Lighting Control of a Resisdential Building Using Hybrid Occypancy Sensor

International Journal of Modern Research in Engineering & Management (IJMREM)
||Volume|| 1||Issue|| 4 ||Pages|| 08-16 ||April 2018|| ISSN: 2581-4540
www.ijmrem.com IJMREM Page 8
Lighting Control of a Resisdential Building Using Hybrid
Occypancy Sensor
1,
Mutiullah Memon, 2,
Dr. Faheemullah Shaikh, 3,
Dr. Pervez Hameed Shaikh
1,
Student M.E (Electrical Power) IICT, Mehran UET, Jamshoro;
2, 3,
Assistant Prof. Electrical Engineering, Mehran UET, Jamshoro.
---------------------------------------------------------ABSTRACT-------------------------------------------------
The growing development and urbanization has made the alterations in the human life style and comfort
requirement particularly in the offices and residential buildings. About 40-45% of world’s energy consumption is
being consumed by these buildings and from this portion 20-25% is the share of the lighting loads only. So, the
challenge of reduction of energy consumption by the lighting loads is yet to be addressed. Besides, human
occupancy pattern and behavior also add to the power consumption. Human comfort and technological
advancement is significant but the primary aim of the buildings is just to provide safety and comfort to the
occupants. Several lighting control strategies have been employed up to now but among them occupancy detection
control techniques are much sophisticated and sensitive in nature. Passive InfraRed (PIR) and Ultra Sonic (US)
sensors are widely used for the purpose of occupancy detection because of their fast and robust response. These
sensors provide the optimal use of lightings in multi-occupant buildings. This paper is of unique nature because
both the sensors PIR and US are used in hybrid arrangement in order to utilize the characteristics of both just to
enhance the performance of occupancy detection.
KEYWORDS: Lighting Control, Hybrid Occupancy Sensor, Energy Savings
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Date of Submission: Date, 14 April 2018 Date of Accepted: 19 April 2018
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I. INTRODUCTION
Energy Efficiency is becoming the major interest of research in the field of electrical engineering at present. This
growing interest in the particular field lead to recognize more effective and smarter means of energy consumption
keeping the global environment under consideration. This reduced use of energy means less energy bills, load
reduction and above all reduces the environmental stresses. Research shows one third of the world’s energy
consumption is shared by the buildings [1]. And among this lighting consumes almost 45% of the total energy
consumption and this consumption can be reduced up to 60% by employing some lighting control strategy [2].
So, there is strong need to reduce the consumption of lighting load which in turn reduces the overall energy
consumption which is the key factor for engineers at present time. Several methods were used to reduce this factor
of energy that includes reduction of rating of lighting loads, reducing the power consumed by such loads,
controlling the switching time of such lighting loads based on different approaches. Besides the energy reduction,
human comfort has also got the high importance [3] because reducing energy consumption by disturbing the
human comfort is not desired.
Studies have shown that with the installation of appropriate lighting control techniques sufficient amount of energy
can be conserved [4]. Nagy et al used the technique based on occupant centered lighting control and found that
almost 13.4% reduction in energy without affecting human comfort [5]. Guo et al worked on the most commonly
used sensors i-e PIR and US. After analyzing two different places it was concluded that 19% of the energy was
conserved in an office building while 11% was conserved in school building [6]. Manzoor et al also worked on
PIR along with RFID sensor to control the usage of lighting loads as well as other HVAC loads. They found
almost 13% of power was reduced in a public-sector building and there are further more chances of reduction [7].
Haq et al described that different lighting control strategies or topologies can provide sufficient energy savings
that would lead to a reduction in energy demand and would also positively impact on the environment [8]. Yun et
al worked on automatic dimming control for lighting and occupancy pattern in order to find out the energy savings.
They concluded that almost 43% of lighting consumption can be reduced in offices and by the change of
occupancy pattern, the use of lighting loads can be increased by 50%. [9]. Wahl et al worked on PIR based
approach to calculate the occupancy pattern and then calculated the energy consumed in an office building. PIR
false triggering has been compensated using distributed sensor information [10].

Lighting Control of a Residential Building…
Thus, to encourage conservation of energy on artificial lighting use, besides the energy efficient luminaries such
as LEDs [11], various control strategies have been designed and proposed. Of which, the technological
developments in the arena of information and communication technology, occupancy detection systems are
sophisticated and sensitive in nature. In addition, Passive Infrared (PIR) sensors are the dominant detection
systems used in buildings for occupancy detection. Moreover, ultrasonic sensors offer additional support for the
modern occupancy detection which is capable of providing information on occupant activity, location, number
and information related user presence [12]. These developments provide optimal lighting use in multi-occupant
buildings. Research shows that there is still potential of work in the field of occupancy sensors in a way that the
sensors commonly used i-e PIR and US suffered from some drawbacks of “False off” and “False on” [5,8].
Because of these problems occupants complained about the automated system that within their presence, the
lighting loads were switched off and vice versa. These drawbacks created the motivation to work with the same
sensors (being simple in operation). Eventually such issues need to be solved in order to provide at most comfort
to the occupants. The paper is organized as follows. Section II explains the background Hybrid Occupancy Sensor
including the circuit arrangement and operational flow chart. Section II details the case study and hardware
implementation of the proposed model. Section IV deals with the results. Finally, the findings and conclusions
will be discussed in Section V.
II. HYBRID OCCUPANCY SENSOR
The word Hybrid means the combination of two things. In this case Hybrid also gives a sense of cascade
arrangement. The two conventional occupancy sensors i-e Passive Infra-Red (PIR) and Ultra Sonic (US) are
connected in hybrid arrangement in such a way that PIR sensor works as primary occupancy detector giving signal
to the US sensor that ultimately triggers the Relay. There are two hybrid circuits containing the PIR and US
sensors. Circuit 1 is used to switch ON the lights while circuit 2 is used to switch OFF the lights. These sensors
of the two circuits are interfaced with each other through Micro controller using Arduino Programming Software.
The circuit arrangement for the hybrid occupancy sensor is shown in Fig. 1.
OCCUPANT
PIRSensor
Ultra Sonic (US)
Sensor
CONTROL ACTION LIGHTS ON/OFF
TIME
RECORDER
OCCUPANCY
COUNTER
Fig. 1 Hybrid Occupancy Sensor
The operational flow chart for the proposed model is shown in Fig. 2. As shown in flow chart, the switching of
lighting loads will only be performed only if both the sensors are activated. The case, if any of the sensors is not
triggered or activated, the relay will be in the same position. In this manner the drawback of both sensors of False
off and False on can be removed.

START
OCCUPANCY
PIR 1
ULTRA SONIC
1
MOTION
DETECTION
COUNT
NUMBER
(1)
END
HIGH SIGNAL
TO REALY
SWITCH ON
THE LIGHTS
PIR 2
ULTRA SONIC
2
MOTION
DETECTION
LOW SIGNAL
TO REALY
COUNT
NUMBER
(2)
COUNT NUMBER (2)=
COUNT NUMBER (1)
SWITCH OFF
THE LIGHTS
HIGH
LOW
YES
NO
HIGH
YES
NO
Fig. 2 Operational Flow chart of Hybrid Occupancy Sensor
III. CASE STUDY
A residential building with three rooms was taken under consideration in order to implement the proposed circuit.
This was to find out the occupancy patterns in all the rooms and finally the energy savings which is the ultimate
purpose of this work. The occupancy was under observations from 08:00 AM to 12:00 AM for 16 Hours. After
this time period the lights were manually turned off. The Plan view of the residential house under consideration
is shown in Fig. 3. The details of the rooms along with the occupancy is drafted in Table 1. The 3D view of a
single room along with the lighting loads arrangement is shown in Fig. 4. The arrangement for the lighting loads
for the rest of the rooms is same as that of Fig. 4
Fig. 3 Plan view of a residential house

Fig. 4 3D view of a room showing position of lighting loads
Table 1 Details of house
Location Area
(ft2
)
Load
(W)
No: of
occupants
Occupant
Type
Room 1 143 64 2 House
Members
Members
Members
The lighting load for one single room of a house is 64W. Thus, the overall lighting load of a house (3 rooms) will
be 192W (approx.). Fig. 5 shows the hardware model of the hybrid occupancy sensor for plug and play operation
and data collection.
Fig. 5 Hardware arrangement of hybrid occupancy sensor

Fig. 6 and Fig. 7 shows the circuits connected at the exit and entrance of Room 1 respectively.
Fig. 6 Circuit connected at exit of room
Fig. 7 Circuit connected at entrance of room
IV. RESULTS AND DISCUSSIONS
When the Hybrid occupancy sensor circuits were installed at the entrance and exit locations of Room 1, Room 2
and Room 3 individually, the occupancy behavior was observed. The occupancy behavior for the room 1 is shown
in Fig. 8. In Fig. 8, the shaded portion shows the time during which the occupancy is present in the room while
the unshaded portion indicates the unoccupied room. The detailed occupancy pattern is tabulated in Table 2.
In the same way, Fig. 9 and Fig. 10 shows the occupancy behavior in Room 2 and Room 3 respectively. The
detailed occupancy pattern for Room 2 and Room 3 is also tabulated in Table 2.

Table 2. Details of occupancy (in mins) of Room 1, Room2 & Room3
Fig. 8 Average Occupancy Pattern in Room
0
0.5
1
OCCUPANCY(NO)
TIME (HOURS)
AVERAGE OCCUPANCY
PATTERN IN ROOM 1
Room 1 Room 2 Room 3Period
Occupancy
Yes(Mins)
Occupancy
No(Mins)
Occupancy
Yes(Mins)
Occupancy
No(Mins)
Occupancy
Yes(Mins)
Occupancy
No(Mins)
08:00 to 09:00 29 31 25 35 17 43
09:00 to 10:00 32 28 11 49 25 35
10:00 to 11:00 37 23 31 29 28 32
11:00 to 12:00 33 27 16 44 32 28
12:00 to 13:00 21 39 28 32 20 40
13:00 to 14:00 28 32 25 35 00 60
14:00 to 15:00 45 15 6 54 16 44
15:00 to 16:00 31 29 7 53 25 35
16:00 to 17:00 60 00 7 53 34 26
17:00 to 18:00 21 39 26 34 00 60
18:00 to 19:00 18 42 36 24 47 13
19:00 to 20:00 18 42 00 60 19 41
20:00 to 21:00 29 31 25 35 19 41
21:00 to 22:00 00 60 00 60 16 44
22:00 to 23:00 48 12 18 42 24 36
23:00 to 00:00 17 43 39 21 24 36
Total (Mins) 467 493 300 660 346 614

Fig. 9 Average Occupancy Pattern in Room 2
Fig. 10 Average Occupancy Pattern in Room 3
From the data tabulated in Table 2, the energy consumption can be calculated with and without hybrid occupancy
sensor by taking load of 24W LED bulb only. The energy consumed by each room with and without hybrid
occupancy sensor is tabulated in Table 3 and Fig. 11 shows the graphical representation.
Table 3. Comparative Energy Consumption with and without hybrid occupancy sensor
Room
Without Occupancy
Sensor (kWh/day)
With Occupancy
Sensor (kWh/day)
Difference
((kWh/day)
Change (%)
1 0.384 0.186 0.198 51.5
2 0.384 0.12 0.264 68.8
3 0.384 0.144 0.24 62.5
Total 1.152 0.45 0.702 61
0
0.5
1
OCCUPANCY(NO)
TIME (HOURS)
AVERAGE OCCUPANCY
PATTERN IN ROOM 2
0
0.5
1
OCCUPANCY(NO)
TIME (HOURS)
AVERAGE OCCUPANCY
PATTERN IN ROOM 3

Fig. 11 Comparative Energy Consumption by the lighting loads in Room 1, Room2& Room 3 and Total
Energy Consumption with and without occupancy Sensor
V. CONCLUSIONS & FUTURE WORK
The paper presented a case study on the Hybrid Occupancy Sensor installed in 3 Rooms of a residential house
with a variable number of occupants. The control action was set based on the entrance and exit of the occupant
irrespective of fixed time delay or preset time.
From the results, we found that both energy consumption and demand can be minimized by introducing hybrid
occupancy sensor without having any impact on human comfort. Furthermore, the hindrances in the application
of these sensors, false off and false on, have been improvised by using two different circuits for the ON and OFF
operation. Other issues, such as the lights being turned OFF in the presence of the occupant in the work place, are
also resolved.
Future work can also be made so as to increase the sensitivity of the sensors because these sensors are quite
sensitive during their operation. Different types of other sensors can also be installed along with PIR and US so
as to increase the efficiency of the system. Moreover, the same system may also be used to control the other types
of loads like HVAC at residential buildings, at office premises and other public buildings such as shopping malls,
apartments, etc. It can be made wireless with the help of advance data acquisition technology like Bluetooth and
Wi-Fi. This will avoid the chances of false operation where possible. Along with hardware models, simulation of
such systems can be done in order to decrease the chances of false operation. Along with this, Solar Panels can
also be incorporated just to supply power to whole lighting system of a building and sensors system.
REFERENCES
[1] Nagy, Z., Yong, F. Y., & Schlueter, A. (2016). Occupant centered lighting control: a user study on
balancing comfort, acceptance, and energy consumption. Energy and Buildings, 126, 310-322.
[2] de Bakker, C., Aries, M., Kort, H., & Rosemann, A. (2017). Occupancy-based lighting control in open-
plan office spaces: A state-of-the-art review. Building and Environment, 112, 308-321..
[3] Meerbeek, B., van Druenen, T., Arts, M., van Loenen, E., & Aarts, E. (2014, November). Impact of
blinds usage on energy consumption: automatic versus manual control. In European Conference on
Ambient Intelligence (pp. 158-173). Springer, Cham..
[4] Pandharipande, A., & Caicedo, D. (2015). Smart indoor lighting systems with luminaire-based sensing:
A review of lighting control approaches. Energy and Buildings, 104, 369-377..
[5] Nagy, Z., Yong, F. Y., Frei, M., & Schlueter, A. (2015). Occupant centered lighting control for comfort
and energy efficient building operation. Energy and Buildings, 94, 100-108..
[6] Guo, X., Tiller, D. K., Henze, G. P., & Waters, C. E. (2010). The performance of occupancy-based
lighting control systems: A review. Lighting Research & Technology, 42(4), 415-431.
[7] Manzoor, F., Linton, D., & Loughlin, M. (2012, September). Occupancy monitoring using passive RFID
technology for efficient building lighting control. In RFID Technology (EURASIP RFID), 2012 Fourth
International EURASIP Workshop On (pp. 83-88). IEEE..
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Room 1 Room 2 Room 3 Total
EnergyConsumption(kWh/day)
COMPARATIVE AVERAGE
ENERGY CONSUMPTION
Without Occupancy Sensor With Occupancy Sensor

[8] ul Haq, M. A., Hassan, M. Y., Abdullah, H., Rahman, H. A., Abdullah, M. P., Hussin, F., & Said, D. M.
(2014). A review on lighting control technologies in commercial buildings, their performance and
affecting factors. Renewable and Sustainable Energy Reviews, 33, 268-279.
[9] Yun, G. Y., Kim, H., & Kim, J. T. (2012). Effects of occupancy and lighting use patterns on lighting
energy consumption. Energy and Buildings, 46, 152-158.
[10] Wahl, F., Milenkovic, M., & Amft, O. (2012, December). A distributed PIR-based approach for
estimating people count in office environments. In Computational Science and Engineering (CSE), 2012
IEEE 15th International Conference on (pp. 640-647). IEEE.
[11] Magno, M., Polonelli, T., Benini, L., & Popovici, E. (2015). A low cost, highly scalable wireless sensor
network solution to achieve smart LED light control for green buildings. IEEE Sensors Journal, 15(5),
2963-2973..
[12] Labeodan, T., Zeiler, W., Boxem, G., & Zhao, Y. (2015). Occupancy measurement in commercial office
buildings for demand-driven control applications—A survey and detection system evaluation. Energy
and Buildings, 93, 303-314.

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