Raad Z. Homod

1. Energy saving by integrated control of natural ventilation and HVAC
systems using model guide for comparison
Raad Z. Homod a
, Khairul Salleh Mohamed Sahari b, *
, Haider A.F. Almurib c
a
Department of Petroleum Engineering, University of Basrah, Basrah, Iraq
b
Department of Mechanical Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor Darul Ehsan, Malaysia
c
Department of Electrical & Electronic Engineering, The University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan,
Malaysia
a r t i c l e i n f o
Article history:
Received 30 August 2012
Accepted 10 June 2014
Available online
Keywords:
Building energy saving
Natural ventilation
Model-based guide
HVAC energy efficiency
Optimal thermal comfort
a b s t r a c t
Integrated control by controlling both natural ventilation and HVAC systems based on human thermal
comfort requirement can result in significant energy savings. The concept of this paper differs from
conventional methods of energy saving in HVAC systems by integrating the control of both these HVAC
systems and the available natural ventilation that is based on the temperature difference between the
indoor and the outdoor air. This difference affects the rate of change of indoor air enthalpy or indoor air
potential energy storage. However, this is not efficient enough as there are other factors affecting the rate
of change of indoor air enthalpy that should be considered to achieve maximum energy saving. One way
of improvement can be through the use of model guide for comparison (MGFC) that uses physical-
empirical hybrid modelling to predict the rate of change of indoor air potential energy storage consid-
ering building fabric and its fixture. Three methods (normal, conventional and proposed) are tested on an
identical residential building model using predicted mean vote (PMV) sensor as a criterion test for
thermal comfort standard. The results indicate that the proposed method achieved significant energy
savings compared with the other methods while still achieving thermal comfort.
© 2014 Elsevier Ltd. All rights reserved.
1. Introduction
Currently, the total worldwide energy accounts for about one
third of use by the building sector and a large amount of this
consumption is directly related to HVAC systems [1,2]. The building
sector therefore bares a great responsibility for matching between
demand and energy supply and for improving the quality of that
match [3]. Improving energy efficiency requirements for designing,
constructing and retrofitting of new buildings will have to follow
schemes of certifications that encompass many methods that
makes it possible to determine, compare and rate new buildings or
existing ones in terms of their energy quality [4,5]. In other words,
energy saving in the building sector can be achieved by two ways;
namely by improving the efficiency of various devices such as
lighting and HVAC systems, and/or by reducing the building loads
using natural ventilations and shadings. Nevertheless, energy
saving contributions due to optimal controls of shadings is insig-
nificant compared with natural ventilations [6].
Natural ventilation in buildings functions well in mild summer
areas, such as Europe [7]. In more severe climates, such as warm
and humid regions excluding some coastal areas, research has
shown that purely natural ventilation systems are not sufficient to
sustain acceptable thermal comfort [8,9]. However, these systems
have recently gained some momentum as a suitable auxiliary
control sources to existing HVAC systems. This integration of nat-
ural ventilation into the HVAC systems operation of indoor thermal
control achieved reduction in energy consumptions in warmer
climates, such as humid sub-tropical [10], tropical [11], and arid
[12]. Therefore, integrating these ventilation systems can lead to
improved cooling systems in terms of efficiency and supply. This
can be seen from the studies on the use of hybrid ventilations that
resulted in considerable energy saving [9]. The quantity of energy
saving depends on three related parameters; climatic, building and
technical parameters [13,14]. The parameters that can be mostly
manipulated are the technical parameters that depend on the hy-
potheses of the study.
In response to this need, there are many examples of such
studies that utilize natural passive ventilation to significantly
* Corresponding author. Tel.: þ60 3 89212020.
E-mail addresses: raadahmood@yahoo.com (R.Z. Homod), khairuls@uniten.edu.
my (K.S.M. Sahari), haider.abbas@nottingham.edu.my (H.A.F. Almurib).
Contents lists available at ScienceDirect
Renewable Energy
journal homepage: www.elsevier.com/locate/renene
http://dx.doi.org/10.1016/j.renene.2014.06.015
0960-1481/© 2014 Elsevier Ltd. All rights reserved.
Renewable Energy 71 (2014) 639e650

2. reduce air conditioning used energy in buildings [13e16]. The
results of these studies are indicating the suitability of natural
ventilation systems for saving energy. However, these studies
usually do not consider all the pertinent outdoor and indoor fac-
tors affecting the indoor thermal comfort. Some of them base their
concept on seasonal winds and building fabric [15], whereas
others concentrated on opening windows only when the outside
temperature is lower in summer [6,16]. But Sakulpipatsin et al. [3]
found out in their work that the total thermal energy consumed in
summer by buildings is mainly due to the thermal energy needed
for cooling the building (49.93 MJ; or 80% of total output thermal
energy). They observed that for cooling a building, the hourly
demand of the thermal energy is the highest one of the year. They
also found out that the internal gain (34.4 MJ) and solar gain
(21.1 MJ) dominate the total consumed thermal energy. The
thermal energy consumed by transmission was 12.1 MJ, and the
one stored in the building envelope was 15.3 MJ. Their investiga-
tion also showed that the thermal exergy loss in the hot summer
hour (22.14 MJ) is much bigger than the thermal exergy loss in the
cold winter hour (12.98 MJ). This indicates the thermal exergy loss
in the building.
From these studies, we recognized that indoor air enthalpy is
affected by many factors and that internal gains effect the internal
conditions the most. The effects of the other factors can also be
calculated from the results. It can be concluded that by only inte-
grating natural ventilation in the HVAC systems, where the natural
ventilation is based on the temperature difference between the
indoor and the outdoor air is unreasonable to improve the energy
saving in the system. Therefore, this study proposes to cover all the
factors affecting the indoor conditioning space. To achieve this, it
requires the representation of the indoor thermal comfort with all
the factors that affect it. This indoor representation can then be
used as a guideline for the real indoor thermal comfort behaviour.
Furthermore, the study follows a new policy to integrate the nat-
ural ventilation to the HVAC system that is different from the
conventional method in which window opening area is used as a
criterion to measure ventilation quantities [17]. Todorovic et al. has
used CFD to study the energy efficiency optimization from fluid
mechanics point of view [18].
The HVAC system model developed by Homod et al. [19,20],
including building type, dimension, and structure is adopted to
analyse the indoor thermal comfort and as a model guide for
Nomenclature
Symbols
A surface area, m2
C heat capacitance, J/C
dEs/dt rate of change in storage energy of the system, J/s
_Ein energy rate entering the system, J/s
_Eout energy rate leaving the system, J/s
M mass, kg
cp specific heat, J/kg C
_m mass flow rate, kg/s
Mcp heat capacitance, J/C
T temperature, C
u humidity ratio, kgw/kgda
h latent heat/heat transfer coefficient, J/kg, W/(m2
K)
_Q cooling load, W
CF surface cooling factor, W/m2
U construction U-factor (overall heat transfer
coefficient), W/(m2
K)
DT cooling design temperature difference, K
OFt,OFb,OFr opaque-surface cooling factors
DR cooling daily range, K
CFfen surface cooling factor, W/m2
uNFRC fenestration U-factor, W/(m2
K)
PXI peak exterior irradiance, W/m2
SHGC solar heat gain coefficient
IAC interior shading attenuation coefficient
FFs fenestration solar load factor
Et, Ed,ED peak total, diffuse, and direct irradiance, W/m2
TX transmission of exterior attachment
Fshd fraction of fenestration shaded by overhangs or fins
L site latitude, N
j exposure (surface azimuth), degrees from south
SLF shade line factor
Doh depth of overhang, m
Xoh vertical distance from top of fenestration to overhang,
m
Fcl shade fraction closed (0e1)
_v volumetric flow rate, L/s
IDF infiltration driving force, L/(s cm2
)
ℛ thermal resistance, C/w
Noc number of occupants
Nbr number of bedrooms
aroof roof solar absorptance
t time constant, s
I infiltration coefficient
Du indooreoutdoor humidity ratio difference, kgw/kgda
Subscripts
m air in mixing box
r room/return
o outside
os outside supply
i inside
He heat exchanger
a air
w water
aHe air in heat exchanger
L leakage
W in water input
W out water output
wl wall
room inside room
out outside room
g glass
fg heat of vaporization
opq opaque
inf infiltration
fen fenestration
f indoor and outdoor
t at time t
flue flue effective
es exposed
ul unit leakage
ig internal gains
l latent
s sensible/supply
fur furniture
cl closed
R.Z. Homod et al. / Renewable Energy 71 (2014) 639e650640

3. comparison (MGFC). The resulting indoor thermal comfort of the
MGFC can then be integrated as part of the control system to obtain
the buildings' ventilation system model [21]. In these systems,
changes in the state of the estimated indoor condition which is
predicted by the MGFC outputs triggers the control actions. The
control process follows the occurrence of one of two switch posi-
tion changes, normal HVAC system or mechanical ventilation.
Subsequently, this set of alternatives (e.g. mechanical ventilation) is
subjected to assess these alternative control commands and their
implications. The assessment action depends on some related fac-
tors indicators such as indoor air change rate, air temperature,
radiant temperature, relative humidity and air velocity. These fac-
tors are tested by the predicted mean vote (PMV) sensor and then
ranked (after being compared) according to some objective func-
tions (standards) set by the desired options. The PMV sensor is used
as a criterion test for thermal comfort standard for three identical
residential building models by implementing nominal, conven-
tional and proposed methods. The indoor air enthalpy is found to
be significantly affected by indoor thermal comfort, which is in turn
affected by the PMV and that indicates that the PMV is a function of
indoor air storage energy. As seen from the results of implementing
the proposed method, it can be concluded that when the gradient
indoor PMV is positive, it is preferable to restrict the air flow rate of
the ventilation as much as possible, and in the reverse case when
the PMV is negative, it will be preferable to increase the ventilating
rate. Also the type of ventilation adopted by this study is me-
chanical in nature to meet as much energy saving as possible, as
explained by Su et al. [22] where they also concluded that it is not
appropriate to evaluate natural ventilation using the energy utili-
zation coefficient alone.
2. Models
The framework of this section is to describe the three types of
models that are used to represent the system. They are a hybrid
(physical/empirical) model, an artificial neural networks model and
a fuzzy logic identification model. The MGFC and plant models are
identified using the first and second types while the PMV sensor
and TS model are constructed using the third type.
2.1. Modelling of the MGFC and the plant
The estimation of indoor thermal comfort can be hardly evalu-
ated by indoor outdoor temperature difference as used in con-
ventional methods. The indoor temperature alone is clearly
insufficient for evaluating the indoor thermal comfort which is
related to indoor air enthalpy [23,24]. To address this, a feasible
approach is to model the contribution of all factors affecting the
indoor condition. Therefore, in this paper, indoor thermal comfort
is estimated using the model guide for comparison (MGFC) method
that takes into consideration most of internal and external system
parameters that affect the indoor air state. MGFC is more suitable in
these applications whether either a natural ventilation or HVAC
system is used. The MGFC outputs are evaluated using an MPV
sensor to check whether it meets the standards of thermal comfort
or not. When the output of the MGFC meets the indoor standards,
the switch operation algorithm reassigns the actuation task from
the HVAC system to mechanical ventilation and then the Takagi-
Sugeno Fuzzy Forward (TSFF) model takes control of the fan
speed of the supplied air.
MGFC is constructed based on systematic measurements of a
residential building. This empirical model uses the information
from the prevailing outdoor and indoor factors affecting indoor
conditions. Such information which is pertinent to external and
internal building factors are formulated in a way that would be
valid for building model. A building structure can be considered as a
system containing four subsystems; conditioned area, opaque
surfaces fabric, transparent fenestration surfaces and slab floors.
These subsystems affect each other as shown in Fig. 1. The theo-
retical models derived from thermo physical laws of these sub-
systems are usually not suited for buildings operation/control [11].
Therefore a hybrid model is adopted based on thermo physical laws
and empirical residential load factors (RLF) to represent indoor
thermal comfort, taking into consideration all the related
parameters.
Conservation of energy and conservation of mass laws are
applied on the control volume of a subsystem model based on
principles of physical and empirical laws, the empirical being the
residential load factor (RLF) used to compute the cooling (heating)
load based on indoor (outdoor) temperature. A thermal and
moisture model of these subsystems is especially designed by the
previous papers [19,20] to evaluate the effectiveness of the hybrid
(HVAC system and mechanical ventilation) strategies to improve
the energy saving level in buildings. For the overall HVAC system
shown in Fig. 1, the outlet of the indoor air temperature (Tr) and the
indoor humidity ratio (ur) are functions consisting of the 12
outside/inside parameters for the five subsystems (building struc-
ture, mixing air chamber, pre-cooling coil, main cooling coil and
conditioned space). The consolidation of the five subsystems
together is discussed in a previous work [19] to provide the overall
equation model as follows:
TrðsÞ
urðsÞ
!
¼
T1;1ðsÞ T1;2ðsÞ T1;3ðsÞ T1;4ðsÞ T1;5ðsÞ T1;6ðsÞ T1;7ðsÞ T1;8ðsÞ T1;9ðsÞ T1;10ðsÞ T1;11ðsÞ T1;12ðsÞ
T2;1ðsÞ T2;2ðsÞ T2;3ðsÞ T2;4ðsÞ T2;5ðsÞ T2;6ðsÞ T2;7ðsÞ T2;8ðsÞ T2;9ðsÞ T2;10ðsÞ T2;11ðsÞ T2;12ðsÞ
!
2
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
4
_mwðsÞ
_mmwðsÞ
_mosðsÞ
_mrðsÞ
ToðsÞ
uoðsÞ
f4
_Qig;l
Aslab
fDR
k2
ΤrðsÞ
3
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
5
(1)
R.Z. Homod et al. / Renewable Energy 71 (2014) 639e650 641

4. where
T1,1(s)T1,2(s),,,T1,12(s) and T2,1(s)T2,2(s),,,T2,12(s) are the
input factors. A detailed description of all parameters and factors of
Eq. (1) and the steps to obtain them are presented in Appendix A.
Eq. (1) and Fig. 1 imply that twelve inputs and two outputs exist
in the system;
The twelve input variables of the system are:
1. _mwðsÞ ¼ chilled water supply flow rate to pre-cooling coil,
2. _mmwðsÞ ¼ chilled water supply flow rate to main cooling coil,
3. _mosðsÞ ¼ outside air flow rate to conditioned space,
4. _mrðsÞ ¼ flow rate of return air to conditioned space,
5. To(s) ¼ perturbations in outside temperature,
6. k2 ¼ perturbations because of building envelope's thermal
resistance,
7. f4 ¼ internal sensible heat gain perturbations,
8. Aslab ¼ slab floors area,
9. fDR ¼ location factor,
10. uo(s) ¼ outside air humidity ratio perturbations,
11. _Qig;l ¼ internal latent heat gain perturbations, and
12. Tr(s) ¼ temperature of conditioned space.
The system's output variables on the other hand are:
1. Tr(s) ¼ conditioned space or room temperature, and
2. ur(s) ¼ conditioned space or room humidity ratio.
Eq. (1) and Fig.1 also indicate that the model is built on the basis
of variable water volume (VWV) and variable air volume (VAV). In
other words, the HVAC system model is analysed entirely using the
large scale system theory based on the “decomposition and coor-
dination” scheme [25].
In reality, however, there are some differences in the output
values between the MGFC model and the real plant that could be
mainly attributed to the simplifications imposed by the model and
input values uncertainties.
Because of the stochastic or uncertainties and the non-linearity
properties of the plant building model, the indoor thermal model is
identified here based on fuzzy-neural strategies. Following the
work of Ayata et al. [15], the adaptive neuro-fuzzy inference sys-
tems (ANFIS) is employed for the identification of the plant. The
fuzzy model is derived from a linear first order Sugeno polynomial
normally composed of a number of rules. Through training and
validation, the identification process resulted in a high correlation
between the data of the simulated MGFC and Artificial Neural
Networks (ANN) plant models, signifying a successful training of
the proposed ANN plant model. The identification process employs
a hybrid learning algorithm in the ANFIS editor of the Fuzzy Logic
Toolbox under Matlab®
.
2.2. Controller and sensor modelling
The previous section discussed the many factors that affect in-
door thermal comfort and that indicates the significance of
ensuring specific hybrid system operational modes to achieve the
desired environmental conditions. To control such stochastic in-
door thermal behaviour, the system needs to be adequately robust
against both internal and external conditions/disturbances.
Because of the coupling property between the relative humidity
and temperature of the building model, this work uses PMV as a
feedback sensor to alleviate this problem [23]. PMV allows the
controller more flexibility to manipulate the input of the model to
obtain the desired response based on the output of the sensor
(objective). The PMV sensor model has been installed based on the
well known Fanger's empirical model that predicts the indoor
thermal comfort. This PMV model became a general standard since
the 1980s [26,27]. The PMV value range is [À3 þ3]. A negative value
indicates a cold sensation, a value close to zero signifies a comfort
situation and a positive value represents a hot sensation. The
empirical equation that estimates the PMV is presented in Ref. [28]
as:
Here, Tcl,pa,hc and fcl are obtained as follows:
Tcl ¼ 35:7 À 0:028ðM À WÞ
À 0:155Icl
h
3:96*10À8
fcl
n
ðTcl þ 273Þ4
À ðTrr þ 273Þ4
o
þ fclhcðTcl À TrÞ
i
pa ¼
psRH
100
and
ps ¼
C1
T
þ C2 þ C3T þ C4T2
þ C5T3
þ C6T4
þ C7 lnðTÞ
hc ¼
(
2:38ðTcl À TrÞ0:25
for 2:38ðTcl À TrÞ0:25
12:1
ffiffiffiffiffi
va
p
12:1
ffiffiffiffiffi
va
p
for 2:38ðTcl À TrÞ0:25
12:1
ffiffiffiffiffi
va
p
hc ¼
1:00 þ 0:2Icl for Icl 0:5 clo
1:05 þ 0:1Icl for Icl 0:5 clo
where M is the metabolism (w/m2
), W is the external work (w/m2
),
Icl is the clothing's thermal resistance (m2
k/w), fcl is the ratio of the
surface areas of the clothed body and the nude body, Tr is tem-
perature of the room (C), Trr is the mean radiant temperature of the
room (C), va is the air relative velocity (m/s), Pa is the pressure of
water vapour (pa), Ps is the saturated vapour pressure at the specific
temperature (pa), RH is the relative humidity in percentage, C1, C2,
…, C7 are empirical constants that can be found from Ref. [29], T is
the absolute dry bulb temperature in kelvins (K), hc is the convec-
tive heat transfer coefficient (w/m2
k) and Tcl is clothing's surface
temperature (C).
The solution for Fanger's model Eq. (2) consumes a lot of time
and requires a lot of computational effort. For this reason, it is
difficult to use Fanger's model in real time applications or to
represent it on modern computers. A possible way of applying a
nonlinear model like this in real-time is through the utilization of
nonlinear system identification methods. Such a method is Fuzzy
Logic.
PMV ¼
À
0:303eÀ0:036M
þ 0:028
ÁÂ
ðM À WÞ À 3:05*10À3
f5733 À 6:99ðM À WÞ À pag À 0:42fðM À WÞ À 58:15g
À1:7*10À5
Mð5867 À paÞ À 0:0014Mð34 À TrÞ À 3:96*10À8
þ fcl
n
ðTcl þ 273Þ4
À ðTrr þ 273Þ4
o
À fclhcðTcl À TrÞŠ
(2)
R.Z. Homod et al. / Renewable Energy 71 (2014) 639e650642

5. Model-based control is a solution considered by the work of Virk
et al. [30]. In fact, many algorithms have been devised and imple-
mented using an MGFC model that generates the necessary control
action for the operation of the ventilation, lighting and shading
systems. Furthermore, the controller of a HVAC system is expected
to be able to manipulate the inherent nonlinear characteristics of
these large scale systems that also have pure lag times, big thermal
inertia, uncertain disturbance factors and constraints. To control
these characteristics and tackle nonlinearities effectively, this paper
proposes the implementation of an online tuned Takagi-Sugeno
Fuzzy Forward (TSFF) control strategy. From the control structure
presented by Homod et al. [24], it is obvious that the TSFF model
has one input (sensor output) and five outputs (AHU inputs);
supplied chilled water to pre-cooling coil and main cooling coil,
return and fresh supplied air to conditioned space and indoor air
velocity.
To clarify the model identification process for the PMV, Eq. (2)
and the TSFF model (data set), we follow the procedure pre-
sented by Homod et al. [23,24], which can be summarized by the
following steps:
1 Prepare an inputeoutput data set for the training of the PMV
sensor model. Using a feasible range for input parameters, these
data are obtained from Fanger's model and the AHU and
building model.
2 Break up the outputs of each model into clusters, and then
represent each cluster by Takagi-Sugeno fuzzy rules. The
weights and clusters parameters are obtained from these rules.
3 The parameters and weight layers, obtained from the training
data set and optimized by GausseNewton Method for Nonlinear
Regression (GNMNR) can be structured as a layered framework.
3. Integrated control structure
When identifying the overall dynamics of the building, the
model should include the effects of surrounding environment
because they heavily dependent on it. To minimize the effect of
various heating load disturbances on the building and lighten the
activity of actuators, natural ventilation can also be used to enhance
control performance. The proposed integration method is easier to
implement in practice than traditional methods where natural
ventilation and HVAC systems are controlled separately. The
application of MGFC is similar to the predictive engines for a model-
based control approach in order to implement the hybrid HVAC
system and mechanical ventilation control. It is feasible to apply
such an integrated control strategy if Takagi-Sugeno Fuzzy Forward
(TSFF) control is used. In order to obtain the most benefits out of
mechanical ventilation, the best optimization would be to set the
variable-air-volume (VAV) to minimum air flow rates when the
rated indoor PMV changes to positive gradient to achieve accept-
able indoor air quality (IAQ). In this case, the VAV is in its minimum
position and this will lead to energy saving as much as possible.
Uncertainties in the weather and in the number of occupants are
taken into account in the MGFC calculations, but if there are any
deviations in the output because of such effects, there is a safety
factor imposed on the amplitude of the standard operation condi-
tion, and that will ensure that the system will provide the desired
indoor condition. So there is no need to find the exact optimized
decision of the PMV in the MGFC loops. In order to realize a good
indoor thermal comfort by MGFC strategy, the TSFF control strategy
should use a virtual model of the residential building running in
parallel. In this manner, the real structure reacts to actual contex-
tual weather conditions, building control operations and occupancy
interventions, while the virtual MGFC model is used to predict the
response of the building to mechanical ventilations. This strategy is
illustrated in Fig. 2, where all the parts are inside a microcontroller
except the plant model which represent the building and struc-
tures. From Fig. 2, the system has two operation modes according to
the MGFC output as follows:
Mode 1: the chiller is tuned on; the controller manipulates five
controlled parameters.
Mode 2: the chiller is turned off; the controller manipulates one
controlled parameter (outdoor air fan speed).
4. Building energy and mass transfer analysis
The hybrid modelling of the MGFC is derived from the balance of
both building's energy and mass for each of the building fabric and
Fig. 1. HVAC subsystems block diagram.
R.Z. Homod et al. / Renewable Energy 71 (2014) 639e650 643

6. air handling unit (AHU) subsystem models. To thermally analyse
and model the overall behaviour of an HVAC system with me-
chanical ventilation, conservation of energy and mass theories are
applied. The reason for such a choice is based on the fact that in any
subsystem control volume, energy enters and leaves it using two
processes; flowing streams of matter and heat transfer. These
processes are dominant in HVAC systems.
In general, the electric power consumption of the HVAC systems
is a function of the Coefficient of Performance (COP) of chillers,
Energy Efficiency Ratio (EER) of building and the cooling load of the
building. The EER and COP are both constant for any specific
building and chiller respectively. Whereas the total cooling load of
the building varies depending on the disturbances and controllable
variables. Therefore, the total electric consumption power can be
summarized by Eq. (3), [31,32].
EP ¼
XN
i
chli
copi
þ EPAHU ¼
TBCL
EER
(3)
where EP is the total electric consumption power, N is the number
of chillers, chl is the chiller power, EPAHU is the electric power
consumed by AHU and TBCL is the Total Building Cooling Load.
From Eq. (3) it is obvious that EP can be derived by two different
methods, from the balance equations of energy and mass of the
building fabric (right term of Eq. (3)) and AHU subsystems equip-
ment (middle term of Eq. (3)). Therefore, the conservation of energy
and mass theories are applied to thermally analyse and model the
overall behaviour of an HVAC system. These theories are based on
the fact that in the control volume of any subsystem, energy
transfers from/to subsystem by two types of processes; association
to the mass transfer and conventional heat transfer (conduction,
convection and radiation).
In this work, the system is subdivided into the building and the
AHU control volumes. To evaluate the building's sensible heat gain,
the following thermal balance equation is applied on the building
control volume:
The left term of Eq. (4) denotes the output of the AHU, which
represents the heat and mass transferred to the building control
volume. On the other side of Eq. (4), the first part (accumulation or
storage of energy) is the thermal mass stored in inner wall, indoor
Fig. 2. The structure of integrated control of mechanical Ventilation and HVAC Systems.
_Qs
z}|{
Cooling load
¼ _Qair þ _Qfur
zfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflffl{
accumulation or storage of energy
þ _Qopq þ _Qfen þ _Qslab þ _Qinf þ _Qig;s
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
difference between input and output of energy
(4)
R.Z. Homod et al. / Renewable Energy 71 (2014) 639e650644

7. air and furniture, and the second part (difference between input
and output of energy) represents other inputs/outputs to the con-
trol volume of the building. These terms can be written mathe-
matically using time dependent heat balance equations as follows:
_Qs;t ¼ _mm cpa
À
Tr;tÀTs;t
Ázfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflffl{
Cooling load exerted by AHU
;
_Qair ¼ Maircpa
dTair
dt
zfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflffl{
storage energy at air mass
;
_Qfur ¼
X
j
Mfurj
cpfurj
dTfur
dt
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
storage energy at furniture mass
;
_Qopq ¼
X
j
Awj
hij
À
TWlin
À Tr
Ázfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
convection heat gain from opaque surfaces
;
_Qfen ¼
À
Tgin
À Tr
Á
Rg
zfflfflfflfflfflfflffl}|fflfflfflfflfflfflffl{
conduction heat gain
þ
X
j
Afenj
PXIj  SHGCj  IACj  FFsj
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
solar radiation heat gain
;
_Qslab ¼
X
j
Aslbj
hij
À
Tslbin
À Tr
Ázfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
convection heat gain from slab floors
;
_Qinf ¼ Cs  AL  IDF
À
To;t À Tr;t
Ázfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
heat gain due to infiltration
and
_Qig;s ¼ 136 þ 2:2Acf þ 22Noc
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
sensible cooling load from internal gains
:
Substituting these quantities into Eq. (4) yields:
_mmcpa
À
Tr;tÀTs;t
Á
¼ Mrcpa
dTr;t
dt
þ
X
j
Mfurj
cpfurj
dTfur;t
dt
þ
X
j
Awj
hij
À
TWlin;t À Tr;t
Á
þ
À
Tgin;t À Tr;t
Á
Rg
þ
X
j
Afenj
PXIj  SHGCj  IACj  FFsj
þ
X
j
Aslbj
hij
À
Tslbin
À Tr
Á
þ Cs  AL  IDF
À
To;t
À Tr;t
Á
þ 136 þ 2:2Acf þ 22Noc
(5)
The building latent heat gain is related to moisture transfer,
which can be evaluated by applying the conservation of time
dependent mass law on the building control volume as shown in
Eq. (6);
_ms
À
ur;t À us;t
Ázfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflffl{
rate of moisture withdrawal by AHU
¼
dMrur;t
dt
zfflfflfflffl}|fflfflfflffl{
rate of moisture change
þ
_Qig;l
hfg
zffl}|ffl{
rate of moisture generation
þ _minf uo;t À _mrur;t
zfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflffl{
rate of moisture transfer
(6)
The left term of Eq. (6) is the moisture rate absorbed by the AHU.
On the right hand side of Eq. (6), the first part (rate of moisture
change) is the change of air moisture in the building in time interval
dt, and the other parts are related to the indoor inputs/outputs and
generated moisture.
To evaluate the sensible and latent heat gains for the building,
it is feasible to calculate the left hand sides of Eqs. (5) and (6)
that can be obtained by applying the laws of conservation of
energy and mass to the control volume of the AHU. The AHU is
subdivided into three subsystems; mixing air chamber, pre-
cooling coil and main cooling coil. The energy is consumed
only in the pre-cooling and main cooling coils, so the energy and
mass control volume will be applied on these two subsystems as
follows:
_mw;tcpwðTwo ÀTwinÞ
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
energy absorbed by the coil
¼ MHecpHe
dTh;t
dt
zfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflffl{
energy accumulation in the metal mass of coil
þ _mo;tcpa
À
To;t ÀTos;t
Ázfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
sensible energy delivered by air
þ _mcon: ;thfg
zfflfflfflfflfflffl}|fflfflfflfflfflffl{
latent energy delivered by moisture withdrawal
(7)
The term “energy absorbed by the coil” in Eq. (7) is the sensible
and latent heat load exerted by the pre-cooling coil. On the right
side of the equation, the first term (energy accumulation in the
metal mass of coil) is the rate of change of heat storage in the coil
mass, the second term (sensible energy delivered by air) is the fresh
air sensible cooling load, and the third term (latent energy deliv-
ered by moisture withdrawal) is the latent energy absorbed by the
coil due to condensation moisture. The third term in right side of
Eq. (7) can be evaluated by applying the law of mass conservation
on air flow stream for pre-cooling coil. The following can be
obtained:
_mo
À
uo;t À uos;t
Ázfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflffl{
rate of moisture withdrawal by preÀcooling coil
¼
dMaheuos;t
dt
zfflfflfflfflfflfflffl}|fflfflfflfflfflfflffl{
rate of moisture change
þ
_mw;tcpwðTwo À TwinÞ À _mo;tcpa
À
To;t À Tos;t
Á
hfg
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
rate of moisture condensation
(8)
Substitute the left-hand side of Eq. (8) for _mcon:;t in Eq. (7) to
obtain:
R.Z. Homod et al. / Renewable Energy 71 (2014) 639e650 645

8. _mw;tcpwðTwo ÀTwinÞ
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
energy absorbed by the coil
¼ MHecpHe
dTh;t
dt
zfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflffl{
energy accumulation in the metal mass of coil
þ _mo;tcpa
À
To;t ÀTos;t
Ázfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
sensible energy delivered by air
þ _mo;t
À
uo;t Àuos;t
Á
hfg
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
latent energy delivered by air dehumidification
(9)
Using the same procedure as the pre-cooling coil to obtain
sensible and latent heating loads for dynamic subsystem equations,
the main cooling coil can be written mathematically by using the
time dependent equation of the control volume as follows:
_mmw;t cpwðTwo ÀTwinÞ
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
energy absorbed by the coil
¼ MmHecpHe
dTh;t
dt
zfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflffl{
energy accumulation in the metal mass of coil
þ _mm;t cpa
À
Tm;t ÀTs;t
Ázfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
sensible energy delivered by air
þ _mm;t
À
um;t Àus;t
Á
hfg
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
latent energy delivered by air dehumidification
(10)
Fig. 3. Percentage signal valve position within 24 h.
Fig. 4. The simulated building plan.
Table 1
Material properties of model building construction.
Component Description Factors
Roof/ceiling Flat wood frame ceiling (insulated with R-5.3 fibreglass) beneath vented attic with
medium asphalt shingle roof
U ¼ 0.03118 (W/(m2
K))
aroof ¼ 0.85
Exterior walls Wood frame, exterior wood sheathing, interior gypsum board, R-2.3 fibreglass insulation U ¼ 51 W/(m2
K))
Doors Wood, solid core U ¼ 2.3 W/(m2
K)
Floor Slab on grade with heavy carpet over rubber pad; R-0.9 edge insulation to 1 m below grade Rcvr ¼ 0.21 (m2
K)/W) Fp ¼ 85 W/(m2
K)
Windows Clear double-pane glass in wood frames. Half fixed, half operable with insect screens (except
living room picture window, which is fixed). 0.6 m eave overhang on east and west with eave
edge at same height as top of glazing for all windows. Allow for typical interior shading, half closed.
Fixed: U ¼ 2.84 W/(m2
K); SHGC ¼ 0.67
Operable: U ¼ 2.87 W/(m2
K);
SHGC ¼ 0.57; Tx ¼ 0.64; IACcl ¼ 0.6
Construction Good Aul ¼ 1.4 cm2
/m2
R.Z. Homod et al. / Renewable Energy 71 (2014) 639e650646

9. Thermal energy transfer rates (sensible cooling load) from the
building by mechanical ventilation air flows (Qvent) is calculated by
using Eq. (11).
_Qvent ¼ _ms;tcpa
À
Tr;t À Ts;t
Á
(11)
The air supply system power in the mechanical ventilation state
(transmission power) is mainly the supply fan power, which can be
calculated by applying the law of conservation of energy on the
control volume of the AHU as fallows [32]:
_Qfan ¼ _ms;tcpa
À
Ts;t À To;t
Á
(12)
According to the energy balance for the indoor conditioned
space of Eq. (4), flow thermal energy values from 1. opaque sur-
faces, 2. transparent fenestration surfaces, 3. infiltration, 4. indoor
load and 5. ventilation, are calculated by using the steady state
conditions of Eq. (4), where all these flow thermal energy values
equal the cooling load extracted by HVAC systems or mechanical
ventilation, which equal the left-hand side of Eq. (5) that can in turn
be calculated by summing Eqs. (9)e(12).
The instantaneous building cooling load can be obtained by
simulation after modelling the HVAC system. Also, the instanta-
neous building cooling loads directly impact the outputs of the
controller signals. Therefore, a calculation method employed in this
work is based on the controller's output signals. The outputs'
Fig. 5. Comparison for indoor operative temperature loop with standard acceptable limits.
Fig. 6. The three strategies indoor thermal action under real indoor/outdoor parameters effects.
R.Z. Homod et al. / Renewable Energy 71 (2014) 639e650 647

10. controller signals manipulate the valves of the pre-cooling coil,
main cooling coil, reheating coil and the dampers of the return and
fresh air to track the objective of the HVAC system. The valves and
dampers are designed according to the heating/cooling load of the
building. The opening position of these valves and dampers are
given by percentage of the fullest extent as shown in Fig. 3.
5. Simulation result and discussion
The simulated building is a single story house chosen as a simple
structure of 4.5 m in height and 248.6 m2
of gross ground floor area.
For the whole building, the net floor area is 195.3 m2
. The windows
and wall exposed gross area is 126.2 m2
while the net wall exterior
area is 108.5 m2
. The volume of the overall house excluding the
garage is 468.7 m3
. The simulated building plan and envelope
components' physical properties are shown in Fig. 4 and Table 1.
5.1. Plant natural ventilation test
The plant is tested under real outdoor/indoor conditions where
the outdoor condition weather is for Kuala Lumpur city, the capital
of Malaysia, where the mean maximum and minimum air tem-
peratures are 35 C and 20 C, respectively. These statistical figures
are obtained from the weather bureau of Kuala Lumpur [33]. In
tropical climates, such as the one in this paper, during the day
buildings are overheated because of the incident solar heat gains
Fig. 7. Three HVAC systems cooling load comparison.
Fig. 8. Energy consumed by three different strategies of HVAC systems.
R.Z. Homod et al. / Renewable Energy 71 (2014) 639e650648

11. through the building envelope and solar diffusion through win-
dows, which greatly affect indoor thermal comfort. According to
these effects, the plant is tested to observe the indoor conditions
behaviour of a loop cycle temperature of 24 h by ASHRAE Standard
55-04 [34]. It is obvious that the indoor loop cycle intersects with
the standard region, as shown in Fig. 5. This means that the indoor
condition is acceptable in some period where at that time there is
no need to use an HVAC system. In spite of the fact that the stan-
dard 55-04 does not allow the use of this option for outdoor tem-
peratures above 33.5 C or below 10 C, in general it meets with
most of the indoor/outdoor conditions. This indicates that natural
ventilation is suitable in saving energy. At the same time, Fig. 5
shows that natural ventilation does not provide or meet desired
indoor conditions within 24 h. In other words, it is inevitable to use
an HVAC system.
5.2. Verifying indoor thermal comfort
The adaptive thermal comfort ASHRAE Standard 55-04 has
many limitations. Apart from outdoor temperature limitation, 90%
acceptable range of indoor occupations is limited by the indoor air
dew point [34]. To evaluate indoor thermal comfort, there are many
other criteria that can be used, but the most popular and closest one
to the human thermal sensation is the PMV [35]. This criterion is
used to estimate indoor thermal sensations for three different
system strategies; nominal HVAC system, conventional integration
and proposed policy. Fig. 6 shows the indoor thermal behaviour for
the three strategies under real weather circumstances for Kuala
Lumpur city. As mentioned before, Kuala Lumpur is a tropical city
that across the year enjoys a night-time temperature range of
20 Ce25 C and a daytime range of 26 Ce35 C [33]. From Fig. 6
and according to the recommendations of ASHRAE 55-92 [26] and
ISO-7730 [27] standards, the indoor acceptable tolerance margin
for the thermal sensation comfort is limited
between À0.5 PMV 0.5. This indicates that the proposed
strategy is functioning within acceptable margins in spite of having
more roughness when compared to other strategies.
The proposed method benefits from mechanical ventilation as
much as possible because it employs many of the effective factors.
The most effective one is the building mass of thermal and this is
not the case in the conventional integration which is based on
outdoor temperature. Also, when the induced mechanical ventila-
tion decline in gradient indoor PMV, thermal comfort is provided
for mainly using ventilation and the required flow rate is large
compared to those required for positive gradient PMV ventilation.
The provision of positive gradient PMV of natural ventilation
related to overheating risk has been identified by the control al-
gorithm based on minimum fresh air flow rate requirement.
5.3. Energy saving results and discussion
Fig. 6 shows the performance of the proposed strategy where it
is clear that mechanical ventilation does not depend on the outdoor
temperature as is the case with conventional integrated methods.
This is supported by the fact that there are many other factors that
have effects on the indoor air enthalpy and the thermal energy
stored in the building. Fabric and its fixtures (thermal mass) in a
building is the most effective factor, which is taken care of here by
the MGFC and plant model calculations. Care should be also taken
for indoor thermal margin variation; this is done by appropriately
setting in place integration control mechanisms to protect against
overcooling the building during cold weather spells. However, it is
not easy to implement this because of stochastic disturbances and
influences affecting the thermal behaviour of the building and re-
quires high quality predictions. Therefore, a switch operation
algorithm is set up with indoor thermal margin varying between
0.35 and À0.35 during the day operation.
The RLF building model built in Simulink makes use of the or-
dinary differential equation (ODE) solvers that can be automatically
configured at run-time. The controller's algorithm is built using
Matlab m-files, the parameters memory layer and S-functions to
make use of the online capabilities required for tuning those pa-
rameters. The techniques of cooling loads' calculation is straight
forward; thermal balance equations are implemented by using
arithmetic functions, and then the consumed energy can be ob-
tained using Eqs. (9)e(12). The simulation results of the consumed
energy by the cooling coil load are shown in Fig. 7.
The nominal system cooling load coil shows more energy con-
sumption than the other systems because the cooling process
continuously operates even at low outdoor temperatures to meet
the demands of indoor thermal comfort and overcome thermal
mass and indoor loads. The conventional integral system exhibits
less cooling load exerted by the HVAC system compared to the
nominal method because it utilizes the outdoor temperature
whereas the proposed integral control method shows the best
performance because it further utilizes the outdoor temperature
and also utilizes absorbed heat by slab floor and thermal inertia
caused by heat stored by internal wall, indoor air and building
fixtures. Therefore, from Fig. 6, it is easy to observe that the pro-
posed strategy extends periods of mechanical ventilation in spite of
higher outdoor temperature than indoor temperature because
other factors contribute to saving energy. The simulation results of
Fig. 7 match quite well with the numerical calculations based on
the Cooling Load Factor for glass/Corrected Cooling Load Temper-
ature Difference (CLF/CLTDc) method [36]. The calculations take
into consideration that variation of the outdoor environment in-
fluences indoor thermal loads. The building cooling load is calcu-
lated every one hour to obtain the margin error between simulation
result (proposed system) and calculation which is varied as
(0.064e0.378) kW. From the simulation results shown in Fig. 8, one
can calculate the consumed energy within 24 h for the building
plant in typical conditions (indoor loads and outdoor weather);
278.5 (Kwh/d) for nominal HVAC system, 235.1 (Kwh/d) for con-
ventional integrated system and 190.4 (Kwh/d) for the proposed
method. The simulation results also show that the proposed
strategy achieved significant energy saving of about 31.6%, while
the conventional method saved about 16%. The results of the pro-
posed method are compatible with the performance approach
movement that focuses on the global intention of energy regula-
tions. The proposed method can also be used to ensure that the
global saving target is achieved. As an example, the goal of ASHRAE
189.1 is to decrease in site energy consumption by 30% compared to
Standard 90.1-2007 [5]. Furthermore, research has shown that a
reduction of between 18% and 60% in cooling load is achieved using
thermal mass [37,38]. But these studies were mainly field experi-
mentations or laboratory monitoring without systematic theoret-
ical justifications. It is therefore the aim of this paper to present a
detailed theoretical analysis on the relationship between uses of
thermal mass and contribution of all factors that can affect indoor
thermal conditions.
6. Conclusion
This paper investigates the use of natural ventilation as a po-
tential passive cooling system to sustain energy consumed by HVAC
systems. This was done by employing all factors affected by indoor
air enthalpy. The proposed control system of this paper includes as
part of its structure an internal model (MGFC) to predict system
deviations and generate control actions pertaining to mechanical
ventilation time. The task of the MGFC output is therefore to act as a
R.Z. Homod et al. / Renewable Energy 71 (2014) 639e650 649

12. navigation guide for the switching decision algorithm. As a case
study for testing the system, the weather of Kuala Lumpur, the
capital of Malaysia, was considered. From the results of the per-
formed simulations, it can be concluded that when the indoor PMV
gradient is positive, it is preferable to restrict the air flow rate of the
ventilation as much as possible and vice versa. One of the impli-
cations of this study is to consider hedging against indoor thermal
fluctuation, by setting the margin of the indoor thermal condition
operation point between À0.35 to 0.35, on the PMV scale indicating
the existence of some safety factor for the MGFC against un-
certainties in the system. This leads to energy saving by the actu-
ated AHU, with the total energy consumption for both conventional
and proposed integrated control systems that was calculated at 16%
less (43.4 kWh/d) and 31.6% less (88.1 kWh/d) than in typical HVAC
system, respectively.
In addition, an important finding of this study was that me-
chanical ventilation cannot depend on the outdoor temperature
alone in spite of its effect on the indoor air enthalpy, because there
are many other factors that affect the indoor air enthalpy, evident
from Fig. 6. This agrees with the fact that the natural ventilation and
thermal mass can reduce energy consumption in the HVAC system
and establish indoor thermal comfort in the building.
The energy saving contribution by this strategy makes it a good
investment due to the low implementation cost of the hardware in
existing buildings. Furthermore, the advantage of the proposed
method is its readiness to be implemented on conventional
building fabric without any changes as in the double skin method
[39], which cost a lot in the modification process of the building
structure. Moreover, such a strategy is beneficial for both heavier
and lighter building constructions [17].
Acknowledgement
The authors would like to thank the Ministry of Higher Educa-
tion, Malaysia (FRGS/1/2012/TK07/UNITEN/03/7) for funding this
project under their Fundamental Research Grant Scheme (FRGS).
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
dx.doi.org/10.1016/j.renene.2014.06.015.
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