Functions of fuzzy logic based controllers used in smart building

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 3, June 2022, pp. 3061~3071
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i3.pp3061-3071  3061
Journal homepage: http://ijece.iaescore.com
Functions of fuzzy logic based controllers used in smart
building
Ali M. Baniyounes1
, Yazeed Yasin Ghadi2
, Eyad Radwan1
, Khalid S. Al-Olimat3
1
Department of Electrical and Computer Engineering, Applied Science Private University, Amman, Jordan
2
Department of Software Engineering and Computer Science Programs, Al Ain University, Abu Dhabi, United Arab Emirates
3
Department of Electrical and Computer Engineering and Computer Science, Ohio Northern University, Ohio, Unites States America
Article Info ABSTRACT
Article history:
Received Jun 13, 2021
Revised Oct 17, 2021
Accepted Nov 5, 2021
The main aim of this study is to support design and development processes of
advanced fuzzy-logic-based controller for smart buildings e.g., heating,
ventilation and air conditioning, heating, ventilation and air conditioning
(HVAC) and indoor lighting control systems. Moreover, the proposed
methodology can be used to assess systems energy and environmental
performances, also compare energy usages of fuzzy control systems with the
performances of conventional on/off and proportional integral derivative
controller (PID). The main objective and purpose of using fuzzy-logic-based
model and control is to precisely control indoor thermal comfort e.g.,
temperature, humidity, air quality, air velocity, thermal comfort, and energy
balance. Moreover, this article present and highlight mathematical models of
indoor temperature and humidity transfer matrix, uncertainties of users’
comfort preference set-points and a fuzzy algorithm.
Keywords:
Energy fifth keyword
Fuzzy logic
Indoor thermal comfort
PID controller
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ali M. Baniyounes
Department of Electrical and Computer Engineering, Applied Science Private University
Al Arab St 21, Amman, Jordan
Email: al_younes@asu.edu.jo
1. INTRODUCTION
The integration of real-time events with indoor air temperature, humidity, the level of carbon dioxide
(CO2) and indoor illuminance level are not a straightforward task using proportional integral derivative (PID)
controllers. Thus, a multi-agent control system, are designed and reconfigured in this paper rather than using
one multi controllers of each separate control signal. In addition, it is quite difficult to apply and design model
free controller to real-time indoor environment control system based on surrounding real life event and real
time thus modifying adaptability is included in this study in order to increase controller feasibility.
Consequently, suitable mathematical models of controlled goals are essential throughout the design and testing
phase, the proposed control strategies. Accordingly, this chapter discusses the mathematical models of the
building’s indoor atmosphere including the model of indoor temperature, humidity, the concentration of CO2
level and the indoor illuminance level. The chapter also discusses the mathematical models of lighting and
heating, ventilation and air conditioning (HVAC) system and systems’ energy usage and savings. The recent
control methods that extensively employed in indoor thermal comfort control mathematical models. The
current research, a fuzzy-logic-based controller is designed and modelled in order to control indoor
temperature, indoor humidity, indoor air quality and indoor illuminances by integrating real life events such as
the usage of ambient air and natural light (day-lighting) into the existing artificial indoor environment while
using minimum energy.

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Int J Elec & Comp Eng, Vol. 12, No. 3, June 2022: 3061-3071
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The mathematical models have been investigated heavily throughout literature such as [1] have
modelled and experimentally investigate indoor thermal comfort. Also, same authors have highlighted indoor
comfort parameters and its effect on indoor air quality [2]. Lately [2]–[6] have introduced necessary parameters
in considering indoor thermal comfort in fuzzy logic controllers. However, the most of those research activities
lack of accurate mathematical models. Accordingly, this article presents and highlight mathematical models of
indoor temperature and humidity transfer matrix, uncertainties of users’ comfort preference set-points and a
fuzzy algorithm and the integration of daylight with existing artificial light.
2. INDOOR ENVIRONMENT MATHEMATICAL MODEL
Here, the controller’s modelling representing indoor climate level containing building’s inner space
temperature, building’s indoor humidity, building’s indoor air quality (IAQ) and comfort index is presented.
The proposed control system which is based on add on multi-agent fuzzy based control system into existing
PID controller algorithms are detailed and presented in the following sub-sections.
2.1. Modelling of space indoor temperature
Inner spaces’ temperature of a building is a very important factor in indoor environment monitoring.
The models of in space air temperature is able to predict the daily change of indoor temperature with time.
According to [7], the in-space temperature is influenced by the atmosphere temperatures in inside climatic
conditions. The modelling process of inner air temperature is built using the regression analysis method by
evaluating conduction, radiant and convection heat transfer. Accordingly, indoor temperature can be evaluated
based on (1) taking into account energy conservation principle:
𝜌𝑎. 𝐶𝑃. 𝑉𝑖
𝑑𝑡
𝑑𝜏
= 𝑄ℎ1 − 𝑈𝑤𝐴𝑤(𝑇𝑖 − 𝑇𝑜) (1)
where ρa is air density in (kg / m3
), Cp represents heat capacity of air in (J/kg∙C°), Vi is room volume in (m3
), Ti is
inner doors temperature in C°, τ is time in (s), Qh1 is HVAC work rate, To is outdoor temperature in C°, Aw (m2
)
is the area of walls, Uw is overall heat transfer coefficient in (𝑊/𝑚2
∙C°). Uw can be calculated using (2).
𝑈𝑤 =
1
1
ℎ𝑎
+
𝑑𝑤
𝑘𝑏
+
1
ℎ𝐵
(2)
Here Kb represents the walls heat conductivity, units is (𝑊/𝑚 ∙ k), dw is walls thickness in (m), hA and hB are
convection heat transfer coefficients on the side of each wall in (𝑊/𝑚2
∙ k). Hence Laplace transform is used to
transform (3) into (3) and then into (6) taking into account the convection heat transfer coefficient is constant [8].
𝑇(𝑠). 𝑆 =
1
𝜌𝑎.𝐶𝑝.𝑣𝑖
𝑄ℎ1(𝑆) (3)
𝑇(𝑠). 𝑆 =
1
𝑄ℎ1(𝑆) +
𝑈𝑤𝐴𝑤
𝑇0(𝑆) (4)
𝑈𝑤𝐴𝑤
𝑇0(𝑆) = 0 (5)
Then (4) can be written as (6):
𝑇𝑖(𝑆)
𝑄ℎ1(𝑆)
=
𝐾𝑖𝑡
𝑇𝑖𝑡(𝑆)+1
(6)
Here 𝑇𝑖𝑡 is the time constant and heat gain is a DC gain of the transfer function is 𝐾𝑎.
2.2. Modelling of inner space humidity
The mathematical model of indoor humidity can be obtained in a similar way to the of indoor
temperature modelling. The humidity of a conditioned space room is considered to be equal to the mean
humidity that are provided by sensors. The humidity of a space depends on the humidity of the provided air
from the inlet air and HVAC ducts, room temperature, and occupant’s number. Using energy conservation
principles, indoor air humidity can be expressed using (7) [9]:

Int J Elec & Comp Eng ISSN: 2088-8708 
Functions of fuzzy logic based controllers used in smart building (Ali M. Baniyounes)
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𝜌𝑎. 𝑉𝑖𝐻.
𝑑ℎ𝑖
𝑑𝜏
= 𝑄ℎ2 − 𝑓𝐻𝜌𝑎(ℎ𝑖 − ℎ𝑜) (7)
where vapor enthalpy of vapor in H ( kJ/ kg), f is the flow rate volume in (m3/s), hi is the inner space humidity
in (g /kg), ℎ𝑜 is the outdoor humidity in (g/kg) and Qh2; work rates in (W) and dτ is time constant.
The (8) is resulted using Laplace transform in order to solve the previous differential (7).
𝐻𝑖(𝑆). 𝑆 =
1
𝜌𝑎.𝑉𝑖.𝐻
𝑄ℎ2(𝑆) −
𝑓
𝑉𝑖
(𝐻𝑖(𝑆) − 𝐻𝑜(𝑆)) (8)
Here, (9) is resulted from rearranging (8).
(𝑆 +
𝑓
𝑉𝑖
) 𝐻𝑖(𝑆) =
1
𝑄ℎ2(𝑆) +
𝑓
𝑉𝑖
(𝐻𝑜(𝑆) (9)
Taking into account that
𝑓
𝑉𝑖
(𝐻𝑜(𝑆) = 0 as the change in indoor enthalpy is neglected due to its’ little effect
and, then the transfer function is given in (10):
𝐻𝑖(𝑆)
𝑄ℎ2(𝑆)
=
𝐾
𝑇𝑖ℎ.(𝑆+1)
(10)
where 𝑇𝑖ℎ=
𝑉𝑖.
𝑓
is the time-constant and 𝑘𝑖ℎ=
1
is the heat gain transfers function.
2.3. Modelling of indoor thermal comfort conditions
The modelling of indoor thermal comfort conditions and environment is defined by ASHRAE 55 and
ISO 7730. In addition, comfort in an indoor environment, indices also the way of measurement methods are
presented here. This section also shows most of parameters that affects local thermal comfort level such as air
speed, thermal radiant gain, and vertical temperature difference as shown in Figure 1. There are a few research
activities available concerning the mathematical model of thermal comfort [10], [11]. These authors have
investigated the implementation of mathematical models in the control process of smart buildings and thus
improving the thermal prediction level of pivot mean value (PMV) model in buildings.
Figure 1. Indoor standards model
2.4. Mathematical model of thermal comfort
Air speed is linked to air sensible heat gain caused by convection heat transfer and latent heat gain
caused by evaporation and, thus, the sensation of thermal comfort is affected by air current. For example,
occupants expose a more indoor air temperature the moment indoor air velocity is decreased. Accordingly,
throughout hot season, ventilation is increased to cool the conditioned space rather than running the cooling
system which means the cooling energy consumption is decreased. Throughout cold season, a higher air
velocity is required for operating heating system which means heating energy consumption is higher. In order
to control air velocity influence, air velocity limitation is applied as indoor air velocity ranging between
0.6-0.90 m/s in hot season and under 0.150 m/s throughout the cold season taking into account temperature
fluctuation. Simultaneously, in two different situations, occupants encounter the same heat loss and thus the
feeling of higher temperature unsatisfactory where air temperature encounters high varying in respect to time,
which is known as air turbulence [10], [11]. The pressure in duct percentage PD% can be obtained based

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on (11) which defines the draft risk (DR) which expresses the percent of people dissatisfied due to draught.
The implied set point temperature is in the range of 22–26 °C, Air speed velocity varies between 0.050 m/s
and 0.40 m/s, and winds concentration is ranged from 0% to 70% [12].
𝐷𝑅 = (34 − 𝑡)(𝑣 − 0.05)0.62(. 37𝑣. 𝑇𝑈 + 3.14) (11)
3. THERMAL RADIATION AND VERTICAL TEMPERATURE DIFFERENCES MODEL
Thermal radiation is defined as the heat energy gained by the mean of emission of electromagnetic
waves. Asymmetric thermal radiation occurs when residents of an inner space environment are indorsed in to
a heat source on one part of their body for substantial time, leading to a certain level of un-satisfactions or
discomfort. The effect of current thermal radiation is obtained by two techniques.
Measuring in 2 opposite directions and utilizing transducer to record radiation that affects a tinny
point from the matching hemisphere and measuring surroundings surface temperature in indoor environments
and evaluate the increase of radiant temperature. Consequently, (12) shows the percent of PID caused by warm
ceiling [13].
𝑃𝐷 =
100
1+𝑒
(2.84−0.174.∆𝑡𝑝𝑟)
− 5.5 (12)
In addition, the percentage of PD caused by a cold ceiling is given by (13).
𝑃𝐷 =
100
1+𝑒
(9.93−0.5∆𝑡𝑝𝑟)
(13)
In a similar way, the percentage of PD caused by cold and hot walls are given in (14) and (15) respectively
[13]:
𝑃𝐷 =
100
1+𝑒
(6.61−0.345∆𝑡𝑝𝑟)
(14)
𝑃𝐷 =
100
1+𝑒(3.72−0.0525∆𝑡𝑝𝑟)
− 3.5 (15)
where Δtpr is the increment of radiant temperature in °C. The vertical temperature differences affect PD for
various increments of temperature, as shown in (16):
𝑃𝐷 =
100
1+𝑒(5.76−0.856∆𝑡𝑝𝑟)
(16)
were Δt is the vertical temperature difference in °C.
3.1. Thermal comfort index
American Society of Heating, Refrigeration and Air Conditioning Standards (55) have outlined the
influences of tinner space temperate and inner environment on residents’ satisfaction under hot and humid
atmosphere also under misty atmosphere based to the model of humans’ metabolism enhanced by that human’s
thermal behavior which is influenced by different environmental conditions and several sole conditions as there
is a definite difference between the PMV model and the thermal sensation, as presented by (metabolic rate and
clothing) [14]. The difference, compared to the neutral temperature (comfort level) is nearly 1.4 °C due to heat
sensing is acquired from occupants’ survey located in certain environment taking into account that, PMV
method utilize heat transfer as demonstrate in (17):
𝑃𝑀𝑉 = 𝑎𝑡 + 𝑏𝑝𝑣 − 𝑐 (17)
where a, b and c are coefficients representing occupants’ gender and their function of spent time as shown in
Table 1. Meanwhile the error in evaluating the body neutral temperature is related to a PMV issue in
determining the metabolic rate and the clothing factor (𝑐𝑙𝑜) which represents the insulation of the body [15].
Environments’ researches of Kansas’s State University helped developing a model of PMV that is
able to evaluate inner thermal comforting an institutional building in order to quantify the parameters of PMV
[15]. This research project developed different regressions and consequently, the thermal sensational value
Tsens which is presented in Table 2 as in (18):

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𝑇𝑠𝑒𝑛𝑠 = 0.305𝑇 + 0.99𝑐𝑙𝑜 − 8.08 (18)
where Tsens the sensational temperature in °C, T represents inner temperatures in °C also clo is clothing factor
in clo.
Table 1. Coefficients (a, b and c and gender of the resident)
Time s / Genders a b c
1 h/Males 0.2190 0.2330 5.670
Females 0.2719 0.24890 7.240
Both (male sand females) 0.2445 0.2490 6.470
2 h/Males 0.2220 0.2690 6.020
Females 0.2820 0.2990 7.690
Both (male and females) 0.2510 0.2390 6.850
3 h/Males 0.2130 0.2990 5.940
Females 0.27560 0.2560 8.6219
Table 2. Coefficient a-b-c and genders
Thermal sensation Thermal sensations
3.0 Warmth
2.0 Hot
1.0 Near hot
0.0 Neutralth
-1.0 Fresh
-2.0 A bit mist cold
-3.0 Coldth
It is worth mentioning that the previous models that were endorsed to assess overall thermal
comforting. Researches’ activities have shown that adaptive-model could be utilized to calculate sat thermal
conditions inside the space atmosphere based on functions of outer ambient climates. This model is able to
utilize where occupant are in resting mode or seated activity conditions also the Met level is ranging between
1–1.3 Met. The ASHRAE proposed model is based on windows that are the medium of controlling thermal
conditions as shown in (19):
𝑇𝑠 = 17.8 + 0.31𝑇0 (19)
here Tc is comfort temperature in °C, To is outer air temperature in °C and Ti is the mean inner air temperature
in °C. Percentage of dissatisfied persons with local thermal comfort (PDP) is given by (20):
𝑃𝐷 =
100
1+𝑒(−3.58+0.18(30−𝑡)+0.14(42.5−0.01𝑃))
(20)
here P is number of persons.
4. INNER TEMPERATURE’S AND HUMIDITY’S TRANSFER MATRIX
Generally indoor environment temperature variation and indoor humidity variation are connected, it is
important to evaluate the two variables jointly and thus establishing indoor temperature and humidity transfer
matrix. In this research, the system output control variables are inner temperatures humidities. The output
matrix is given by (21):
𝑌 = [𝑇𝑖 − 𝐻𝑖]𝑇
(21)
where Ti is the inner temperatures in °C and Hi is the inner humidities in g/ kg. Considering the cooling system
including humidifier supply power are the inputs while the output are the controlled temperatures and
humidities. Accordingly, the input matrix given by (22):
𝑌 = [𝑄ℎ𝑖 − 𝑄ℎ2]𝑇
(22)
here Qh1 is the cooling system power supply in kWh and Qh2 is the dehumidifier power supply in kWh. The
model of the two input parameters and the two output parameters is shown in Figure 2 where T-controller is
the temperature controller and H- controller is humidity controller.

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Figure 2. Inner temperatures and humidity’s single input, and output model
In the previous model, the model transfer-matrix which evaluates the relationship between output Y
and input X is given in (23):
[
𝑇𝑖𝑐
𝐻𝑖𝑐
] = [
𝐵11 𝐵12
𝐵22 𝐵22
] [
𝑄ℎ1
𝑄ℎ2
] (23)
where Tic is the indoor temperature controller output in °C, Hic is the indoor humidity controller output in g/kg,
Qh1 is work rate of heater in W, Qh2 is work rate of humidifier in W and B’s are the matrix transfer function.
Hence, the air supply flow rate is constant; the humidity is not affected by air cooling process power as it is
influenced by the dehumidifier’s process power. In short, the mathematical description of the previous
paragraph means that G22=0. Simplifying (26) will give (24).
[
𝑇𝑖𝑐
𝐻𝑖𝑐
] = [
𝐵11 𝐵12
0 𝐵22
] [
𝑢ℎ1
𝑢ℎ2
] (24)
4.1. Mathematical model of thermal comfort
In this thesis, the indoor temperature and humidity is controlled by integrating outdoor ambient air
into indoor once its temperature and humidity are suitable to use. In other words, if the outside air temperature
is ranging from 20–25 °C and its humidity is ranging between 40%–70%, the ambient outdoor air can be
pumped into the building, and, hence, less power is needed to power HVAC system. The indoor temperature
and humidity are considered to be difficult as both temperature and humidity are linked together and
consequently any indoor humidity changes, will endorse changes in the indoor temperature. Therefore, indoor
temperature and humidity single loop control system is not able to cover the optimum control necessity and
also it will produce poor control functioning. Accordingly, the design and implementation of decoupling
control strategy will remove the link between indoor temperature and indoor humidity. Later, once the indoor
temperature and humidity are not linked to each other, using single-loop controllers to control indoor
temperature and humidity control can be satisfactory. Figure 3 represent a two input and two output control
system where the input and output variables affect each other’s, in the figure Rt and Rh are the system inputs
(the outdoor air temperature and humidity). Xt and Xh are the controllers’ temperature control outputs and
humidity control outputs respectively while Yt and Yh are the system temperature and humidity output,
respectively. The input matrix is obtained from (25) as shown in (25):
𝑋𝑡,ℎ = [𝑄ℎ1 − 𝑄ℎ2]𝑇
(25)
where Qh1 is the cooling system power supply in kWh and Qh2 is the dehumidifier power supply in kWh.
Furthermore, the output matrix is given by (26):
𝑋𝑡,ℎ = [𝑇𝑖 − 𝐻𝑖]𝑇
(26)
where Ti is the indoor temperature in °C and Hi is the indoor humidity in g/kg and hence the matrix transfer
function can be given by (27) and (28): Adding the decoupling elements; E11, E12, E21 and E22 as shown in
Figure 4 to (30) will result a new transfer matrix as shown in (28):
Rt Ti

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[
𝑌𝑡
𝑌ℎ
] = [
𝐵11 𝐵12
𝐵21 𝐵22
] [
𝑋𝑡
𝑋ℎ
] (27)
[
𝑌𝑡
𝑌ℎ
] = [
𝐵11 𝐵12
𝐵21 𝐵22
] [
𝐸11 𝐸12
𝐸21 𝐸22
] [
𝑋𝑡
𝑋ℎ
] (28)
Figure 3. A two-input two-output control system
Figure 4. Two-input two-output control system with decoupling
Assuming that the decoupling elements; E11, E12, E21 and E22 are equal to the inverse of matrix transfer
function as in (29) and (30):
[
𝐸11 𝐸12
𝐸21 𝐸12
] = [
𝐵11 𝐵12
𝐵21 𝐵22
]
−1
(29)
[
𝑌𝑡
𝑌ℎ
] = [
𝐵11 0
0 𝐵22
] [
𝑋𝑡
𝑋ℎ
] (30)
Consequently, the two-input and two-output control system is represented by two single-input single-output
control system (SISO) as demonstrated in Figure 5.
As mentioned previously, temperature and humidity are given by (24). and can be noted that B21=0,
so the flow chart of the control system can be summarized in Figure 6 which contains two control loop:
temperature control loop and humidity control loop to ensure outdoor conditions are used by removing the
effect of B21. Consequently, the decoupling control element of the system E12 is configured as in (31) which
can be added to the previous control system as shown in Figure 7.
𝐸12(𝑆) =
𝐵12(𝑆)
𝐵11(𝑆)
(31)
Rt Yt
Xh

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It is may possible controlling inner temperature and inner humidifies utilizing 2 single loops controller that is
capable to add outer ambient atmosphere in treated inner environment [16]–[19].
Figure 5. SISO Equivalent control system
Figure 6. Temperature and humidity control system
Figure 7. Temperature and humidity control system with decoupling strategy
5. RESULT AND DISCUSSION
System’s simulations medium evaluated HVAC’s System also the illumination systems energy usage
knowing that residents’ occupancy changes. ADDON fuzzy PIDs’ controlling systems which readjusted so it
could carry on all calculations. The fuzzies controlling input is (eR) also errors variation (Δ eR) that highlights
variations of buildings former residents and building’s todays residents. Additionally, memberships formations
of ADDON (fuzzy PID) controlling inputs as well as outputs all show in Figure 8. The controlling input-output
uses the stated functions value: negative-high (NH), negative-medium (NM), negative low (NL), zero (Z),
positive low (PS), positive medium (PM) and positive high (PH) are demonstrated in Table 3. The output of
Yt
Xt
Xh Yh
T-controller
H-controller
B11
B22
Rt Ti
Rt Ti
T-controller
H-controller
T-controller
H-controller

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ADDON (fuzzy PID) controlling systems are basic energy variations depends on residents occupancy density
that endorses keeping inner thermal-visual comforting level. The research, BMS-fuzzy logic controlling
systems of institutional spaces under Australian Rockhampton campus of Central Queensland University have
been evaluated, designed, and built. The performance of the proposed control system was numerically
simulated and analyzed using MATLAB, DAYSIM and EnergyPlus software [20]–[22]. The study presented
a comprehensive understanding and assessment of control strategies of smart buildings application using
advanced fuzzy logic based control system.
The study investigated existing conventional control systems including control for indoor
temperature, humidity, air quality, HVAC system and the lighting system. Help designers to designed and
developed models for fuzzy-logic based controllers of the reference building. The mathematical model of
proposed fuzzy-logic-based control system was developed based on real-life events such as the utilizing
un-conditioned ambient fresh air, day-lighting, and the ability of performing head count for indoor environment
such as indoor temperature, humidity and thermal comfort [23]–[26]. In addition, the study will help in
developing methodologies and strategies to achieve potential energy savings in the reference building taking
into account of indoor environmental conditions. The existing and proposed control systems including
conventional PI controller, fuzzy logic controllers and add on multi-fuzzy-logic-based controllers were
simulated using energy plus, DAYSIM and MATLAB [27].
Figure 8. Membership functions of various occupancy levels
Table 3. Fuzzy control rules for local various occupancy levels
Energy changes eR
NH’s NM’s NL’s Z’s PL’s PM’s PL’s
(Δe R) NH’s NH’s NH’s PH’s PH’s PH’s PH’s PH’s
NM’s NH’s NM’s Z’s PM’s PM’s PH’s PH’s
NL’s NH’s NM’s NH’s PH’s PM’s PH’s PH’s
Z NH’s NM’s NH’s Z’s PL’s PM’s PH’s
PL NH’s NH’s NM’s NH’s PL’s PM’s PH’s
PM NH’s NH’s NM’s NM’s ZE’s PM’s PH’s
PL NH’s NH’s NH’s NH’s NH’s PH’s PH’s
6. CONCLUSION
Occupants spent the most of their time indoor, thus indoor environment has major effect on occupants’
health and productivity. The main factors of indoor climate aspects are indoor temperature, humidity, inner
illumines, and inner air quality. Nevertheless, almost inner climatic features are calibrated by utilizing regular
controlling systems that its functions depends on ON/OFF controlling system and PID controlling system. As
mentioned earlier, the conventional controlling system are not a suitable solution in saving energy as the
functions of HVACs’ systems and illuminations’ systems are non-linear. Therefore, the utilization of fuzzy
controlling systems in smart buildings will be energy efficient that is able in saving energy and also
accordingly, able greenhouse gas emission reduction and reducing its negative impact on the environment. The

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structure of the newly proposed fuzzy logic based controllers contains two main segments; a market available
conventional PID control which normally control indoor air temperature and an add-on fuzzy logic controller
which is able to integrate the newly controlled real life events such as outdoor air temperature, humidity, and
daylight usage to the PID controller based on current indoor situations.
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BIOGRAPHIES OF AUTHORS
Ali M. Baniyounes is an associate professor and the former chair of the Electrical
Engineering Department at Applied Science Private University, Amman-Jordan. He obtained
his BS in Electrical Engineering from Yarmouk University: Jordan in 1997, the MS in
Computer control Engineering from the University of Technology Sydney, Australia in 2004.
He joined CQ university, Australia in 2009. His areas of interest are power engineering,
adaptive, fuzzy and intelligent control, and engineering education. He can be contacted at
email: al_younes@asu.edu.jo.
Yazeed Ghadi received the Ph.D. degree from the Department of Biomedical
Engineering, Central Queensland University, Australia. He was working as a Postdoctoral
Research Fellow at CQU. He is currently an Assistant Professor with the Department of
Computer Science and Software Engineering, Al Ain University, UAE. His research interests
include multimedia contents and artificial intelligence, machine learning, computer vision,
fuzzy logic and renewable energy. He can be contacted at email: yazeed.ghadi@aau.ac.ae.
Eyad Radwan completed his BSc. from JUST in 1996 (Jordan), MSc. and PhD.
from UPM (Malaysia) in 1999 and 2004 respectively. From 1999 till 2011 he was with UCSI
University, Malaysia. During his term with UCSI University, Prof. Radwan served as the
Dean of the faculty of Engineering & built environment from 2007 to 2009, and as Chief
Operating Officer from 2009 to 2011. Prof. Radwan also served as member of the Malaysia
Engineering Accreditation Council from 2009-2011. He joined the dep. of Electrical
Engineering at the Applied Science Private University, Amman-Jordan in 2012. In addition
to his role as a faculty member, in 2021 Prof. Radwan has been appointed as the President’s
Assistant for International Relations. His specialization is in the area of Electrical power,
Drives and Control. He can be contacted at email: e_redwan@asu.edu.jo.
Khalid S. Al-Olimat is a professor and the former chair of the Electrical &
Computer Engineering and Computer Science Department at Ohio Northern University. He
obtained his BS in Electrical Engineering from Far Eastern University in 1990, the MS in
Manufacturing Engineering from Bradley University in 1994 and his PhD in Electrical
Engineering from the University of Toledo in 1999. He joined ONU in fall of 1999. He is a
recipient of Henry Horldt Outstanding Teacher Award twice in 2004 and 2014. He received
the Herbert F. Alter Chair of Engineering in 2005 and the University of Toledo Outstanding
Alumni Award in 2018. He is a senior member of IEEE and served as Treasurer, Secretary,
Vic-Chair, and Chair of IEEE-Lima section. He is serving the profession as a technical
reviewer for many IEEE transaction journals in the areas of power and energy systems. His
areas of interest are power engineering, adaptive, fuzzy and intelligent control, and
engineering education. Al-Olimat is an ABET Program Evaluator (PEV) and a registered
professional engineer in the State of Michigan. He can be contacted at email:
k-al-olimat@onu.edu.

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