. Fig. 6 presents the correlation between Ewp and Es under overcast skies. It can be observed that the output of the sensor is approximately proportional to the workplane illuminance. The ratio of Ewp/Es is found to be 0.92 with R2 of 0.96. Likewise, the Ewp against Es for non-overcast days is shown in Fig. 7. It can be seen that there are a few occasions when the photosensor detects small portion of direct sunlight of high illuminance levels and the workplane light levels cannot be fully simulated for this increase in illuminance. Apart from this, the response of the photosensor is roughly proportional to the workplane illuminance. The ratio of Ewp/Es is 0.85 and R2 is 0.93, which is slightly less than that under overcast skies.
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Table 3 Summary of MBE and RMSE for the simulated indoor daylight illuminance
Sun-shaded surface Sun-facing surface
MBE (lx)
% MBE
RMSE (lx)
% RMSE
Average measured illuminance (lx)
11.1 578
2.5 20.6
95.6 928
21.4 33
447 2810
1000
1600
electric-lighting load (W)
Workplane point illuminance (lx)
1800
1400 1200 1000 800 600 400
600
400
200
200
0
0 0
200
400
600
800
1000
1200
1400
1600
1800
Photosensor signal (lx)
0
2200 2000 1800 1600 1400 1200
100
200
300
400
500
600
1300 1400 1500
photosenor signal (lx)
(a) 1000
electric-lighting load (W)
Fig. 6. The correlation between workplane point illuminance (Ewp) and photosensor signal (Es) under overcast skies.
Workplane point illuminance (lx)
overcast skies
800
non-overcast skies
800
600
400
200
1000 800
0
600
0
100
200
(b)
400 200 0 0
200
400
600
800 1000 1200 1400 1600 1800 2000 2200
300
400
500
600
1300
1400
1500
photosenor signal (lx)
Fig. 8. The correlation between electric-lighting load and photosensor signal (Es) under (a) overcast and (b) non-overcast skies.
Photosensor signal (lx)
Fig. 7. The correlation between workplane point illuminance (Ewp) and photosensor signal (Es) under non-overcast skies.
The lighting level detected by the photosensor (Es) was used to control the electric light output for daylightlinked control system. It is obvious that the electriclighting load depends on Es. Fig. 8 shows the plot of Es against the electric-lighting load for overcast and nonovercast days. The general trend and characteristics of the correlations for the two weather conditions are very similar. The electric-lighting loads are close to its full capacity (between 700 and 800 W) when the detected daylight data are of low values (below 50 lx), then the lighting power drops gently with the detected daylight illuminance up to 600 lx. Thereafter, the loads are
around zero with increasing daylight levels obtained. Through regression analysis, the following two equations for the two sky conditions were determined as: Overcast sky W ¼ 2:53 106 E 3s þ 5:69 103 E 2s 3:86E s þ 860 ðfor 0 lxpEso600 lxÞ ðR2 ¼ 0:952Þ:
ð3Þ
Non-overcast sky W ¼ 7 106 E 3s þ 0:0102 103 E 2s 4:84 E s þ 860 ðfor 0 lxpE s o600 lxÞ
ðR2 ¼ 0:916Þ;
ð4Þ
where W=electric-lighting load (W) With the coefficient of determination (R2) over 0.91 for both cases, it is argued that the electric-lighting load
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Table 4 Modelled and measured daily lighting energy savings for the corridor
Simulated lighting energy saving (kWh) Measured lighting energy saving (kWh)
Feb
Mar
April
June
July
Aug
6.29 5.9
7.24 6.93
7.01 6.43
8.16 8.09
7.73 8.31
7.52 6.23
is well correlated with Es. To get some idea of the performance of the modelled equations (Eqs. (3) and (4)) and the ratios of Ewp/Es, six-month data (February–April 2002 and June–August 2002) from 7:00 to 17:00 were used for the assessment. In total, 94 sets of daily data were obtained. Table 4 summarises the modelled and measured daily lighting energy savings for the corridor. The simulated daily lighting energy savings varied from 6.29 kWh in February to 8.16 Wh in June while measured data ranged between 5.9 kWh in February and 8.31 kWh in July. Apart from July, all measured results were smaller than the predicted data. Generally, the predicted and measured results for all months were in good agreement. In June, for instance, the difference was 0.07 kWh representing only 1% of the measured value. Large saving values indicated that the daylight received was always larger than the design indoor illuminance. Even though the internal illuminances were not accurately simulated, fully dimming ratios and hence maximum electric lighting energy savings were obtained. Also, in June and July the switching illuminance set points were varied from 200 to 450 lx for energy savings and switching frequency analysis [17]. With smaller design indoor illuminance settings, more fully dimming ratio data were recorded. Apart from the errors in predicting the interior illuminance, there are several reasons for the discrepancies. Firstly, the study was based on measured weather data for a daylit corridor facing moderate external obstructions with real occupancy. Very often, certain amount of daylight would be blocked by the occupants and items of movable furniture. As a result more artificial lighting was required. However, such a situation was not considered. Secondly, substantial variations in outdoor illuminance would appear within a record. When the reading was close to the design illuminance, it would overestimate the electric lighting savings. Also, the system operated in a close-loop mode with the photosensor detecting both controlled electric light as well as daylight. The response time was longer and the dimming ratios were also affected by the artificial lighting. Overall, the results indicated a fairly good agreement between measured and simulated lighting energy savings.
7. Conclusions A study of the DC approach using the RADIANCE program to predict indoor daylight in a daylit corridor
has been carried out. Fifty-seven sets of illuminance data were recorded and analysed. It was found that the indoor illuminance results simulated by DC showed reasonably good agreement with the measured data. When the daylight component was mainly diffuse, the interior illuminances were well predicted. When the daylight directly received by the window fac- ade contained small amount of direct sunlight (i.e. less than 4000 lx), the illuminances were underestimated. When the internal illuminances were dominated by the direct component (i.e. over 4000 lx), the simulated illuminances were found very close to the measured values. Analysis was extended to model the lighting energy savings for the corridor with a close-loop daylightlinked control system. Apart from July, it was found that the daily lighting energy savings for all the 6 months were overestimated. The simulated daily lighting energy savings varied from 6.29 kWh in February to 8.16 kWh in June while measured data ranged between 5.9 kWh in February and 8.31 kWh in July. With large savings in June, the modelled and measured results were in good agreement. The rather small discrepancy obtained for this month was attributed to the frequent in full dimming ratio associated with zero electric lighting consumption and the lower switching illuminance set points. For other months, the discrepancies between the measured and modelled savings were relatively large. Such differences would be due to the fact that a certain amount of daylight was obstructed by the occupants. Illuminance detected by the sensor was substantially fluctuated around the design indoor level and the close loop mode of the control system which were not fully simulated.
Acknowledgements The work described in this paper was supported by a grant from the City University of Hong Kong (Project No. CityU 7001567). References [1] Li DHW, Lam JC, Wong SL. Daylighting and its implications to overall thermal transfer value (OTTV) determinations. Energy— The International Journal 2002;27(11):991–1008. [2] Li DHW, Lam JC. Evaluation of lighting performance in office buildings with daylighting controls. Energy and Buildings 2001;33(8):793–803.
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[3] Atif MR, Galasiu AD. Energy performance of daylight-linked automatic lighting control systems in large atrium spaces: report on two field-monitored case studies. Energy and Buildings 2003; 35(5):435–531. [4] Kim SY, Mistrick R. Recommended daylight conditions for photosensor system calibration in a small office. Journal of the Illuminating Engineering Society summer 2001;30(2):176–88. [5] Neida BV, Maniccia D, Tweed A. An analysis of the energy and cost savings potential of occupancy sensors for commercial lighting systems. Journal of the Illuminating Engineering Society summer 2001;30(2):111–25. [6] Knight IP. Measured energy savings due to photocell control of individual luminaires. Lighting Research and Technology 1999;31(1):19–22. [7] Slater AI, Bordass WT, Heasman TA. People and lighting controls. BRE information paper IP 6/9. Garston: CRC; 1996. [8] Li DHW, Lam JC. An investigation of daylighting performance and energy saving in a daylit corridor. Energy and Buildings 2003;35(4):365–73. [9] Littlefair PJ, Aizlewood ME. Measuring daylight in real buildings. In: Proceedings of the CIBSE national lighting conference, Bath, UK, 1996. [10] Santamouris M, Argiriou A, Dascalaki E, Balaras C, Gaglia A. Energy characteristics and savings potential in office buildings, Solar Energy 1994;52(1):59–66. [11] Rutten AJF. Sky luminance research imperative for adequate control of temporary supplementary artificial lighting installations. Building and Environment 1994;29(10):105–11. [12] Tregenza PR, Waters IM. Daylight Coefficients. Lighting Research and Technology 1983;15(2):65–71. [13] Bellia L, Cesarano A, Minichiello F, Sibilio S. De-light: a software tool for the evaluation of direct daylighting illuminances both indoors and outdoors—comparison with Superlite 2.0 and Lumen Micro 7.1. Building and Environment 2000;35(4):281–95. [14] Li DHW, Lau CCS, Lam JC. Predicting daylight illuminance by computer simulation techniques. Lighting Research and Technology 2004;36(2):113–29. [15] Reinhart CF, Herkel S. The simulation of annual daylight illuminance distributions—a state-of-the-art comparison of six RADIANCEbased methods. Energy and Buildings 2000;32(2):167–87. [16] Mardaljevic J. Beyond daylight factors: An example study using daylight coefficients. In: Proceedings of the lighting 2000, York, UK, 2000, pp. 177–186.
[17] Li DHW, Lam JC. An analysis of lighting energy savings and switching frequency for a daylit corridor under various indoor design illuminance levels. Applied Energy 2003;76(4):363–78. [18] Littlefair PJ. Photoelectric control: the effectiveness of technique to reduce switching frequency. Lighting Research and Technology 2001;33(1):43–58. [19] Bellia L, Cesarano A, Sibilio S. Daylighting contribution in interior lighting: Experimental verification of software simulation results. Lighting Research and Technology 1994;26(2):99–105. [20] Ward GL, Shakespeare R. Rendering with RADIANCE. The art and science of lighting visualization. Los Altos, CA: Morgan Kaufmann; 1998. [21] Mardaljevic J. The BRE-IDMP dataset: a new benchmark for the validation of illuminance prediction techniques. Lighting Research and Technology 2001;33(2):117–36. [22] Mardaljevic J. Validation of a lighting simulation program under real sky conditions. Lighting Research and Technology 1995; 27(4):181–8. [23] Compagnon R. RADIANCE: A simulation tool for daylighting systems. Tutorial notes, The Martin Centre, University of Cambridge, UK, 1997. [24] Li DHW, Lam JC, Lau CCS. A study of solar radiation daylight illuminance and sky luminance data measurement for Hong Kong. Architectural Science Reviews 2002;45(1):21–30. [25] Tregenza PR. Subdivision of sky hemisphere for luminance measurements. Lighting Research and Technology 1987;19(1): 13–4. [26] Chartered Institution of Building Services Engineers. Lighting guide 11: Surface reflectance and colour, its specification and measurement for designers. London, UK, 2001. [27] Fontoynont M. Daylight performance of buildings. London: James and James; 1999. [28] Li DHW, Lau CCS, Lam JC. Predicting daylight illuminance on inclined surfaces using sky luminance data. Energy—The International Journal 2004; in press. [29] Choi A, Mistrick RG. On the prediction of energy savings for a daylight dimming system. Journal of the Illuminating Engineering Society 1997;26(2):77–90. [30] Choi AS, Mistrick RG. Analysis of daylight responsive dimming system performance. Building and Environment 1999;34(3):231–43. [31] Rubinstein F, Ward G, Verderber R. Improving the performance of photo-electrically controlled lighting systems. Journal of the Illuminating Engineering Society 1989;15(1):70–94.
Building and Environment 40 (2005) 973–982 www.elsevier.com/locate/buildenv
An analysis of measured and simulated daylight illuminance and lighting savings in a daylit corridor Danny H.W. Li, Ernest K.W. Tsang Building Energy Research Group, Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, SAR, China Received 18 March 2004; received in revised form 15 April 2004; accepted 10 September 2004
Abstract Lighting control integrated with daylighting is recognised as an important and useful strategy in energy-efficient building designs and operations. Prediction of the internal daylight levels is a key stage in daylighting designs. With the advances in computer technology, the computation of daylight illuminances can be conducted via lighting simulation program. This paper presents a study of the daylight coefficient (DC) approach using RADIANCE lighting software in simulating the indoor daylight illuminance of a corridor. The interior daylight illuminance data measured in the corridor were compared with the simulated results based on the computer software. It was found that the DC approach could give satisfactory results especially for the sun-shaded surface and sunfacing surface receiving a large amount of direct sunlight. Further, the daylight illuminance detected by the photosensor was also simulated in conjunction with measured daylight illuminance, dimming ratio and electric lighting power to predict the lighting energy savings. The findings suggested that the measured and predicted data showed a good agreement when large electric lighting savings resulted. The probable reasons causing the discrepancies were discussed. r 2004 Elsevier Ltd. All rights reserved. Keywords: Daylight coefficient; Dimming controls; Daylight illuminance; Electric lighting; Sky luminance
1. Introduction Artificial lighting is one of the major electricityconsuming items in many non-domestic buildings, constituting about 20–30% of the total building energy load [1]. Recently, there has been an increasing interest in incorporating daylight in architectural and building designs to save building energy consumption [2,3]. Many studies have revealed that proper daylighting controls have a strong potential for reducing energy demand in non-domestic buildings by exploiting daylight more effectively [4–6]. In circulation areas such as corridors, people expect the way ahead to be lit adequately. It has been reported that, in daylit corridors, photoelectric lighting controls can give excellent energy savings [7,8]. Corresponding author. Tel.: 852 2788 7063; fax: 852 2788 7612.
E-mail address: [email protected] (D.H.W. Li). 0360-1323/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.buildenv.2004.09.007
Lighting energy expenditures and their characteristics are important information for establishing certain norms in energy code and design guidelines [9]. This information can also be used by building owners and operators to develop energy conservation strategies and management programmes [10]. Determination of daylight illuminances in an interior space is a key stage in daylighting studies. Actual daylight illuminance of a room is mainly influenced by luminance levels and patterns of the sky in the direction of view of the window [11]. Daylight coefficient (DC) concept [12], which considers changes in the luminance of the sky elements, offers an effective way of computing indoor daylight illuminances under various sky conditions and solar positions. Daylight illuminance determination based on DC approach is, however, very complex. The advancements of computer development can reduce the time for illuminance calculations [13].
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A detailed simulation program would be useful in analysing daylighting schemes. A lighting simulation program, viz., RADIANCE, is being used by a number of researchers to compute interior illuminances based on the DC concept. In a previous work, we examined the DC approach via the RADIANCE program using data in a scale model and a classroom [14]. It was found that DC approach gave more accurate results than traditional daylight factor (DF) method, especially for internal spaces under non-overcast skies. Reinhart and Herkel [15] simulated the indoor illuminance distributions for two office geometries with six different RADIANCE-based daylight simulation methods including DC approach based on daylight data from the Freiburg test reference year (TRY). Mardaljevic [16] examined the DC approach using the RADIANCE computer package and measured data from the unfurnished office of the Building Research Establishment (BRE). This paper studies the accuracy of the RADIANCE daylighting simulation computer program. Simulated results including illuminance levels and electric lighting energy expenditures are compared against field measurements in a daylit corridor. Characteristics of findings and implications for daylighting designs are discussed.
2. Description of the corridor and the lighting control system A fully air-conditioned daylit corridor at City University of Hong Kong facing northwest (3201) was selected for this study [8,17]. The corridor is used for circulation to different classrooms and lecture theatres located on the fourth floor with the dimensions of 2.85 m (deep) 18.3 m (along the window) 2.65 m (height). The University is situated near residential areas and faces several building blocks and hillsides. Certain parts of the sky are thus obstructed by these external components. Fig. 1 shows the interior view of the corridor. There are totally 12 ceiling-mounted recessed fluorescent luminaires with standard diffusers evenly distributed along the corridor. Each luminaire consists of two 36 W fluorescent tubes with dimmable electronic ballasts, which can dim the lamp output smoothly and uniformly. The dimming range is from 100% to 1% of the lighting output. The maximum total lighting load is 864 W plus electronic ballast load. The lighting power density in the corridor is 16.6 W/m2. Measurement of the illuminance level of electric lighting was conducted at night when all the lights were on and no occupant walked along the corridor. The lighting levels for several points along midway of the whole corridor were recorded. The mean interior illuminance of the circulation area including the corridor was found to be around 450 lx.
Fig. 1. Interior view of the daylit corridor.
Initially, two photosensors were used to regulate and record the light intensity. The first sensor with measuring range from 0 to 2000 lx was mounted onto the ceiling located at the mid-way of the corridor. It detected both daylight and reflected light from corridor surfaces to provide a ‘close loop’ control dimming system. Since it is a narrow plan walkway (18.3 m long 2.85 m wide), a single-zone control is considered adequate. The lighting level received was sent to a dimming controller, which varied the light output of the fluorescent lamps accordingly via dimmable electronic ballasts. The second sensor with measuring illuminance level up to 16,000 lx was mounted next to the windows to record the amount of transmitted daylight. The measured data only gave an indication of the daylight availability for the corridor, and the sensor itself did not form any part of the dimming control system. Information on the amount of daylight available is essential to daylighting designs. In addition, it has been pointed out that an ‘open loop’ approach for communal areas such as the corridor can reduce the number of photocells required [18]. This would greatly reduce the time and effort required for installation, calibration and regular maintenance of these photocells. Measured parameters including dimming ratio of the fluorescent fittings, transmitted daylight availability (from second sensor) and electric lighting consumption were recorded. The measurement of electric lighting consumption was conducted by means of a single-phase AC energy meter. The meter displayed the readings in kWh with built-in software to capture the measured data from the energy meter and transmit these data to a microcomputer for storage and subsequent analysis. The light intensity detected by the first sensor was delivered to the controller for light output monitoring but the illuminance data were not displayed and stored. Later, another photosensor was installed next to the first sensor to record the illuminance readings. The third sensor with similar features to the first sensor was linked
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up with the same microcomputer for data logging and display.
3. Indoor illuminance prediction method In a photosensor-controlled dimming system, the illuminance at the reference point is detected by the first sensor, and the electric lights are subsequently dimmed based on this reading. Conventionally, the illuminance from natural sources is often determined in terms of DF with the calculations being based on the CIE overcast sky. However, daylight illuminances inside a room are not, in general, proportional to the external illuminance, but depend on the exact sky luminance distribution at that time. This is because a point in a room will receive direct light only from certain areas of the sky and the illuminance within a room is not equally sensitive to changes in the luminance of different parts of the sky. Interior daylight calculations using standard skies and DF would give poor estimates of actual illuminance [19]. As proposed in 1983, the concept of DC that considers the changes in the luminance of the sky elements, offers a more effective way for computing indoor daylight illuminances [12]. Previous research work on predicting daylight illuminance by computer simulation techniques revealed that DC approach gave more accurate results than DF method particularly for internal spaces near the window fac- ade under nonovercast skies [14]. For the present study, the DC approach was used to estimate the indoor illuminance. The DC, Dyf ; is expressed as [12] Dyf ¼
DE yf ; Lyf DSyf
(1)
where DE yf is the illuminance ultimately produced at a point in a room by a small sky element at elevation y and azimuth f (lx), Lyf the luminance of the element (cd/m2) and DSyf the angular size of the sky element (sr). The Dyf depends on the geometry of the room and its exterior environment, the reflectances of many surfaces and transmittance of the windows. It is, however, independent of sky luminance distribution because DE yf changes in proportion to Lyf : Hence, building characteristics and surrounding climatic conditions are separated. The total daylight illuminance, E, received at the point in the room is the integral of Eq. (1). For all, but the simplest cases (e.g. direct light from a uniform or the CIE standard overcast sky via glass with constant light transmission value on an unobstructed horizontal surface), it is not possible to compute analytically. Instead of integration, numerical techniques can be used such as X E¼ Dyf Lyf DS yf : (2) yf
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This gives the illuminance for a particular point as the sum of the products of D, L and S for each sky patch. Accordingly, the computation can be used to calculate the daylight illuminance for indoor spaces facing various orientations. Owing to the advancements of computer technology, there has been a steady increase in the use of computer simulation technique to evaluate daylighting schemes. A lighting simulation program called RADIANCE has been used by a number of researchers to compute interior illuminances. Measured sky luminance data can be used in RADIANCE simulations to compute the DC and hence the internal daylight illuminance [20]. The luminance of each sky patch was based on measured sky luminance patterns (i.e. discrete data values). To use the measured sky luminance data in a RADIANCE simulation, the data needed to be used as a pattern modifier (i.e., conversion of sky luminance data to RADIANCE format) to a constant luminance sky [21]. In this way, the measured sky luminance values were converted into regular grid forms using interpolation. For ‘out-of-range’ measurements (points close to solar position), an estimation of the sky brightness was made from a simple average of the brightness at nearby points. It has been reported that simple averaging methods would underestimate the sky brightness around the sun position. However, on bright sunny days, the direct solar contribution would dominate and such approximation would not cause a substantial loss of accuracy [22]. The other component of the model sky is the sun. The sun luminance was estimated based on the direct normal illuminance and solar disc angle. For the present analysis, the solar disc angle was taken to be 0.51. It has been reported that RADIANCE lighting simulation system could predict internal illuminance to high degree of accuracy for a range of realistic sky conditions. RADIANCE is a computer simulation package for simulating and visualizing lighting in and around architectural environments. It can examine advanced lighting regimes using the backward ray-tracing technique, in which the light is traced from the observer to the dominant light sources to calculate the luminances required for visualization. To estimate the working plane illuminance levels more accurately, we used RADIANCE program to calculate daylight illuminance values with the DC concept. Currently, RADIANCE is available in several versions [23]. In the present study, the analysis was based on the RADIANCEE version 3.5 developed at Lawrence Berkeley Laboratory running under UNIX workstation.
4. Measuring station Data measurement is regarded as the most effective and accurate method of setting up reliable databases.
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A measuring station was established at the City University of Hong Kong to record the daylight illuminances and sky luminance distributions in 1991. Initially, only horizontal surface measurements were made. In 1996, the measuring station was extended to record vertical outdoor illuminance on the four principle surfaces facing the north, east, south and west. The measuring station was upgraded in 1999 with the installation of a sky scanner to record the luminances of the whole sky patches [24]. The measuring station is inside a laboratory space on the top floor of the City University of Hong Kong, which is not located in a high-density urban area. The surrounding buildings and the University block are similar in height. All sensors were installed on the rooftop in a position relatively free from external obstructions and readily accessible for inspection and general cleaning. Data collection starts before sunrise and finishes after sunset. All measurements are referred to true solar time (TST). 4.1. Daylight Illuminance measurement The illuminance sensors (T-10 M) with an accuracy of 2%, manufactured and calibrated by Minolta of Japan,
were used for the outdoor illuminance measurements. Data-management software was used to capture the measured results from the main body adapter, which were fed into a microcomputer for storage. The measured data were displayed in real time for individual receptors and a measurement interval of 1 min was set. 4.2. Sky luminance measurement The sky luminance distribution was measured by means of a sky scanner (EKO MS-300LR), which was manufactured and calibrated by the EKO Company of Japan. It measures the luminance at 145 points (shown in Fig. 2) in the sky by scanning the sky dome. The full view angle of the scanner is 111, giving a sky coverage of approximately 68% [25]. The important parts of the sky scanner were housed in a weatherproof casing allowing continuous outdoor operation. Output data from the scanner were recorded on a microcomputer placed inside the laboratory space on the top floor. A Visual Basic computer program was used to capture and transmit the measured data. To safeguard the sensor, light was prevented from falling on it when the luminance was greater than 35 kcd/m2 by using an
Fig. 2. Measurement points for the sky scanner.
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automatic shutter. Each scanning period was about 4 min and measurements were taken every 10 min.
5. Field measurement and simulation setting To verify the accuracy of the RADIANCE program, on-site measured indoor illuminance data were compared with the simulated results. The illuminance testpoint was located in the mid-way of the corridor. Fig. 3 shows the layout plan. The interior illuminances were recorded by means of a hand-held illuminance sensor (T-10 M) manufactured and calibrated by Minolta of Japan. The time for each reading was carefully recorded so that the ‘synchronous’ measurements of the indoor illuminance and sky luminance data in our measuring station could be selected for simulation. The indoor illuminance data were collected every minute and averaged over 10-min intervals. The measurements were made at daytime between June and July 2003 with all electric lights off. We did some trials for several points and found that the results were in good agreement.
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Therefore, one point illuminance measurement would be acceptable. The simulation model consists of two parts: the corridor elements including external obstructions, which are comprised of opaque and transparent material, and the self-luminous source that represents the sky and the sun. The key simulation parameters for daylight illuminance determinations are the number of reflections and resolution of the inter-reflection calculation, which are referred to as the ambient parameters. A convergence test was conducted to obtain the settings for ambient parameters so that the accuracy and simulation time of the ray-tracing calculations in RADIANCE are reduced to an acceptable level. Table 1 shows the ambient parameter settings for the simulations. Geometrically, the model generated for simulation was close to the actual dimensions of the corridor. Internal components including lockers, window frames, and glazing panes were modelled as discrete elements. Reflectances were obtained by selecting the sample (with known reflectance value) that most closely resembles the colour and brightness of the surface [26]. The results were also checked with the measurements using luminance and illuminance meters [26]. The glazing transmittance, which is the ratio of illuminance behind the glazing and in front of the glazing, was obtained using the illuminance sensors under overcast sky conditions [27]. Externally, a circular ground plane with a common reflectivity of 0.2 and a radius of 500 m was set. The non-luminous external objects including hillsides and building blocks were modelled in RADIANCE. The physical dimensions were determined from a map and on-site measurement, and the reflectance values were obtained according to their colours and textures (similar to the approach for internal materials). Table 2 summaries the corridor details. It should be pointed out that there are several limitations using sky luminance data to predict interior daylight illuminance. Firstly, for measurement of the sky luminance distributions, the celestial hemisphere is split into 145 circular angular patches. The division of the sky can avoid any double counting, but it leads to uncovered regions of the sky. Secondly, the measured data are based on discrete results rather than continuous analytical functions. Sky luminances between adjoining measurement points may vary significantly. Thirdly, Table 1 Set of ambient parameters used for simulation
Fig. 3. Measurement point of the daylit corridor.
Ambient calculation
Setting
Ambient Ambient Ambient Ambient Ambient
1024 512 0.08 1000 5
division sampling accuracy resolution bounces
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Table 2 Parameters used for the corridor Parameter
Ceiling Door Floor Locker Internal wall Window frame External surface
Reflectance 0.789 0.2 0.311 0.497 0.774 0.3 0.246
Glazing
Transmittance 0.23
every scanning time is about 4 min and the measurements are taken every 10 min. Substantial variations in sky luminance may occur between each record. Also, the simple average of the luminance at nearby points to estimate the ‘out-of-range’ sky brightness measurements (points close to the solar position) may introduce data distortion [28].
6. Data analysis In interpreting sky conditions, climatic parameters such as sunshine hours (SH) were always used. The sun is considered to be shining when an object under its direct light casts a well-defined shadow. It seems obvious that more cloud results in less sunshine and vice versa. Since the corridor faced north-west, the window fac- ade would have received pure diffuse illuminance of a low level in the morning or under overcast sky conditions and certain amount of direct sunlight giving high illuminances in the afternoon under non-overcast skies. For a fair evaluation of RADIANCE program, the sky luminances should not vary substantially during the scan. The whole set of measured data was rejected when horizontal outdoor illuminance data showed a difference of 10% or more of the mean values.
based on DC approach settings. Fig. 4 presents the plots of field-measured and computer-simulated results when the data were of a diffuse nature. It can be observed that the simulated data are reasonably close to the measured values. In some cases, the predicted and measured illuminances overlapped. Without direct component, indoor illuminances were of low values, varying between 198 and 630 lx for the measured results, and ranging from 204 to 616 lx for the simulated data. The peak difference was about 160 lx corresponding to 36.8% of the measured value. Likewise, the illuminance data for the test point when the sun was ‘seen’ by the window wall (i.e. including direct sunlight) are shown in Fig 5. It can be seen that the indoor illuminance levels in the space are mostly underestimated. The measured illuminance was between 890 and 6940 lx, while the predicted illuminance varied between 720 and 6470 lx. In general, the accuracy for predicting the illuminance including direct sunlight was not as good as the accuracy for the test point with purely diffuse component. For large illuminance data (i.e. 4000 lx or more) consisting of mainly the direct component, the generated illuminances were in good agreement with the measured readings. When the illuminance results contained smaller amount of direct component (i.e. below 4000 lx), the simulated illuminances were far less than the measured values, resulting in an underpredicted maximum space illuminance of 60%. The findings support that the simple averaging methods for ‘out-of-range’ measurements caused data distortion and underestimated the indoor daylight illuminance. If the direct component was dominated, the interior illuminance could be accurately simulated. In order to closely examine the performance of the program, two widely used statistics, viz. mean bias error (MBE) and root mean square error (RMSE) were employed. The MBE provides information on the long-term performance of the models. The RMSE gives information on the short-term performance and
6.1. Simulated internal daylight illuminance In total, 57 sets of data were used for the analysis. In general, the data set can be classified according to whether the direct component penetrates into the corridor. Two categorisations, namely sunlit under non-overcast sky conditions (containing a certain amount of direct sunlight) and sun shaded or overcast skies (excluding the direct component) were used. Accordingly, 37 and 20 sets of illuminance data were classified sun shaded or overcast skies, and sun facing under non-overcast skies, respectively. The indoor daylight illuminances for the test point at various times and dates were simulated by RADIANCE software
Fig. 4. Measured and simulated indoor horizontal illuminance for sun-shaded surface or overcast skies.
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Fig. 5. Measured and simulated indoor horizontal illuminance for sun-facing surface under non-overcast skies.
indicates the scattering of data around the models. The MBE and RMSE for the test point with and without direct component were computed and are presented in Table 3. The statistical results indicate that the interior illuminances can be estimated to a satisfactory accuracy when the direct sunlight is excluded. The MBE and RMSE are 11.1 and 95.6 lx, respectively. These represent 2.5 and 21.4% of the mean measured interior illuminance. The illuminances exhibit relatively large MBE and RMSE results when the test point could ‘see’ the centre of the sun disk. The simulated daylight illuminances are often less than the measured values, resulting in large MBE and RMSE values. The MBE is 578 lx representing 20.6% of the mean-measured illuminance. The RMSE is 928 lx accounting for 33% of the average-measured value. 6.2. Modelling electric lighting savings The purpose of a daylight-linked dimming control system is to maintain the resulting workplane illuminances (daylight plus dimmed electric light) at the target illuminance level such that the system can provide the required light value with minimum electric use. However, many constraints and variables can affect the system performance. The correlation between daylight illuminance at a workplane point directly below the photosensor (Ewp) and the photosensor signal (Es) is a key factor to measure the performance of a daylight dimming system. In real installation cases, workplane illuminances would be deviated from the target level due to various ratios of Ewp/Es across sky types and lighting systems [29,30]. Rubinstein et al. [31] analysed these correlations by using scale model measurements. Choi and Mistrick [29,30] modelled photosensor signals and the resulting workplane illuminances using computer simulation tools. As the daylight responsive dimming system for a daylit corridor was monitored, it is possible
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to study such correlations in a real internal space with measured weather data. The light intensity in the corridor directly below the photosensor (Ewp) can be simulated by the RADIANCE software using DC approach with measured sky luminance and outdoor illuminance data. For a closeloop control system, the illuminance detected by the photosensors (first and third sensors) included both daylight and dimmed artificial light, but only the first sensor would send the signal to the dimming controller to dim the light output of the fluorescent lamps. It indicates that the measured dimming ratio can be used to predict the electric light level. The 100% dimming ratio reading represents savings of a standard on-off control (zero electric light output) while zero dimming ratio shows the system under full operation (target illuminance level). Dimming ratios between 1 and 99% indicate that the lighting control system is under dimming and the dimming ratios should be proportional to the electric light output. Accordingly, the daylight illuminance at the photosensor signal (Es) was determined based on the light intensity recorded by the third sensor and the corresponding measured dimming ratio. It was inevitable that there would be some period of missing data for various reasons such as power failure and instrument fault. In particular, due to the malfunctioning of the sky scanner, there was a major interruption in May 2002. Also, the sky scanner was sent to the manufacturer in August 2002 for repair, calibration and general inspection. Totally, 6-month data (February–April 2002 and June–August 2002) from 7:00 to 17:00 were used for analysis. The target illuminance was set to be 450 lx. However, between June and mid-July, the switching illuminance set points were varied between 200 and 400 lx for energy savings and switching frequency analysis. Ideally, the sensor illuminance should be exactly proportional to the working plane illuminance. A big difference between the two ratios indicates that the ceiling mounted sensor would not be able to maintain a constant illuminance on the workplane with various weather conditions. Fig. 6 presents the correlation between Ewp and Es under overcast skies. It can be observed that the output of the sensor is approximately proportional to the workplane illuminance. The ratio of Ewp/Es is found to be 0.92 with R2 of 0.96. Likewise, the Ewp against Es for non-overcast days is shown in Fig. 7. It can be seen that there are a few occasions when the photosensor detects small portion of direct sunlight of high illuminance levels and the workplane light levels cannot be fully simulated for this increase in illuminance. Apart from this, the response of the photosensor is roughly proportional to the workplane illuminance. The ratio of Ewp/Es is 0.85 and R2 is 0.93, which is slightly less than that under overcast skies.
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Table 3 Summary of MBE and RMSE for the simulated indoor daylight illuminance
Sun-shaded surface Sun-facing surface
MBE (lx)
% MBE
RMSE (lx)
% RMSE
Average measured illuminance (lx)
11.1 578
2.5 20.6
95.6 928
21.4 33
447 2810
1000
1600
electric-lighting load (W)
Workplane point illuminance (lx)
1800
1400 1200 1000 800 600 400
600
400
200
200
0
0 0
200
400
600
800
1000
1200
1400
1600
1800
Photosensor signal (lx)
0
2200 2000 1800 1600 1400 1200
100
200
300
400
500
600
1300 1400 1500
photosenor signal (lx)
(a) 1000
electric-lighting load (W)
Fig. 6. The correlation between workplane point illuminance (Ewp) and photosensor signal (Es) under overcast skies.
Workplane point illuminance (lx)
overcast skies
800
non-overcast skies
800
600
400
200
1000 800
0
600
0
100
200
(b)
400 200 0 0
200
400
600
800 1000 1200 1400 1600 1800 2000 2200
300
400
500
600
1300
1400
1500
photosenor signal (lx)
Fig. 8. The correlation between electric-lighting load and photosensor signal (Es) under (a) overcast and (b) non-overcast skies.
Photosensor signal (lx)
Fig. 7. The correlation between workplane point illuminance (Ewp) and photosensor signal (Es) under non-overcast skies.
The lighting level detected by the photosensor (Es) was used to control the electric light output for daylightlinked control system. It is obvious that the electriclighting load depends on Es. Fig. 8 shows the plot of Es against the electric-lighting load for overcast and nonovercast days. The general trend and characteristics of the correlations for the two weather conditions are very similar. The electric-lighting loads are close to its full capacity (between 700 and 800 W) when the detected daylight data are of low values (below 50 lx), then the lighting power drops gently with the detected daylight illuminance up to 600 lx. Thereafter, the loads are
around zero with increasing daylight levels obtained. Through regression analysis, the following two equations for the two sky conditions were determined as: Overcast sky W ¼ 2:53 106 E 3s þ 5:69 103 E 2s 3:86E s þ 860 ðfor 0 lxpEso600 lxÞ ðR2 ¼ 0:952Þ:
ð3Þ
Non-overcast sky W ¼ 7 106 E 3s þ 0:0102 103 E 2s 4:84 E s þ 860 ðfor 0 lxpE s o600 lxÞ
ðR2 ¼ 0:916Þ;
ð4Þ
where W=electric-lighting load (W) With the coefficient of determination (R2) over 0.91 for both cases, it is argued that the electric-lighting load
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Table 4 Modelled and measured daily lighting energy savings for the corridor
Simulated lighting energy saving (kWh) Measured lighting energy saving (kWh)
Feb
Mar
April
June
July
Aug
6.29 5.9
7.24 6.93
7.01 6.43
8.16 8.09
7.73 8.31
7.52 6.23
is well correlated with Es. To get some idea of the performance of the modelled equations (Eqs. (3) and (4)) and the ratios of Ewp/Es, six-month data (February–April 2002 and June–August 2002) from 7:00 to 17:00 were used for the assessment. In total, 94 sets of daily data were obtained. Table 4 summarises the modelled and measured daily lighting energy savings for the corridor. The simulated daily lighting energy savings varied from 6.29 kWh in February to 8.16 Wh in June while measured data ranged between 5.9 kWh in February and 8.31 kWh in July. Apart from July, all measured results were smaller than the predicted data. Generally, the predicted and measured results for all months were in good agreement. In June, for instance, the difference was 0.07 kWh representing only 1% of the measured value. Large saving values indicated that the daylight received was always larger than the design indoor illuminance. Even though the internal illuminances were not accurately simulated, fully dimming ratios and hence maximum electric lighting energy savings were obtained. Also, in June and July the switching illuminance set points were varied from 200 to 450 lx for energy savings and switching frequency analysis [17]. With smaller design indoor illuminance settings, more fully dimming ratio data were recorded. Apart from the errors in predicting the interior illuminance, there are several reasons for the discrepancies. Firstly, the study was based on measured weather data for a daylit corridor facing moderate external obstructions with real occupancy. Very often, certain amount of daylight would be blocked by the occupants and items of movable furniture. As a result more artificial lighting was required. However, such a situation was not considered. Secondly, substantial variations in outdoor illuminance would appear within a record. When the reading was close to the design illuminance, it would overestimate the electric lighting savings. Also, the system operated in a close-loop mode with the photosensor detecting both controlled electric light as well as daylight. The response time was longer and the dimming ratios were also affected by the artificial lighting. Overall, the results indicated a fairly good agreement between measured and simulated lighting energy savings.
7. Conclusions A study of the DC approach using the RADIANCE program to predict indoor daylight in a daylit corridor
has been carried out. Fifty-seven sets of illuminance data were recorded and analysed. It was found that the indoor illuminance results simulated by DC showed reasonably good agreement with the measured data. When the daylight component was mainly diffuse, the interior illuminances were well predicted. When the daylight directly received by the window fac- ade contained small amount of direct sunlight (i.e. less than 4000 lx), the illuminances were underestimated. When the internal illuminances were dominated by the direct component (i.e. over 4000 lx), the simulated illuminances were found very close to the measured values. Analysis was extended to model the lighting energy savings for the corridor with a close-loop daylightlinked control system. Apart from July, it was found that the daily lighting energy savings for all the 6 months were overestimated. The simulated daily lighting energy savings varied from 6.29 kWh in February to 8.16 kWh in June while measured data ranged between 5.9 kWh in February and 8.31 kWh in July. With large savings in June, the modelled and measured results were in good agreement. The rather small discrepancy obtained for this month was attributed to the frequent in full dimming ratio associated with zero electric lighting consumption and the lower switching illuminance set points. For other months, the discrepancies between the measured and modelled savings were relatively large. Such differences would be due to the fact that a certain amount of daylight was obstructed by the occupants. Illuminance detected by the sensor was substantially fluctuated around the design indoor level and the close loop mode of the control system which were not fully simulated.
Acknowledgements The work described in this paper was supported by a grant from the City University of Hong Kong (Project No. CityU 7001567). References [1] Li DHW, Lam JC, Wong SL. Daylighting and its implications to overall thermal transfer value (OTTV) determinations. Energy— The International Journal 2002;27(11):991–1008. [2] Li DHW, Lam JC. Evaluation of lighting performance in office buildings with daylighting controls. Energy and Buildings 2001;33(8):793–803.
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