Design of an energy-saving controller for an intelligent LED lighting system

Energy and Buildings 120 (2016) 1–9

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Design of an energy-saving controller for an intelligent LED lighting system Ivan Chew a , Vineetha Kalavally a,∗ , Naing Win Oo a , Jussi Parkkinen b a b

Department of Electrical and Computer Systems Engineering, Monash University Malaysia, Malaysia School of Computing, University of Eastern Finland, Finland

a r t i c l e

i n f o

Article history: Received 13 July 2015 Received in revised form 15 March 2016 Accepted 16 March 2016 Available online 19 March 2016 Keywords: Daylight harvesting Energy saving Intelligent lighting LED lighting system Occupancy sensing Smart lighting

a b s t r a c t In this paper, we present an energy-saving controller that is capable of shaping the light output of an LED lighting system autonomously based on data received from sensors. We implement an optimized smart algorithm on a controller to process the sensor feedback and employ pulse width modulation dimming to vary the brightness of the luminaire. A wireless sensor module was designed to provide accurate sensor feedback to the controller. A purpose-built smart luminaire complete with an LED driver was designed and constructed to study the performance of the control system. We validate the energy saving potential of the designed controller under different real world situations. It is shown experimentally that the controller achieved 55% energy savings in a continuous usage pattern environment and 62% energy savings in a discrete usage pattern environment under our test conditions. A cost analysis showed that the proposed energy-saving system is 32% more cost-effective than a near-equivalent commercial system while promising greater energy savings through the use of additional energy-saving techniques. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Lighting contributes up to 20% of the world’s total energy usage [1]. Interestingly, commercial and office buildings account for up to 71% of the total energy usage, of which 18% is used for lighting [2]. The typical total annual energy usage in office buildings range from 100 to 1000 kWh/m2 , depending on geographic location, office equipment use, operating hours, use of HVAC systems, and installed lighting technologies, among other factors [3]. Recently, the 2010 Energy Performance of Buildings Directive (EPBD) emphasized the need for near-zero energy use levels in European buildings for a sustainable future [4]. Fortunately, research has indicated that modern buildings have a high potential for energy savings [5]. Electric lighting is a key target area for power consumption reduction as energy-saving lighting systems promise high energy savings and are relatively straightforward to retrofit. Intelligent lighting systems seek to achieve this by utilizing integrated sensors to provide feedback in a closed-loop control system. A common energy-saving technique is occupancy sensing, which obtains feedback from integrated occupancy sensors [6,7].

∗ Corresponding author. E-mail addresses: [email protected] (I. Chew), [email protected] (V. Kalavally), [email protected] (N.W. Oo), jussi.parkkinen@uef.fi (J. Parkkinen). http://dx.doi.org/10.1016/j.enbuild.2016.03.041 0378-7788/© 2016 Elsevier B.V. All rights reserved.

Occupancy sensing based energy-saving systems typically demonstrate 17–60% energy savings depending on occupant usage patterns [8]. However, most occupancy sensing systems report lower energy savings of around 3–50% when placed in environments with a continuous usage pattern [9,10]. These systems are normally based on single-point detection, which can potentially introduce significant uncertainty in the sensor data, especially if the lighting system is not tuned or optimized well [11]. Preset time delays are often introduced to compensate for this uncertainty. Some smart lighting systems also rely on manual user feedback to control the luminaires via remote control as a complement to occupancy sensing [12,13]. Other than that, techniques such as daylight harvesting and automatic dimming control can also be employed to increase energy savings [14–16]. Daylight harvesting takes advantage of natural light from building apertures to complement the artificial lighting from luminaires in order to reduce the brightness needed to achieve a certain level of illumination. Daylight-linked control systems can be very effective as most commercial and office spaces have sufficient daylight from windows to eliminate the need for electric lighting [17]. Additional techniques such as illumination balancing [18], enhanced presence sensing [19], and adaptive illumination rendering [20] can also be employed to enhance system performance. The reported energy savings from daylight-linked systems are typically above 40%; however, the effectiveness is highly dependent on multiple factors, including:

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altitude and orientation, window characteristics, shading devices, surface reflectance, ceiling height, and partition height [21,22]. As a result, the reported real world performance of daylight harvesting systems are generally much lower compared to the simulated performance [23]. It stands to reason that improved energy-saving performance can be achieved by combining multiple energy-saving techniques in a single control system [24]. The integration of daylight-linked control can alleviate the inherently poor energy-saving performance of occupancy sensing based systems in continuous usage pattern environments. Roisin et al. reported simulation results of between 49–63% for a combined occupancy sensing and daylight harvesting system [25] while Hughes et al. recorded up to 68% energy savings with a similar system [26]. However, it is also necessary to verify the performance of energy-saving systems in real world environments under different usage patterns as simulation results do not always provide an accurate representation of real world situations. Besides that, optimal system performance should be ensured through controller optimization or calibration. Controller optimization is an important step to ensure favorable system performance. An example of an optimization method is model predictive control, which is normally used in industrial process control systems, and more recently in power system balancing models [27]. This control method optimizes a finite-time horizon, while only implementing the current time slot. Recent works in this area include improving the stability of model predictive controllers for open-loop stable systems [28] and implementing a model predictive controller on an FPGA based system to improve the on-line computational performance [29]. Another popular optimization method is the hill climbing optimization technique which is an iterative algorithm that attempts to find the optimal solution by incrementally changing a single element during each iteration. Benefits of hill climbing optimization include its relative simplicity and excellent ability to find a local optimum solution in a search space. Recent research has shown stochastic hill climbing optimization to be effective when applied to lighting control [30]. In this paper, we combine multiple autonomous energy-saving techniques to achieve greater energy savings. A smart lighting system was developed as a platform to implement and optimize our energy-saving controller. The performance of the controller was validated experimentally under different usage patterns, which should be an accurate indication of the real-world performance of the system, offering a different approach compared to popular simulation-based methods. In addition to that, we also perform a cost analysis on the energy-saving controller to further quantify the impact of the energy saving system. The rest of this paper is structured as follows: Section 2 describes the energy saving techniques used in the implemented control algorithm and discusses the controller optimization; Section 3 presents the system design; we verify the energy saving performance of the controller through experimentation under different usage patterns in Section 4; Section 5 describes a cost analysis of the system, and finally the conclusion is discussed in Section 6.

2. Energy saving techniques It is apparent that a substantial amount of energy can be saved by exercising a greater degree of control over the lights that we use. Therefore, we choose suitable sensors to provide feedback on relevant information to a 16 MHz 8-bit AVR RISC-based microcontroller. We use a passive infrared (PIR) sensor which measures infrared light radiating from objects to provide feedback on the occupancy status within its field of view. When a human passes through the sensor’s field of view, the sensor will convert the resulting change in the infrared radiation into a change in sensor voltage,

Fig. 1. The relative spectral response of the TEMT6000 ambient light sensor over the visible light region [31]. The spectral response is adapted to match the human eye responsitivity.

which triggers the detection. This sensor returns a logic high signal when movement is sensed at distances of up to 7 meters in a 110◦ cone field of view. The PIR sensor is used for occupancy sensing, where we program the luminaire to automatically turn off when there are no users within the field of view. We allow the user to define a suitable timeout, . The timeout is the time difference between a logic high signal from the PIR sensor and the luminaire output dropping to zero. A small timeout value will ensure significant energy savings but may lead to the luminaire turning off while there are users in the room if they are motionless for a period of time. A larger timeout value will solve this problem, but will lead to lower energy savings. We also utilized a light sensor, which is a silicon NPN epitaxial planar phototransistor that is sensitive to the visible spectrum. The incident illuminance on the sensor is directly proportional to the collector light current. We chose a TEMT6000 ambient light sensor [31] which can measure the incident illuminance up to 1000 lx with peak sensitivity at around 580 nm, with a spectral sensitivity curve which is adapted to match the human eye responsitivity (shown in Fig. 1). The sensor output analog photo-current is converted to a variable voltage (0–5 V) that is read by the micro-controller by connecting a 10 K series resistor. Consider a room with a window. During the day, the user may elect to leave the curtains open which allows sunlight to illuminate the room. In this situation, the artificial lighting may be redundant as there is more than enough ambient light in the room to illuminate the work space. We can harness the ambient light to complement the existing lighting, which is a technique called daylight harvesting. This will lead to increased energy savings as the luminaires need not be constantly switched on at maximum brightness. 2.1. Control algorithm We implement a control system that automatically adjusts the luminaire state based on feedback from the sensors. The block diagram energy saving control system is shown in Fig. 2. The user defined inputs to the control system are the target brightness (SP), gain (K), hysteresis (H), timeout (), and sampling period (T). The non-user definable inputs to the control system are the user presence [M(t)] and the measured brightness [PV(t)]. These non-user definable inputs are obtained via feedback from the sensors. The maximum ambient light intensity is assumed to be within the sensing limits of the ambient light sensor. Furthermore, the sampling period is assumed to be large enough to account for the clock speed of the controller. Table 1 summarizes the inputs for the energy saving control system.

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Algorithm 1. 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16:

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Psuedocode for the energy-saving controller

if movement ← 1 then DaylightHarvesting ← 1; timeoutPIR ← millis() + timeout; movement ← 0; end if while DaylightHarvesting ← 1 do AverageLightData(); error = LightData - SP; if error > hysteresis then brightness = brightness - gain; end if if error < -hysteresis then brightness = brightness + gain; end if end while analogWrite(light, brightness);

movement is sensed begin daylight harvesting reset PIR timeout

average ambient light data calculate error adjust brightness

adjust brightness

write duty cycle

Fig. 2. The block diagram of the energy saving control system. Table 1 The control inputs. Inputs

Symbol

Description

Target brightness Gain

SP K

Hysteresis

H

Timeout



Sampling period

T

User presence

M(t)

Measured brightness

PV(t)

The targeted brightness value in terms of lux measured by the light sensor. User definable input. Step size of the output duty cycle adjustment per clock cycle. This value is a positive integer. At K = 1, the duty cycle is increased by 0.39% per clock cycle (the step size for an 8 bit integer from 0 to 255 corresponds to the minimum and maximum duty cycle respectively). User definable input. The hysteresis defines the acceptable range of errors in terms of brightness (lux) before the controller starts to adjust the duty cycle of the luminaire. User definable input. Time delay in seconds between a returned logic high signal from the occupancy sensor and the luminaire switching off. User definable input. Time period in seconds between each subsequent reading of the measured process variable. User definable input. The current state of the occupancy sensor. This is either logic high or logic low. Non-user definable input. The instantaneous measured brightness value in terms of lux measured by the light sensor. Non-user definable input.

The target brightness, SP is stored in the EEPROM section of the microcontroller as non-volatile read-only memory. Based on the inputs, the controller adjusts the pulse width modulated (PWM) signal, p(t) that is sent to the LED driver. A PWM dimming scheme was chosen over a constant current reduction dimming scheme due to the lower chromaticity shift when the LEDs are dimmed using pulse width modulation [32]. By varying the duty cycle, d(t) of this PWM signal, the controller can regulate the LED string current, and as a result, the LED brightness. The controller measures the duty cycle as an unsigned 8 bit integer (0–255). A PWM frequency of 1000 Hz was chosen to mitigate unwanted biological effects such as headaches, nausea, and seizures that could arise due to invisible flicker at low frequencies up to 165 Hz [33]. The goal of the control system is to minimize the error, e(t) within the bounds of the acceptable range of errors. The error signal is calculated with: e(t) = SP − PV (t)

From Eq. (2), it can be seen that the user presence, M(t) has the highest impact on the state of the control system followed by the error signal, e(t). It is also notable that the rise time of the duty cycle is dependent on the gain (K) and that the error tolerance is dependent on the hysteresis (H) variable of the control system. The rate of change is dependent on the user definable sampling period (T). The response of the system to the control algorithm is shown in a timing diagram in Fig. 3. The system responds on the rising and falling edge of the clock signal at the controller clock speed of 16 MHZ. In practice, there will be a small rise time and fall time at the edges of each signal. The light sensor data feedback rate and wireless transmission frequency is dependent on the sampling period of the control system. 2.2. Controller optimization We can optimize the controller by tuning certain input parameters: the gain (K), hysteresis (H), and sampling period (T). Varying any of these parameters will have an effect on the response of the control system. In this section, we study the control system response through experimentation to provide a guideline on how to optimize the controller. We first define a base case where K = 1, H = SP/20, T = 200 ms and SP = 1200 lx at 0.5 m from the luminaire. K, H and T are then varied individually to observe the controller response by measuring the luminaire output power until steady state is reached. First, we vary the sampling period, T. The results are shown in Fig. 4(a). We observed that as T increases, the response time of the controller decreases, where a 200 ms increase in the sampling period results in a 30 s decrease in the controller response time. In the case of T = 800 ms, the controller takes almost two minutes to reach its steady state. However, the sampling rate is limited by the bandwidth of the wireless module, which transmits the sampled data and associated packet header. We found the minimum sampling period to be T = 200 ms to ensure reliable wireless data transmission. A lower sampling period also results in a more abrupt

(1)

The controller attempts to minimize the error by adjusting the duty cycle, d(t) of the PWM signal that is sent to the LED driver. The controller regulates d(t) with the equation:



d(t) =

M(t)[d(t) + K]

if e(t) ≥ H

M(t)[d(t) − K]

if e(t) ≤ −H

(2)

Fig. 3. The timing diagram of the control system with 0s rise time and the PIR timeout,  = 0.

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Fig. 4. The controller response over 120 s with varying paramenters.

Fig. 5. The developed smart luminaire.

change in the illumination level which may not be comfortable for the user. Also, decreasing the sampling period increases the processing load on the controller. Next, we observe the effect of varying the gain, K which is the step size between subsequent duty cycle adjustments. The results are shown in Fig. 4(b). We observed that the response time of the controller increases when K increases which means that the system converges faster toward its steady state. In the case of K = 1, it takes around 30 s for the controller to achieve its steady state. This suggests that the response time of the system is a function of the gain and the sampling period. However, a large gain will lead to overshoot, especially when the error is small as can be seen when K = 10. When there is an overshoot, sinusoidal oscillation around the target PV will occur as the system attempts to correct itself by decreasing the duty cycle. This is not acceptable for general illumination if the light flickers at a frequency that is visible to humans. Finally, we vary the hysteresis, H which is the acceptable range of errors before the controller begins adjusting its output and is defined as a function of the set point, SP. The hysteresis helps to stabilize the controller in noisy environments. If the hysteresis value is not significant enough, it will lead to a constantly varying output which will result in noticeable flickering at lower frequencies as shown in Fig. 4(c). Based on the results, we notice that a large hysteresis can lead to inaccuracy in the steady state level of the controller. There is a noticeable deviation from the target set point when H = SP/5. The deviation is even larger when H = SP/2. We also observe that the hysteresis has no effect on the response time of the system, as evidenced by the similar gradient, dP/dt for different values of H. Based on the experimental results, the recommended input parameters for the controller are K = 2, H = SP/20, and T = 200 ms. These parameters were selected to give an optimal rise time,

a stable steady state performance and an accurate steady state output level. 3. Smart luminaire design A smart LED luminaire was designed and constructed to implement the proposed controller. The smart luminaire consists of an LED driver, LED strings, a ZigBee module, a microcontroller and sensors. A wireless sensor module was also designed to interface the ambient light sensor to smart luminaire via the IEEE 802.15.4 ZigBee standard. We place the ambient light sensor at the work plane level rather than with the luminaire to allow the ambient light sensor to collect ambient light data accurately at work plane height. Fig. 5(a) describes the relationship between all the system components; the constructed smart lighting system and wireless sensor module are shown in Fig. 5(b). 3.1. The LED driver An LED driver with high electrical efficiency is important to achieve higher energy savings. We designed a DC–DC LED driver to power eight LED strings at a maximum string current of 700 mA for each channel. We used the Texas Instruments LM3406 constant current buck converter as the power controller of our driver. The LM3406 contains a high side N-channel MOSFET switch with a suitable current limit. It also has a dimming pin that can be driven by the ATMega328 microcontroller to achieve step-wise dimming using PWM. Through experimentation, we verified that the brightness of the LED strings driven by our designed LED driver is directly proportional to the string current and the duty cycle of the PWM signal as shown in Fig. 6.

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Fig. 8. The power consumption of the luminaire visualized with and without the control system. Fig. 6. The brightness of the luminaire at 1.6 m and the PWM duty cycle versus the LED string current.

Fig. 9. Test setup in the laboratory. The luminaire was switched on for 8 h and the power consumption data recorded.

Fig. 7. The input and output power consumption of the designed LED driver.

We used a Tektronix PA1000 power analyzer to measure the power consumption of the luminaire. The electrical power input at the maximum duty cycle was measured at 82.08 W. The electrical power output at the maximum duty cycle for all channels was measured at 75.86 W, which translates to a 92.41% maximum electrical efficiency. Fig. 7 illustrates the variance in the input power and output power over the entire dimming range. From the graph, we notice that the efficiency remains near constant over the entire duty cycle range. In fact, the average electrical efficiency was measured at 91.96%, which is close to the maximum measured efficiency. 4. Experimental power consumption analysis The amount of power saved can be calculated with the following equation:



Psaved (t) =



t

P0 (t)dt − 0

t

Pc (t)dt

(3)

0

where Pc (t) and P0 (t) represent the output power of the designed luminaire with and without the implemented control system respectively. Fig. 8 shows a visualization of the power consumption of the lighting system with and without the control system implemented. From the figure, we can see that the energy consumed is equivalent the area under the graph. Fig. 8 also shows the response of the control system to the various inputs that were defined by the user. We investigated the power consumption of the luminaire in two different environments. Pc (t) and P0 (t) were logged to calculate Psaved (t). In the first experiment, the luminaire was placed in

a laboratory that is used continuously throughout the day. In the second experiment, the luminaire was tested in a classroom which experiences a discrete usage pattern according to a timetable. We present the results in the following section.

4.1. Continuous usage pattern environment First, we tested the luminaire in a laboratory with a continuous usage pattern. The luminaire was placed near two windows to allow a large variance in the ambient light during the test. The test area has pre-installed fluorescent lights. The brightness due to the fluorescent lights alone was measured to be 467.2 lx at table height (0.8 m) using a Konica Minolta CL-200A chroma meter before the luminaire was switched on. We switched on the luminaire at the required brightness to produce SP = 1200 lx at 1 m in a dark room for a six hour period (1.30 PM-7.30 PM) to record our base results for comparison. The power consumption was recorded. Then, we included our closed-loop control system and switched on the luminaire for a similar six hour period and measured the power consumption again. The power consumption was logged using a Tektronix PA1000 power analyzer at an interval of 10 s. Fig. 9 shows our test setup. The results of the test are shown in Fig. 10(a). The power consumption of the luminaire with the implemented control system can be seen to increase over time as the ambient light in the room decreases. The power consumption is at its highest after the sun sets at around 7 PM. The average power consumption of the base case was recorded at 35.8 W over the six hour period. In contrast, the average power consumption of the luminaire with the control system implemented was measured at 19.43 W. The energy saved can be calculated by:

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Fig. 10. (a) Power consumption over time of the luminaire for a 6-hour period. We set t = 300 s, K = 1, H = SP/10, T = 200 ms, and SP = 1200 lx at 1 m. (b) The power consumption of the luminaire with and without the proposed control system for a 7 day period. We set t = 300 s, K = 1, H = SP/10, T = 200 ms, and SP = 1200 lx at 1 meter.

Fig. 11. The effect of varying the ambient light conditions on the luminaire brightness in a classroom. (a) The ambient light level is set to maximum. (b) The ambient light level is set to 50%. (c) The ambient light level is set to 25%. (d) The ambient light level is set to minimum.

Ps (t) =

Psaved (t) × 100% P0 (t)

(4)

Under the test conditions, the addition of the control system saved 16.37 W per hour or 98.22 W h over the test period. This translates to 45.73% energy savings during this test period. We then logged the power consumption of the luminaire for a seven day period with and without the control system. The luminaire was switched on for seven days from 8 AM to 8 PM (12 h) during the normal laboratory working hours. The results are summarized in Fig. 10(b). The average daily power consumption without the proposed control system was measured at 70.94 W h while the average daily power consumption with the proposed control system was measured at 31.89 W h. By implementing the proposed control system, we managed to achieve energy savings of 55.05% over the 7 day test period.

4.2. Discrete usage pattern environment We also installed a luminaire in a classroom with a discrete usage pattern to analyze the power consumption of the luminaire with the designed control system (shown in Fig. 11). The classroom has pre-installed fluorescent lights. The brightness due to the fluorescent lights alone was measured to be 536.8 lx at table height (0.8 m) using a Konica Minolta CL-200A chroma meter before the luminaire was switched on. The power consumption was logged using a Fluke 345 Power Quality portable clamp meter at an interval of 10 s. The average power consumption of the reference test where the luminaire was switched on at all times (8.30 AM–3.30 PM) was 14 W. In contrast to this, the average power consumption of the luminaire with the implemented control system was 5.2 W. We calculated that the control system implementation managed to save

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Fig. 12. Power consumption from 8.30 AM to 3.30 PM for a typical day in a classroom. We set t = 300 s, K = 1, H = SP/10, T = 200 ms, and SP = 1200 lx at 2 m.

Table 2 Power consumption analysis for a discrete usage pattern environment. Region (time)

Analysis

1 (8 AM–10 AM)

We see a gradual reduction in the power consumption during the class. This is due to the increasing ambient sunlight coming in from the windows as the sun rises. The control system compensates for this by reducing the luminaire light output intensity and by extension, the power consumption of the luminaire. Circle 1 shows when the class ended, leading to zero occupancy. Therefore the control system turns off the luminaire. We notice that the power consumption is near constant. The sun was almost fully risen at this time, which means that the ambient light level is now near constant. The luminaire remains switched on between region 2 and region 3 because the class in region 2 ended late. This is shown by Circle 2. Therefore, the occupancy data remains constantly high. Circle 3 shows the time when the class finally ended, and the occupancy data goes to low. There were no classes during this region as evidenced by the zero power consumption. The power consumption is high during the start and tail end of this region as students were late to leave. During this time, the natural sunlight received by the classroom is the highest since the sun is shining at its brightest; hence the power consumption is the lowest in this region as compared to the previous two regions. The power consumption is also now near constant. Circle 4 shows when the lights were still switched off because the class at this time started late. During these regions, no classes were held. However, the classroom was used by students to study. The usage pattern can be seen to be more sporadic as students come and go. The ambient brightness in the room can be seen to be almost constant.

2 (10 AM–11 AM)

3 (11 AM–11.27 AM)

4 (11.27 AM–12 PM)

5 (12 PM–1.30 PM)

6 (1.30 PM–3.30 PM)

8.8 W per hour or 61.6 W h over the 7-hour test period. This translates to a total of 62.86% energy savings. The results are recorded in Fig. 12. The power consumption of the luminaire can be divided into a few notable regions which are presented in Table 2. 5. Cost analysis The total setup cost of the implemented smart luminaire with an integrated controller and wireless module is calculated and presented in Table 3. The cost of the micro-controller, integrated

Table 3 Cost analysis of the developed energy-saving controller and a near-equivalent commercial system. Item

Unit cost (USD)

No.

Total cost (USD)

ATMEGA328-PU 1.84 micro-controller 2.03 TEMT6000 ambient light sensor 2.37 PIR sensor 0.03 10 ␮F capacitor 0.01 10 K resistor 220 resistor 0.01 1.00 16 MHz clock crystal 22 pF capacitor 0.03 LM3409 Buck 1.86 Converter Power MOSFET 0.13 0.08 1 ␮F capacitor 470 pF capacitor 0.05 Resistors 0.06 Inductor 0.35 0.37 Diode Terminal block 0.11 CREE XT-E LED 0.82 ZigBee Module 3.19 PCB cost 2.37 Proposed energy-saving system cost (USD)

1

1.84

1

2.03

1 2 1 2 1 2 1

2.37 0.03 0.01 0.02 1.00 0.06 1.86

1 3 1 4 1 1 2 6 2 2

0.13 0.24 0.05 0.24 0.35 0.37 0.22 4.91 6.38 4.74 26.85

18.99 Philips dimmable 19 W LED bulb [34] 20.22 Motion sensing light socket [35] Near-equivalent commercial system cost (USD)

1

18.99

1

20.22 39.21

sensors and the wireless module is 48.83% of the total system cost. For comparison, the cost of a near-equivalent commercial system is also presented. The commercial setup is manually dimmable and has an integrated PIR sensor. However, it does not take advantage of the additional daylight harvesting technique proposed in this paper and does not include a wireless sensor module. Our total system cost was calculated to be 31.52% cheaper than the commercial system. Although packaging, improved optics and commercialization can add to the final total system cost, it is reasonable to assume that the total price of our system is still comparable and which has superior energy-saving capabilities, can be further decreased as smart lighting becomes more prevalent in the global lighting market. To further analyze the cost-saving potential of our designed system, we conducted a cost analysis on campus in a total of 28 classrooms of various sizes for a period of 3 months (1 semester), summarized in Table 4. Based on the number of hours that the

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Table 4 Cost analysis for 28 classrooms of varying sizes. Type

Rooms

Occupied hours/week

Required luminaires

Luminaire unit cost (USD)

Setup cost/room (USD)

Total setup cost (USD)

Small Medium Large

13 9 6

25.5 28.3 27.3

6 9 12

26.85 26.85 26.85

161.1 241.65 322.2

2094.3 2174.85 1933.2

Total setup cost for all classrooms (USD)

6202.35

Fig. 13. Cost effectiveness analysis of the developed energy-saving controller.

classrooms were utilized and the results obtained in Section 4, the total electricity cost with the energy-saving control system implemented was estimated and compared with the current total electricity cost, as shown in Fig. 13(a). From the data presented in Fig. 13 (a), we notice an estimated USD 1280.10 savings for a 3 month period, or a total savings of USD 426.7 a month. The cost of setting up the new energy-saving luminaires to produce comparable lighting performance in all 28 classrooms was calculated to be USD 6202.35 (Table 4). Based on this sum, the time for the cumulative cost savings to exceed the initial setup cost was calculated to be 14.54 months, as illustrated in Fig. 13 (b). 6. Conclusion In this paper, we developed an energy-saving controller for a smart LED lighting system utilizing various sensors to provide closed-loop feedback. Occupancy sensing and daylight harvesting techniques are used to achieve high energy savings under different usage patterns, which are verified with real world tests. The framework of the smart lighting system along with the controller optimization process was also described. Experimentally, the implemented control system managed to achieve 55% and 62% energy savings in a continuous usage pattern environment and a discrete usage pattern environment respectively under real world test conditions. The results show increased energy-savings compared to similar systems, especially for a continuous usage pattern environment. Based on a cost-analysis, the designed energy-saving control system is almost 32% more cost-effective compared to a near-equivalent commercial setup, whilst introducing additional autonomous daylight-linked control. The scope of this paper was limited to the design of an energysaving controller and validation of its performance via real-world experimental results. Further technical details regarding the design of the smart lighting platform and the implementation of other smart algorithms will be discussed in future publications. Possible improvements include integrating additional energy-saving techniques and implementing algorithms to take user preferences into account.

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