Quantifying energy savings in daylight responsive systems: The role of dimming electronic ballasts

Energy and Buildings 40 (2008) 36–50 www.elsevier.com/locate/enbuild

Quantifying energy savings in daylight responsive systems: The role of dimming electronic ballasts L. Doulos a,*, A. Tsangrassoulis b, F. Topalis a a

Laboratory of Photometry, National Technical University of Athens, Iroon Politexniou 9, 157 80 Zografou, Greece b Department of Architecture, University of Thessaly, Pedion Areos, 383 34 Volos, Greece Received 5 December 2006; received in revised form 4 January 2007; accepted 22 January 2007

Abstract The application of lighting control technologies with photosensors has led to an increase in public interest. Although these technologies have been promoted during the last years their successful use in buildings has been accomplished in a small percentage of new projects. One reason is the difficulty in quantifying the energy savings and thus the subsequent payback period. Daylight responsive dimming systems consist of three basic components: photosensor, controller, and dimming unit. Electronic dimming ballast (EDB) is one substantial component of these lighting control systems which can adjust the light output due to the transferred signal from the photosensor and lighting controller. The aim of this study is to quantify energy savings among different EDBs. Eighteen commercial EDBs were selected and various sets of electrical and illuminance measurements were taken for different dimming levels, in order to develop polynomial functions between light output and consumed power. Using the measured data, a set of simulations were performed for a photosensor with an ideal cosine spatial sensitivity distribution installed in a typical office room using two control algorithms, closed loop and integral reset, trying to quantify the relative differences in energy savings. # 2007 Elsevier B.V. All rights reserved. Keywords: Daylight; Dimmable electronic ballast; Energy savings; Photosensor

1. Introduction As the cost of energy continues to rise, increasing effort has gone into minimizing the energy consumption of lighting installations. This effort has evolved, along with the development of new energy efficient lighting equipment, the utilization of improved lighting design practice and the improvement of lighting control systems. Lighting controls that perform on–off operations due to occupant presence, time scheduling, manual dimming, automatic dimming in connection to daylighting, demand control and lumen depreciation, have great potential in reducing lighting energy consumption [1,2]. Daylighting can be considered to be a very important strategy in substituting electric energy for artificial lighting. It cannot only reduce the lighting consumption but it can be very efficient in reducing peak electrical loads. A variety of results in relation to energy

* Corresponding author. Tel.: +30 210 772 3627; fax: +30 210 772 3628. E-mail addresses: [email protected] (L. Doulos), [email protected] (A. Tsangrassoulis), [email protected] (F. Topalis). 0378-7788/$ – see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2007.01.019

savings due to daylighting are presented in literature. Based on simulation results Szerman [3] found 77% for lighting energy savings and 14% of total energy savings. Embrechts and Van Bellegem [4] measured that an individual lighting dimming system can offer 20–40% of lighting consumption savings. Opdal and Brekke [5] compared measurements and calculation results and obtained 40% of lighting savings from calculation and 30% of lighting energy savings from measurements. Based on field measurements, Li et al [6] estimated annual savings that represented a 33% reduction in energy use of electric lighting under the dimming control in the tested office. Also, Lee and Selkowitz [7] measured significant daily lighting energy use savings up to 60%. The indicative values presented are quite difficult to compare because they refer to a particular climate, building and daylighting system. Daylight responsive dimming systems controlled by photosensors adjust the electric lighting level according to the amount of daylighting impinging on the photosensor. They consist of three basic components; photosensor, lighting controller and dimming unit. Photosensor detects luminous flux and converts a signal to the controller which in turn processes this signal and defines the desired dimming level.

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After that, the controller sends a dimming control voltage to the electronic ballasts forcing them to reduce power. For the prediction of the performance of the electric lighting control system and its effects on energy use, the following parameters are required [8–11]: 1. accurate computation of daylighting [12]; 2. accurate simulation of the performance of the photosensor; 3. reliable simulation of the artificial lighting system output in relation to the control voltage. There are three simple control algorithms that can be easily designed for accurate simulation of the sensor performance [13–15]. These are:  closed loop,  open loop,  integral reset. Bierman and Conway [16] reviewed different photosensor models with different acceptance angles and varying spatial and spectral sensitivity and they have provided the data necessary in improving the accuracy of simulations of the actual performance of photosensors. Ehrlich et al. [17] have presented a method of simulating the photosensor behaviour based on the multiplication of two fisheye images, one generated from the angular sensitivity of the photosensor and the other from a 1808 fisheye image of the space as ‘‘seen’’ by the photosensor. Analyzing the final image, photosensor illuminance can be calculated accurately. In order for energy consumption to be estimated accurately, functions of control voltage, light output ratio and consumed power of the lighting system are needed. These can be estimated using experiments or using the manufacturer’s data [18–20]. The present paper examines and compares the performance of a number of electronic dimmable ballasts (EDBs), quantifying their impact on the energy savings in daylight

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responsive dimming systems, installed in a typical room under various control algorithms. 2. Measurements Eighteen samples of 230 Vac EDB, from five different manufactures, were tested, divided in nine pairs. Analytically:     

3 2 2 1 1

pairs of ballasts for 1  54 W T5 tubular lamp; pairs of ballasts for 1  28 W T5 tubular lamp; pairs of ballasts for 2  54 W T5 tubular lamps; pair of ballasts for 1  49 W T5 tubular lamp; pair of ballasts for 2  28 W T5 tubular lamps.

The ballasts have been codified using three digits. The first digit is the number of the pair (1 to 9 for the nine pairs), the second digit is a letter (A or B) to distinguish the ballasts in each pair (the two ballasts of each pair are identical) and the last one corresponds to the manufacturer of the ballast (i to v for five manufacturers). For example, the 3Bi code corresponds to the second ballast of the third pair manufactured by i. All ballasts were tested with the same lamps. These lamps were seasoned and the same preheat time was applied for each lamp and ballast before the first measurement. Measurements took place in the Photometry Laboratory of National Technical University of Athens. The test space was the dark room of the Photometry Laboratory (20 m  7 m  4 m) with black mat painted surfaces (Fig. 1). Ambient temperature was maintained constant at 26 8C and input voltage at 230 Vac, 50 Hz through the voltage stabiliser for all tested conditions. Measurements regarding the light output and active power were taken for various dimming levels. Each ballast was manually dimmed through a digital power supply, from a maximum control voltage setting to a minimum value (from 10 V to 0 V) and vice versa (from 0 V to 10 V) in tandem procedure [20]. The light output was measured as an illuminance value at the same distance from the lamps. The measurement of the

Fig. 1. Experimental arrangement with the lamp and ballast attached on the goniophotometer head (left) and the photometer sensor inside a cylinder (right) that eliminates the stray light.

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Fig. 2. Light output ratio and corresponding control voltage from 10 V to 0 V and vice versa for the 1st pair of the tested ballasts.

Fig. 4. Light output ratio and corresponding control voltage from 10 V to 0 V and vice versa for the 3rd pair of the tested ballasts.

luminous intensity was performed according to the CIE standards no. 121 (1996) and no. 70 (1987) [21,22]. Every lamp was attached to the goniophotometer head at a distance of 9.90 m from the photometer head. The photometer sensor was placed inside a black mat painted cylinder with baffles, protected from stray light (Fig. 1). The field of view of the photometer sensor covered the entire emitting surface of each tested lamp. Measurements of the light output were monitored every 5 min and were taken only if the light output was stabilized, meaning that the standard deviation between the last two measurements was less than 2%. This procedure varied from 15 min to 20 min for all cases [18]. A single-phase power meter was used for the measurement of the active power of the lighting system (ballast and the corresponding lamps) for various dimming levels. In addition, the root mean square (rms) voltage and rms current feed were measured (apparent power) in order for the power factor of the tested EDB during the dimming procedure to be estimated. The dimming range was calculated for each individual ballast from an upper limit of 100% (maximum dimmed level) to a minimum level near 0%. No lamp was extinguished before reaching the reported minimum light output, nor flickered at the minimum level for all the tested ballasts.

2.1. Light output ratio versus control voltage

Fig. 3. Light output ratio and corresponding control voltage from 10 V to 0 V and vice versa for the 2nd pair of the tested ballasts.

Fig. 5. Light output ratio and corresponding control voltage from 10 V to 0 V and vice versa for the 4th pair of the tested ballasts.

Figs. 2–10 present the light output percentage with regard to control voltage for the tested ballasts, changing control voltage from 10 V to 0 V and vice versa. The values were the same when the lamps were measured from maximum to minimum light output (decreasing control voltage of tested ballast from 10 V to 0 V) and from minimum to maximum light output (increasing control voltage from 0 V to 10 V). Furthermore, Figs. 2–10 illustrate the light output percentage with regard to control voltage between the A and B ballast of the same pair. The standard deviations of light output ratio for the same control voltage for each of the tested pairs of the EDBs were considered statistically insignificant, equal to a confidence level of 0.05. Table 1 shows the derived functions from polynomial interpolation between control voltage and light output ratio for the tested ballasts for decreasing control voltage from 10 V to 0 V. The R2 values varied from 0.9999 (for 1Ai ballast) to 0.9995 (for 6Biii ballast) for the above described test conditions. These best-fit functions can be used to improve control system performance accuracy by determining the dimming ratios required to meet an illuminance value and the appropriate corresponding control voltage for the EDB.

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Fig. 6. Light output ratio and corresponding control voltage from 10 V to 0 V and vice versa for the 5th pair of the tested ballasts.

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Fig. 9. Light output ratio and corresponding control voltage from 10 V to 0 V and vice versa for the 8th pair of the tested ballasts.

control voltage for each of the tested pairs of the EDBs were considered statistically insignificant, equal to a confidence level of 0.05. It is evident from Figs. 2–19, that all tested pairs of ballasts formed different relationships between control voltage and its corresponding light output ratio or relative consumed power.

Fig. 7. Light output ratio and corresponding control voltage from 10 V to 0 V and vice versa for the 6th pair of the tested ballasts.

2.2. Relative consumed power versus control voltage Figs. 11–19 present the relative consumed power with regard to control voltage of the lighting system between the A and B ballast of the same pair for various dimming levels. Also, the standard deviations of the consumed power for the same

Fig. 8. Light output ratio and corresponding control voltage from 10 V to 0 V and vice versa for the 7th pair of the tested ballasts.

Fig. 10. Light output ratio and corresponding control voltage from 10 V to 0 V and vice versa for the 9th pair of the tested ballasts.

Fig. 11. Relative consumed power and corresponding control voltage for the 1st pair of the tested ballasts.

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Table 1 Derived functions from polynomial interpolation between control voltage (x, 0 to 10) and light output ratio (y, 0 to 1) Ballast

Derived functions

R2

1Ai 1Bi 2Ai 2Bi 3Ai 3Bi 4Aii 4Bii 5Aii 5Bii 6Aiii 6Biii 7Aiv 7Biv 8Aiv 8Biv 9Av 9Bv

y = 0.00002344x6 + 0.00053992x5  0.00435613x4 + 0.01620990x3  0.02355387x2 + 0.01287959x + 0.01589684 y = 0.00004117x6 + 0.00103790x5  0.00954040x4 + 0.04081082x3  0.07561641x2 + 0.05062558x + 0.01270736 y = 0.00003096x6 + 0.00082004x5  0.00786903x4 + 0.03491077x3  0.06735055x2 + 0.04733242x + 0.01194209 y = 0.00002982x6 + 0.00077231x5  0.00720138x4 + 0.03083989x3  0.05618906x2 + 0.03676216x + 0.01410432 y = 0.00003856x6 + 0.00101547x5  0.00973654x4 + 0.04314729x3  0.08350657x2 + 0.05728914x + 0.01129706 y = 0.00003997x6 + 0.00105920x5  0.01022311x4 + 0.04546504x3  0.08791534x2 + 0.05998760x + 0.01198853 y = 0.00001308x6 + 0.00041201x5  0.00476300x4 + 0.02293525x3  0.02466238x2 + 0.00289382x + 0.01912537 y = 0.00001070x6 + 0.00034724x5  0.00411776x4 + 0.02003170x3  0.01872196x2  0.00187107x + 0.01757083 y = 0.00000650x6 + 0.00022331x5  0.00268164x4 + 0.01049128x3 + 0.01831943x2  0.03454918x + 0.01170384 y = 0.00000543x6 + 0.00018574x5  0.00221287x4 + 0.00814483x3 + 0.02122482x2  0.02995033x + 0.01367352 y = 0.00000184x6  0.00008839x5 + 0.00131755x4  0.00982973x3 + 0.05066776x2  0.04294000x + 0.01994780 y = 0.00000338x6  0.00012648x5 + 0.00163005x4  0.01053693x3 + 0.04838349x2  0.03582594x + 0.02532301 y = 0.00000514x6  0.00022456x5 + 0.00383343x4  0.03224775x3 + 0.12887922x2  0.06704693x + 0.01386342 y = 0.00000618x6  0.00025104x5 + 0.00406254x4  0.03288684x3 + 0.12785028x2  0.06106582x + 0.01403806 y = 0.00000858x6 + 0.00024988x5  0.00252019x4 + 0.00741477x3 + 0.02601918x2  0.02475672x + 0.02363751 y = 0.00001560x6 + 0.00045325x5  0.00471128x4 + 0.01805299x3 + 0.00481759x2  0.01645685x + 0.01862676 y = 0.00000039x6 + 0.00011303x5  0.00257459x4 + 0.01817555x3  0.02517757x2 + 0.01457912x + 0.03006010 y = 0.00000940x6 + 0.00034254x5  0.00460579x4 + 0.02518692x3  0.03126050x2 + 0.01103266x + 0.02995589

0.9999 0.9997 0.9998 0.9998 0.9997 0.9996 0.9999 0.9999 0.9999 0.9999 0.9997 0.9995 0.9999 0.9999 0.9999 0.9999 0.9998 0.9999

14). For the same range, the 5th pair had a linear relationship between control voltage and corresponding consumed power (Fig. 15).

Therefore, it can be concluded that dimming with settled control voltage values forced the tested ballast to respond with different work plane illuminance levels and different power consumption. For example, 3Ai ballast gives 8.90% light output ratio and 20.20% relative consumed power for 5 V control voltage (Figs. 4 and 13, respectively), while 8Aiv ballast gives 54.89% light output ratio and 60.09% relative consumed power for the same control voltage (Figs. 9 and 18, respectively). The light output ratio and relative consumed power values for the other tested ballasts varied between the above corresponding values. Furthermore, all tested ballasts showed a non-linear relationship not only between control voltage and corresponding light output ratio, but also between control voltage and relative consumed power. Linear relationships can be assumed only for specific ballasts and for a specific range of control voltage. Only the 4th pair of ballasts had a linear relationship for both corresponding light output ratio and consumed power with the control voltage varying from 2.2 V to 1 0V (Figs. 5 and

Table 2 presents power factor values at maximum and minimum dimming levels of the tested EDBs. Power factor during dimming ranged from 0.9999 at maximum light output to 0.6293 at minimum light output ratio for the sum of the ballasts. The best performance according to power quality was for the 9Av ballast that performed 0.9999 at maximum light output and 0.8654 at minimum light output ratio. On the contrary, ballast 4Aii performed 0.9831 at maximum light output and 0.6293 at minimum light output ratio. This decrease resulted from the increase in the current total harmonic distortion (THD) during dimming. The power factor varied differently for each of the tested ballasts due to the different technical specifications and different THD.

Fig. 12. Relative consumed power and corresponding control voltage for the 2nd pair of the tested ballasts.

Fig. 13. Relative consumed power and corresponding control voltage for the 3rd pair of the tested ballasts.

2.3. Power factor versus control voltage

L. Doulos et al. / Energy and Buildings 40 (2008) 36–50

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Fig. 14. Relative consumed power and corresponding control voltage for the 4th pair of the tested ballasts.

Fig. 17. Relative consumed power and corresponding control voltage for the 7th pair of the tested ballasts.

Fig. 15. Relative consumed power and corresponding control voltage for the 5th pair of the tested ballasts.

Fig. 18. Relative consumed power and corresponding control voltage for the 8th pair of the tested ballasts.

2.4. Relative consumed power versus light output ratio All the above measurements were used for the identification of the relationship between power consumption and corresponding light output ratio. Figs. 20–28 present the relative power consumption with regard to light output ratio and the

corresponding polynomial trend lines between light output ratio and consumed power for the tested ballasts. The standard deviations of consumed power for the same light output for each of the tested pairs of the EDBs were considered statistically insignificant again, equal to a confidence level of 0.05.

Fig. 16. Relative consumed power and corresponding control voltage for the 6th pair of the tested ballasts.

Fig. 19. Relative consumed power and corresponding control voltage for the 9th pair of the tested ballasts.

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Table 2 Power factor values at maximum and minimum dimming level of the tested electronic dimming ballasts Ballast

Power factor at maximum dimming level

Power factor at minimum dimming level

Ballast

Power factor at maximum dimming level

Power factor at minimum dimming level

1Ai 2Ai 3Ai 4Aii 5Aii 6Aiii 7Aiv 8Aiv 9Av

0.9851 0.9675 0.9810 0.9831 0.9938 0.9999 0.9881 0.9804 0.9999

0.7443 0.7551 0.7132 0.6293 0.7561 0.8574 0.8235 0.7666 0.8654

1Bi 2Bi 3Bi 4Bii 5Bii 6Biii 7Biv 8Biv 9Bv

0.9847 0.9744 0.9819 0.9847 0.9940 0.9977 0.9879 0.9790 0.9978

0.7405 0.7723 0.7167 0.6345 0.7615 0.8454 0.8241 0.7723 0.8612

Fig. 29 illustrates the overlapping range of relative consumed power versus the light output ratio as the ballasts dim the lamps. The test results for the A ballasts show significant deviations between consumed power for the same light output ratio. For example, the deviation in consumed

power reached 10.95% at a light output level of 50% between 1Ai (46.84%) and 4Aii ballast (57.79%). Also the minimum relative consumed power was significantly different at the minimum light output ratio (1%). Deviation of consumed power reached 7.40% between 2Ai (20.26%) and 7Aiv ballast

Fig. 20. Relative consumed power and corresponding light output ratio for the 1st pair of the tested ballasts and the corresponding polynomial trend lines.

Fig. 22. Relative consumed power and corresponding light output ratio for the 3rd pair of the tested ballasts and the corresponding polynomial trend lines.

Fig. 21. Relative consumed power and corresponding light output ratio for the 2nd pair of the tested ballasts and the corresponding polynomial trend lines.

Fig. 23. Relative consumed power and corresponding light output ratio for the 4th pair of the tested ballasts and the corresponding polynomial trend lines.

L. Doulos et al. / Energy and Buildings 40 (2008) 36–50

Fig. 24. Relative consumed power and corresponding light output ratio for the 5th pair of the tested ballasts and the corresponding polynomial trend lines.

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Fig. 27. Relative consumed power and corresponding light output ratio for the 8th pair of the tested ballasts and the corresponding polynomial trend lines.

Fig. 25. Relative consumed power and corresponding light output ratio for the 6th pair of the tested ballasts and the corresponding polynomial trend lines. Fig. 28. Relative consumed power and corresponding light output ratio for the 9th pair of the tested ballasts and the corresponding polynomial trend lines.

Fig. 26. Relative consumed power and corresponding light output ratio for the 7th pair of the tested ballasts and the corresponding polynomial trend lines.

Fig. 29. Relative consumed power and corresponding light output ratio for the A tested ballasts.

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Table 3 Derived functions from polynomial interpolation between light output ratio (x, 0 to 1) and consumed power (y, 0 to 1) Ballast

Derived functions

R2

1Ai 1Bi 2Ai 2Bi 3Ai 3Bi 4Aii 4Bii 5Aii 5Bii 6Aiii 6Biii 7Aiv 7Biv 8Aiv 8Biv 9Av 9Bv

y = 0.977706x6 + 0.239089x5  3.679707x4 + 4.088178x3  1.662223x2 + 0.842162x + 0.159039 y = 9.924819x6  26.847866x5 + 28.219949x4  13.827847x3 + 2.976032x2 + 0.394242x + 0.173705 y = 0.940967x4  1.623302x3 + 0.843554x2 + 0.623477x + 0.196240 y = 1.070045x4  1.863278x3 + 1.054618x2 + 0.525632x + 0.199747 y = 11.971834x6 + 37.406467x5  43.167761x4 + 22.733779x3  5.391217x2 + 1.274263x + 0.118789 y = 11.655003x6 + 36.078653x5  40.844462x4 + 20.876606x3  4.794888x2 + 1.218423x + 0.122187 y = 1.996558x5  4.950908x4 + 4.513234x3  1.832852x2 + 1.116802x + 0.160585 y = 2.004026x5  5.135495x4 + 4.858207x3  2.053187x2 + 1.163743x + 0.158028 y = 5.421484x6  12.397328x5 + 9.791266x4  2.458107x3  0.385793x2 + 0.902573x + 0.123755 y = 4.288371x5  9.503951x4 + 7.705963x3  2.662753x2 + 1.047512x + 0.122704 y = 1.666712x6  5.005310x5 + 5.299045x4  1.831090x3  0.224156x2 + 0.932445x + 0.161000 y = 2.446988x6  8.847862x5 + 11.665299x4  6.538234x3 + 1.355865x2 + 0.761782x + 0.151381 y = 3.421217x6 + 12.434566x5  17.086818x4 + 11.699729x3  4.079106x2 + 1.340429x + 0.115611 y = 8.257493x6  21.976922x5 + 21.862671x4  9.299340x3 + 1.210752x2 + 0.815512x + 0.123314 y = 12.957250x6  36.344419x5 + 39.277755x4  20.370448x3 + 4.991440x2 + 0.290365x + 0.196582 y = 8.567832x6  24.136848x5 + 26.047660x4  13.216629x3 + 3.026019x2 + 0.515818x + 0.189789 y = 10.920384x6 + 30.105357x5  30.655581x4 + 14.777570x3  3.531097x2 + 1.089238x + 0.138061 y = 16.186624x6  46.055120x5 + 49.402729x4  24.460930x3 + 5.640250x2 + 0.115409x + 0.162542

0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9998 0.9999 0.9996 0.9995 0.9999 0.9999 0.9998 0.9999 0.9999 0.9996

(12.86%) at the minimum light output ratio. Thus, the tested EDBs consumed different amounts of the necessary power to maintain the arc across the lamp. Table 3 shows the derived functions from polynomial interpolation between power consumption, as y value varying from 0 to 1 and light output ratio, as x value varying from 0 to 1 for the tested ballasts. The R2 values varied from 0.9995 for 6Biii ballast to 0.9999 for 7Aiv ballast for the above described test conditions. These best-fit functions can be used to predict energy savings of the electric lighting in daylight responsive dimming systems controlled by photosensors and quantify differences of energy savings between the tested ballasts.

the purpose of this study, the room was assumed to be empty while, the electric lighting system consisted of six surface mounted fluorescent luminaires with parabolic louvers in a uniform layout. A photosensor with an ideal cosine spatial sensitivity distribution was placed in the geometrical center of the room mounted on the ceiling (Fig. 30). The point beneath the photosensor was used as the work plane illuminance

3. Simulations Simulations were performed using DAYSIM software [23]. DAYSIM has been used in previous photosensor studies and has been shown to compare reasonably well with other software [24,25]. The room (Figs. 30 and 31) that was used for the simulations of the daylight levels is a typical space in an office building and has been used in the past for IEA task 27 and SWIFT projects [26,27]. The windows were located on the south facade of the building. The optical properties of the elements inside the office room are presented in Table 4. For Fig. 31. Layout of south fac¸ade of the office room.

Table 4 Optical properties of the elements inside the office room

Fig. 30. Vertical cross section of the office room and location of the photosensor.

Elements of the room

Reflectance

Transmittance

Ceiling Walls Floor Door Window frame Door frame Window

0.85 0.65 0.20 0.40 0.80 0.80 –

– – – – – – 0.55

L. Doulos et al. / Energy and Buildings 40 (2008) 36–50

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Table 5 Operational equations for different control algorithms [14] Control algorithm Integral reset

Transfer function

Task illuminance

Conditional expression

ST ðtÞ ¼ SEm

  ðtÞ I T ðtÞ ¼ I D ðtÞ þ I Em 1  SSDEm

I D ðtÞ SD ðtÞ

¼ SI Em Em

I D ðtÞ SD ðtÞ

¼ SI DDðtðtcal ÞÞ

ðtÞ d ¼ 1  SSDEm

d ¼ MðST ðtÞ  SEm Þ þ 1

Closed loop

I T ðtÞ ¼ I D ðtÞ þ I Em

D ðtÞSEm Þ or d ¼ 1þMðS 1MSEm

M ¼ I D ðt



1þMðSD ðtÞSEm Þ 1MSEm



cal

I D ðtcal Þ cal ÞSEm I Em SD ðtcal Þ

ST(t): signal produced by photosensor (time dependent); SD(t): daylight component of ST(t); SD(tcal): daylight component of ST(t) at calibration time; SE(t): electric light component of ST(t); d: fractional output of electric lights (dmin  d  1). Full light output d = 1, minimum light output d = dmin; IEm: task illuminance level for d = 1 without daylight; SEm: signal produced by photosensor for d = 1 without daylight; IT(t): total light at task (time dependent); ID(t): daylight at task (time dependent); ID(tcal): daylight at task at calibration time; IE(t): electric light at task (time dependent).

reference at the standard desk height. The photosensor was oriented with its maximum response towards the work plane. The preferred dimming level for a given daylight situation was the electric light output for which the work plane illuminance equaled the design illuminance. Simulations have been performed on an hourly basis for a typical year using Athens Greece TMY. Two control algoritms were tested:  closed loop;  integral reset. The following table (Table 5) presents the operational equations for these two control algorithms that must be satisfied in order for the control to be achieved [14]. The condition of the electric lighting system (nighttime condition) was added to determine the photosensor signal SEm and the task illuminance IEm, when only electric light is received from the photosensor. The simulated system provided an average of 505 lx across the room (Reference height 0.75 m, Uniformity Emin/Emax 0.64 and maintenance factor 0.67) at full light output with a predefined design illuminance of 500 lx. The simulation procedure gave the daylight illuminance at work plane ID(t) and the daylight component of the signal that was produced by the photosensor SD(t) on an hourly basis for a

typical year, using as operation schedule, all days from 8:00 a.m. to 18:00 p.m. The most common ratio ID(t)/SD(t) of all the simulated ID(t) and SD(t) values, was selected as the conditional expression for the closed loop control algorithm ID(tcal)/SD(tcal). Thereby the daytime calibration was concluded by adjusting the slope of the algorithm response at an appropriate time during the day where ID(t) and SD(t) values did not exceed the design illumination (500 lx). Neither correlations between task illuminance and photosensor signal for the location of the photosensor, nor evaluation of the system for the optimum performance of the control algorithms were performed analytically in this study. The purpose of the simulations was to create a reference signal for all the tested EDBs and thus check the influence of different EDBs on a specific control signal. Nevertheless, location, aiming, commissioning, and calibration procedures of the photosensor were adapted from previous studies [8–10,13– 15,28–31] in order to have a reliable performance of the daylight responsive dimming system. Giving the necessary inputs for the operational equations of each control algorithm (Table 5), an appropriate dimming percentage d was determined from the output of the photosensor and light controller. By using this dimming percentage d as the lighting output ratio to the derived functions from the measurements of the tested EDBs (Table 3), the consumed

Table 6 Monthly and annual energy savings for the A ballasts for the closed loop algorithm Energy savings (%) for closed loop algorithm

January February March April May June July August September October November December Annual a

Ballast.

1Ai a

2Ai a

3Ai a

4Aiia

5Aiia

6Aiii a

7Aiva

8Aiva

9Ava

63.82 69.51 73.33 74.64 76.13 76.93 76.93 76.23 74.21 68.83 62.55 60.86 71.17

58.92 64.69 68.87 70.41 72.33 73.41 73.40 72.46 69.99 64.62 57.83 55.88 66.91

63.35 69.67 74.17 75.83 77.81 78.85 78.85 77.94 75.28 69.35 62.11 60.04 71.95

58.96 64.97 69.43 71.06 73.04 74.16 74.15 73.18 70.58 64.91 57.83 55.81 67.35

64.74 70.86 75.00 76.43 78.13 79.10 79.10 78.25 76.03 70.31 63.36 61.50 72.74

60.05 66.10 70.45 71.99 73.83 74.89 74.89 73.95 71.58 65.80 58.93 56.97 68.29

63.32 69.54 73.87 75.36 77.09 78.06 78.06 77.21 74.91 68.96 62.06 60.15 71.55

59.64 65.47 69.56 71.05 72.96 74.08 74.07 73.09 70.69 65.41 58.40 56.47 67.58

66.66 70.76 73.90 75.07 76.36 77.23 77.24 76.50 74.96 70.91 65.85 64.47 72.50

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power of the system and the differences in energy savings between the tested ballasts could be estimated. 4. Results Tables 6 and 7 show the monthly and annual average energy savings for the closed loop algorithm for each tested EDB. More analytically, Table 6 gives the monthly and annual average energy savings for the A ballasts of each pair. The absolute maximum value of the monthly average energy savings was 79.10% for the 5Aii ballast in June and July. The maximum annual average energy saving value was 72.74% for the 5Aii ballast. On the contrary, the absolute minimum monthly value was 55.81% for the 4Aii ballast in December while, the minimum average value of the annual calculated energy savings was 66.91% for the 2Ai ballast. Table 7 presents the monthly and annual average energy savings for the B ballasts of each pair. The absolute maximum value of the monthly energy savings and the maximum average value of the annual energy savings were 79.20% (June and July) and 72.82% for the 5Bii ballast, respectively. On the other hand,

the absolute minimum value of the monthly energy savings and the minimum average value of the annual energy savings were 55.94% (December) and 67.37% for the 4Aii ballast, respectively. The differences in annual energy savings, comparing the Agroup of ballasts with the corresponding B ballasts, were insignificant. In total, the differences varied from 0.02% (between the 4Aii and 4Bii ballast) to 0.51% (between the 2Ai and 2Bi ballast). Only for the 6th (1.23%) and the 7th (1.07%) pair of the tested ballasts, the differences were slightly increased in comparison with the other pairs. These minimal differences resulted mainly from the differences in the derived polynomial functions (Table 3). Tables 8 and 9 show the monthly and annual average energy savings for the case of the integral reset algorithm, for each tested EDB. The absolute maximum value of the monthly energy savings and the maximum average value of the annual energy savings were 79.24% (May, June, July and August) and 76.09% for the 5Bii ballast, respectively. On the contrary, the absolute minimum value of the monthly energy savings and the minimum average value of the annual energy savings were

Table 7 Monthly and annual energy savings for the B ballasts for the closed loop algorithm Energy savings (%) for closed loop algorithm

January February March April May June July August September October November December Annual a

1Bia

2Bi a

3Bia

4Biia

5Biia

6Biiia

7Biva

8Biva

9Bva

63.45 69.43 73.20 74.45 75.93 76.80 76.80 76.03 74.13 68.61 62.02 60.30 70.93

59.46 65.28 69.44 70.96 72.82 73.85 73.84 72.95 70.52 65.09 58.34 56.41 67.42

63.28 69.57 73.98 75.61 77.57 78.64 78.63 77.71 75.10 69.25 62.03 59.98 71.79

59.05 65.00 69.43 71.05 73.02 74.13 74.13 73.16 70.58 64.96 57.94 55.94 67.37

64.77 70.90 75.09 76.56 78.27 79.20 79.20 78.39 76.08 70.34 63.40 61.53 72.82

60.99 67.19 71.70 73.30 75.25 76.39 76.38 75.38 72.90 66.98 59.90 57.85 69.52

64.64 70.77 74.87 76.27 77.97 78.99 78.99 78.09 75.93 70.23 63.30 61.43 72.63

59.45 65.28 69.42 70.92 72.79 73.88 73.87 72.92 70.54 65.15 58.27 56.35 67.41

64.56 70.63 74.84 76.28 77.96 78.91 78.91 78.08 75.86 70.11 63.23 61.37 72.57

Ballast.

Table 8 Monthly and annual energy savings for the A ballasts for the integral reset algorithm Energy savings (%) for integral reset algorithm

January February March April May June July August September October November December Annual a

Ballast.

1Aia

2Aia

3Aia

4Aiia

5Aiia

6Aiiia

7Aiva

8Aiva

9Ava

69.54 74.78 76.61 76.96 76.96 76.96 76.96 76.96 76.83 72.03 67.79 66.92 74.10

65.26 70.84 72.99 73.45 73.45 73.45 73.45 73.45 73.29 67.92 63.89 62.94 70.35

70.09 76.18 78.42 78.89 78.89 78.89 78.89 78.89 78.73 72.99 68.49 67.49 75.56

65.61 71.44 73.72 74.20 74.20 74.20 74.20 74.20 74.03 68.33 64.21 63.21 70.95

71.04 76.71 78.73 79.14 79.14 79.14 79.14 79.14 79.00 73.77 69.18 68.20 76.02

66.53 72.30 74.49 74.93 74.93 74.93 74.93 74.93 74.78 69.21 65.10 64.17 71.76

69.74 75.58 77.69 78.10 78.10 78.10 78.10 78.10 77.95 72.48 68.07 67.13 74.92

66.03 71.54 73.66 74.12 74.13 74.13 74.13 74.13 73.97 68.71 64.42 63.39 71.02

69.10 75.10 77.22 77.63 77.63 77.63 77.63 77.63 77.48 71.88 67.60 66.73 74.43

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Table 9 Monthly and annual energy savings for the B ballasts for the integral reset algorithm Energy savings (%) for integral reset algorithm

January February March April May June July August September October November December Annual a

1Bia

2Bia

3Bi a

4Biia

5Biia

6Biiia

7Biva

8Biva

9Bva

69.32 74.70 76.48 76.83 76.84 76.84 76.84 76.84 76.71 71.96 67.31 66.38 73.91

65.75 71.33 73.44 73.89 73.89 73.89 73.89 73.89 73.74 68.40 64.31 63.37 70.80

69.97 76.01 78.21 78.67 78.68 78.68 78.68 78.68 78.52 72.88 68.35 67.35 75.38

65.65 71.42 73.70 74.17 74.18 74.18 74.18 74.18 74.01 68.34 64.28 63.29 70.95

71.07 76.79 78.83 79.23 79.24 79.24 79.24 79.24 79.09 73.81 69.23 68.25 76.09

67.72 73.68 75.96 76.43 76.43 76.43 76.43 76.43 76.27 70.48 66.34 65.39 73.15

70.95 76.61 78.62 79.03 79.04 79.04 79.04 79.04 78.89 73.68 69.14 68.16 75.92

65.80 71.34 73.47 73.92 73.92 73.92 73.92 73.92 73.76 68.45 64.28 63.30 70.82

70.85 76.47 78.55 78.94 78.95 78.95 78.95 78.95 78.80 73.49 69.06 68.08 75.82

Ballast.

Table 10 Annual energy savings from minimum to maximum and their differences between the A ballasts Closed loop

Integral reset

Ballast

Annual energy savings (%)

Energy saving difference (%)

Ballast

Annual energy savings (%)

Energy saving difference (%)

2Ai 4Aii 8Aiv 6Aiii 1Ai 7Aiv 3Ai 9Av 5Aii

66.91 67.35 67.58 68.29 71.17 71.55 71.95 72.50 72.74

– 0.44 0.67 1.38 4.26 4.65 5.04 5.59 5.83

2Ai 4Aii 8Aiv 6Aiii 1Ai 9Av 7Aiv 3Ai 5Aii

70.35 70.95 71.02 71.76 74.10 74.43 74.92 75.56 76.02

– 0.60 0.66 1.40 3.74 4.07 4.56 5.20 5.66

62.94% (December) and 70.35% for the 2Ai ballast, respectively. As in the case of the closed loop control algorithm, the differences in annual energy savings, comparing the A ballasts with the corresponding B ballasts for the case of the integral reset algorithm were also insignificant. In order to investigate the annual differences in the calculated energy saving values between the tested EDBs, a sorting was made for A and B ballasts from the ballast with the minimum energy saving value to the one with the maximum. Tables 10 and 11 present this sorting and the differences in energy savings between the tested ballasts for the two control

algorithms. The maximum difference between the A ballasts was estimated equal to 5.83% (between 2Ai and 5Aii) for the case of the closed loop algorithm and 5.66% (also between 2Ai and 5Aii) for the case of the integral reset algorithm. For B ballasts, the maximum relative difference was 5.44% (between 4Bii and 5Bii) for the case of the closed loop algorithm and 5.29% (between 2Bi and 5Bii) for the case of the integral reset algorithm. Totally, the maximum annual difference in the energy saving values between the tested EDBs was estimated equal to 5.91% (between 2Ai and 5Bii) for the case of the closed loop algorithm and 5.74% (also between 2Ai and 5Bii)

Table 11 Annual energy savings from minimum to maximum and their differences between the B ballasts Closed loop

Integral reset

Ballast

Annual energy savings (%)

Energy saving difference (%)

Ballast

Annual energy savings (%)

Energy saving difference (%)

4Bii 8Biv 2Bi 6Biii 1Bi 3Bi 9Bv 7Biv 5Bii

67.37 67.41 67.42 69.52 70.93 71.79 72.57 72.63 72.82

– 0.04 0.05 2.15 3.56 4.41 5.19 5.25 5.44

2Bi 8Biv 4Bii 6Biii 1Bi 3Bi 9Bv 7Biv 5Bii

70.80 70.82 70.95 73.15 73.91 75.38 75.82 75.92 76.09

– 0.02 0.15 2.35 3.10 4.57 5.02 5.12 5.29

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L. Doulos et al. / Energy and Buildings 40 (2008) 36–50

Fig. 32. Monthly and annual differences in energy savings between the A ballasts for the closed loop algorithm.

Fig. 34. Light levels on workplane for integral reset control algorithm (3rd of January).

for the case of the integral reset algorithm. Figs. 32 and 33 present the monthly energy savings between 2Ai and 5Aii for the closed loop and integral reset algorithm, respectively. The lower and upper lines of the box plots represent minimum and maximum values. By taking into account only the calculated energy savings, this study showed a better performance for the integral reset than the closed loop algorithm. However, analyzing the implemented simulations; the visual comfort (based on the percentage of hours that the total illuminance levels exceed the target illuminance) has fallen short. There were a remarkable number of hours that integral reset control performed poorly, since it dimmed out the lights despite the fact that the daylight levels were inadequate. It should be mentioned here that the aim of this study is to quantify energy savings among different EDBs under two common control algorithms. Nevertheless as an example, Figs. 34 and 35 present the workplane illuminance levels as a function of time for a randomly selected day (in our case the 3rd of January). The blue area in each graph shows the contribution of daylight and the

yellow area the contribution of the artificial lighting to the workplane illuminance for the indicated algorithm. The upper boundary of these two areas is the total illuminance at the workplane. The closed loop control algorithm performed much better than integral reset, providing sufficient electrical light to meet target illuminance for more hours during the day. Fig. 36 gives the monthly percentage of operational hours (07:00– 18:00) that the achieved illuminance levels are lower than the target illuminance (500 lx) for the two examined control algorithms. The light levels were below the target illuminance level for 10.74% of the yearly operational hours (closed loop algorithm) and 42.60% for the integral reset algorithm. Thus, it is evident that the proper choice of the control algorithm should be based firstly on its ability to maintain target illuminance and not on the energy savings achieved. The integral reset controller maintains a constant value on the sensor located on the ceiling. This results in progressively lower total illuminance levels on the workplane as daylight increases making this type of controller not so suitable for daylighting applications. Although the performance of integral reset

Fig. 33. Monthly and annual differences in energy savings between the A ballasts for the integral reset algorithm.

Fig. 35. Light levels on workplane for closed loop control algorithm (3rd of January).

L. Doulos et al. / Energy and Buildings 40 (2008) 36–50

49

the optimum choice of ballast is also required, because as presented the differences in energy savings can be significant. Recently, new simulation tools and software [32] have been developed, aiming at the calculation of energy savings due to daylight taking different ballast profiles into account. Measurements of these properties can help these programs to provide more reliable results since using default values for the ballast profiles may result in errors. Necessary developments that will help manufactures, lighting and building designers, contractors in daylighting design are:

Fig. 36. Percentage of total monthly hours that the workplane illuminance is lower than the target illuminance for two control algorithms, closed loop and integral reset.

controller can be improved by using a fully shielded sensor the shortfall in light levels in comparison with closed loop controller have been justified by other papers as well [14]. 5. Conclusions Lighting control system is a complex technology that changes rapidly. A variety of controllers, software, sensors and devices are currently available, but there is lack of information concerning the actual performance of these systems and control strategies. In order to fully exploit their capabilities and implement the most energy efficient control strategies, simulation software, reliable data from these components measured or provided from the manufacturers, official directives and guidelines are needed during the initial design phase. To overcome this barrier many studies have been conducted. The present study shows that in a daylight responsive system even a small modification in the specifications of the electronic dimming ballasts can turn in to significant differences in energy consumption. Eighteen ballasts have been tested. The relationships between light output ratio and control voltage, consumed power and control voltage, power factor and control voltage and between consumed power and light output ratio were identified. Best-fit functions between consumed power and light output ratio were also introduced and these can be used together with simulations to quantify differences of energy savings. The simulations were performed for a typical room in an office building, while the analysis of the results revealed that there are significant differences in energy savings due to tested ballasts. The differences in energy saving values between the same ballasts of each tested pair were insignificant. This result was predictable but the procedure helped to gain more accurate measurements and results. The control algorithm dominates the performance of the daylight responsive dimming system. Of course the spatial and spectral response functions of the photosensor, calibration and commissioning affect the overall operation as well. However,

 well defined ballast studies as benchmark;  a data base with control functions for control voltage, consumed power and lighting output ratio for a large number of ballasts;  guidelines for manufactures of electronic dimming ballasts and other lighting control components of the data that must be provided to lighting and building designers.

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