A novel hybrid modelling structure fabricated by using takagi sugeno fuzzy to forecast hvac systems energy demand in real-time for basra city

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Sustainable Cities and Society
journal homepage: www.elsevier.com/locate/scs
A novel hybrid modelling structure fabricated by using Takagi-Sugeno fuzzy
to forecast HVAC systems energy demand in real-time for Basra city
Raad Z. Homoda,
*, Hussein Togunb
, Haider J. Abdc
, Khairul S.M. Saharid
a
Department of Oil and Gas Engineering, Basrah University for Oil and Gas, Basra, 61004, Iraq
b
Department of Biomedical Engineering, University of Thi-Qar, Iraq
c
Department of Electrical Engineering, Babylon University, Iraq
d
Department of Mechanical Engineering, Universiti Tenaga Nasional, Malaysia
A R T I C L E I N F O
Keywords:
Nonlinear modeling
Uncertain disturbance
Multi-climatic zone model
Outdoor thermal comfort
TS Fuzzy identification
Multi-zone energy forecasting
A B S T R A C T
the HVAC systems consume more than half of the total buildings energy demand, forecasting the cooling/heating
load of the building is important to predict buildings energy demand. The energy assessment tools such as a
model for forecasting building energy consumption is based on outdoor thermal conditions, the outdoor con-
ditions are highly nonlinear in real life cannot be represented by linear differential equations and have an
uncertain disturbance nature. This paper contrives a novel nonlinear model structure to cope with such diffi-
culty, which is composed of two hybrid nonlinear forms, Takagi-Sugeno fuzzy system (TS-FS) and Neural
Networks’ Weights. Such a structure has many advantages, including suitability for multi-layer implementations
like an integrated eight-dimension net of parameters and weights which represents model input-output relations
of a nonlinear system. The Gauss-Newton algorithm is used to tune model weights and parameters for the fitting
of nonlinear regression of clusters model to data. The main feature of the proposed model is to express the
dynamic conditions of the outdoor thermal environment of each fuzzy implication by a cluster functions model
and thus promote the prediction performance. The overall proposed model is tested on the training and vali-
dation of multizone then compared with the RLF model. The corresponding results show that a better hybrid
modelling and uncertainty mitigation which is achieved without significant loss of prediction accuracy.
1. Introduction
The curve trend of global energy consumption is significantly on the
rise and according to United Nations Environment Program (UNEP), the
residential and commercial buildings consume more than 40 percent of
this energy (Alfonso, Savino, Alice, Ilaria, & Vincenzo, 2017; Fang, Ma,
& Deng, 2020), with HVAC systems contributing more than 40 percent
of buildings energy (Homod, 2018). Based on such an energy usage
profile for buildings and HVAC systems, the previous researches show
that the assessment of Greenhouse Gas (GHG) emissions is about one-
third of the world's average (2015, Ahmed & Homod, 2014; Ahmed,
Mohamed, Homod, & Shareef, 2016; Péan, Costa-Castelló, & Salom,
2019; R. Homod, 2014). The GHG emissions are caused by maintaining
the suitable indoor thermal comfort of buildings and running other
facilities in buildings that require energy (Kumar, Pal, & Singh, 2019).
The reducing buildings energy whenever it is necessary, leads to miti-
gate GHG emissions and assist to promote sustainable buildings. The
HVAC system which is the largest consumer of energy in the buildings,
it is represented as a main potential contributor to reduce GHG emis-
sions and saving energy (Ren & Cao, 2019).
The accurate prediction model of the buildings load is categorical
imperative to evaluate building energy consumption, therefore in re-
cent years, there are many studies which have suggested diff ;erent
prediction models (Duana, Darvishan, Mohammaditab, Wakilde, &
Abedinia, 2018). The models for building's load forecasting techniques
can be divided into three types of techniques; traditional forecasting,
modified traditional, and soft computing. There are many kinds of
traditional and modified traditional available for building's load fore-
casting, like nonlinear autoregressive model (NARM) (Ahmad & Chen,
2019), support vector machines (SVM) (Xuemei, Lixing, Jinhu, Gang, &
Jibin, 2010) and residential load factor (RLF) (Homod, Sahari, Almurib,
& Nagi, 2011; Homod, Sahari, Almurib, & Nagi, 2011). The latest stu-
dies on soft computing techniques are divided into three categories: a
white-box model such as fuzzy logic (Moglia, Cook, & McGregor, 2017),
a grey-box model such as expert systems (Wang, Wang, & Zhang, 2020),
and a black-box model such as an artificial neural network (ANN) (Liu
https://doi.org/10.1016/j.scs.2020.102091
Received 26 June 2019; Received in revised form 18 December 2019; Accepted 5 February 2020
⁎
Corresponding author.
E-mail address: raadahmood@yahoo.com (R.Z. Homod).
Sustainable Cities and Society 56 (2020) 102091
Available online 14 February 2020
2210-6707/ © 2020 Elsevier Ltd. All rights reserved.
T

et al., 2019; Sha et al., 2019). Most of these categories for building's
load forecasting are built based on residential building electricity
consumption (Fendri & Chaabene, 2019; Yuan, Farnham, Azuma, &
Emura, 2018), where others are highly dependent on both residential
and commercial buildings (Rahman, Srikumar, & Smith, 2018; Yuan
et al., 2018).
The buildings in developed countries such as the U.S. consumes a
large amount of energy demand, which accounts for up to 47.6 % of the
total demand for energy (Rashid et al., 2019), and the building elec-
tricity consumption is highly related to HVAC load. Therefore, this
study cares about the HVAC systems loads calculating to forecast en-
ergy consumption in the buildings sector which is responsible for more
than 55 % of its global electricity demand (Homod & Sahari, 2013;
Homod, 2018). In fact, the electricity demand curve of the HVAC
system is influenced initially by the outdoor conditions (Homod, Sahari,
& Almurib, 2014; Homod, Sahari, Mohamed, & Nagi, 2010). The most
important factors of outdoor conditions effect on HVAC energy
consumption are relative humidity (RH) and outdoor temperature
(2017a, Ahmed, Mohamed, Shareef et al., 2016; Homod, 2013; Homod,
Sahari, Mohamed, & Nagi, 2010), where the impact on outdoor con-
ditions of energy and thermal indoor conditions are investigated by
previous works (Ahmed, Mohamed, Homod, & Shareef, 2016; Ahmed,
Mohamed, Homod, & Shareef, 2017). The HVAC energy consumption is
complex to be predicted due to the nonlinear and dynamic nature of
their working conditions (R.Z. Homod, 2014). Therefore, precise fore-
casting of HVAC energy consumption is required to improve the accu-
racy of predict outdoor conditions models so as to predict indoor
building thermal load and the use of such models significantly improves
the predictive accuracy of HVAC energy consumption (Ahmed,
Mohamed, Homod, & Shareef, 2017; Homod, Sahari, Almurib, & Nagi,
2012; Martins et al., 2016; Sahari, Abdul Jalal, Homod, & Eng, 2013).
The strong impact of the outdoor condition on energy consumption by
HVAC systems in buildings is the stimulus behind the development of
forecasting climate models (Liu, Liu, Pan Jian, & Lin, 2018).
Nomenclature
Input Symbols
x1 time, (moth:day:h:min:s)
x2 latitudinal, (29 °N-31°01'N)
x3 longitudinal, (46°31'E-48°31'E)
x4 altitude, (1–16 m)
x5 wind speed, (m/s)
x6 wind direction, (º)
x7 hours of daylight, (h)
x8 hours of sunlight, (h)
Output Symbols
y1 temperature, (ºC)
y2 relative humidity, (%)
Symbols
X input variables
Y output variables
F TS consequent function
M fuzzy set
N number of rules
C calculate/calculate centroid
CMM correlation matrix memories
RH relative humidity (%)
T temperature, (ºC)
TS-FS Takagi-Sugeno fuzzy system
a parameter
b parameter
σ standard deviation
ω weight
β membership degree
Subscripts
m number of inputs
k number of outputs
i rules number
j cluster number
p predicted value
n sampling time
q initial value
TS Takagi-Sugeno
Fig. 1. Schematic diagram of the proposed model incorporated with a psychrometric model.
R.Z. Homod, et al. Sustainable Cities and Society 56 (2020) 102091
2

Accordingly, the motivation behind this paper is to predict thermal
outdoor condition spaces based on the reconstruction of the consequent
functions of the Takagi-Sugeno fuzzy system (TS-FS) (white-box) into a
black-box model, such reconstruction is done by clustering and rule
extraction using TS-FS. This structure is implemented in memory by
storage and retrieval parameters and weights into correlation matrix
memories (CMM). The CMM structure provides fast recall because time
performance is independent of the previous information stored, unlike
classical structures, where the processing time increase as the amount
of information to store increases (Aykin & Keefe, 2009). Then it im-
proves the computational of output model errors by using the Gauss-
Newton method for nonlinear regression algorithm to tune consequent
of the TS-FS model based on the clustering concept of the learning data
set. This tuning reﬂects to obtain small margin errors of output com-
putational model, because it signiﬁcantly reduced the number of rules,
accordingly reducing return times of iterations. To evaluate the energy
consumption of residential buildings, it leads to taking into account
both comfort conditions of indoor environments and outdoor climate
(Marino, Nucara, & Pietrafesa, 2012). The main objective of this model
is to investigate the energy consumption of buildings, thus, by plugging-
in the model outputs into a psychrometric process model to show the
perspective of how zone energy behaves as shown in Fig. 1. The thermal
comfort zone (TCZ) on the psychrometric chart is determined by dry
bulb temperature and relative humidity RH (Homod & Sahari, 2013;
R.Z. Homod, 2014). The overall model of Basra city thermal environ-
ment is achieved by blending of multidimensional subsystem structures.
Fig. 2. Model framework composite of the 8D multi-layered structure.
3

This paper proposes an innovative model forecasting building en-
ergy consumption depending on the zone location of the building. So,
the main contributions of this work can be summarized as follows:
1 A new hybrid forecast engine in dealing with highly nonlinear
problems is constructed by converting a TS-FS model into hybrid
layers.
2 On this base, hybrid layers are fabricated from hybrid vectors by the
clustering concept of the learning data set.
3 The fuzzy rule generates a novel hybrid vector (parameters and
weights) for each of these clusters.
4 Then adjusting these parameters and weights of layers by using the
Gauss-Newton method for nonlinear regression algorithm to in-
crease the prediction accuracy.
2. Model structure
In order to obtain good data fit the model and achieve a high degree
of accuracy for such a model characterizing nonlinearly and the in-
herent uncertainty caused by the stochastic nature of the disturbance,
the multilayer-structured memory, in essence, is employed to overcome
these challenges. In general, the concept of the modeling structure is
divided into two opposing parties’ those which support the white-box
model, i.e., mathematical (physical) models, and those which support
the black-box model, i.e., neural network and fuzzy models. In fact,
they each have a different ability to simulate thermal comfort models
which are considered a nonlinear model and affected by a large number
of variables to describe this model. The policy for improving perfor-
mance for such model type is based on first part models (white-box) by
separating it into several sub-models to alleviate the complexity of the
model (Homod et al., 2012b; Homod, 2013). In this way, the second
part models (the black-box model) to divide the large-scale projections
such as regional or local scales of climate to a sub-regional or multi-
climatic zone, actually, a large number of ensemble members are si-
mulated (Feng et al., 2015). Several architecture of the black-box group
models covering different classes of algorithms exists, each describing
their different aspect of the algorithms under considerations. In this
study, a correlation matrix memories (CMM) architecture which gives
an identical TS-FS based on storing fuzzy relations in a CMM structure,
by extracted rule sets from a training data set. Such architecture has
many advantages for large logical data model development, of which
one of the most important is the scalability. That including fitness for
hardware implementations, fast online learning, matching and handling
of missing data. Furthermore, the scalable CMM structure enables a
significantly improved representation of the observations and a faster
response in terms of training and recalling a time as compared to an-
other like k nearest neighbour (k-NN), a large margin nearest neighbour
(LMNN), condensed nearest neighbour (CNN), and so on (Zhou &
Austin, 1999). The CMM structure parameters and weight, obtained
from an optimal training data set by sampling the climate to the multi-
Fig. 3. Hybrid model simulation domain and multi-climatic zone configuration.
4

climatic zones and optimized by the Gauss-Newton method for non-
linear regression algorithm. This structure can be arranged as a layer to
compose for final 8D framework structure as shown in Fig. 2. From
Fig. 2, it is possible to envision that the model structure is expressed in
terms of space form geometry such as output space, weight memory
space, parameters memory space, and input space. The outdoor thermal
comfort demonstrated that its CMM layers of model output through
time (x1) is strongly influenced by geographic location (x2, x3), altitude
(x4), wind speed and direction (x5, x6) and hours of daylight and sun-
light (x7, x8). While in the application, the limited effect of in-
dependents atmospheric pressure and ground cover or surface albedo
on output. Thus, it is accordingly neglected to provide one of plausible
nature, to reduce computation demand for the network model. In fact,
the atmospheric pressure factor is implicitly represented by wind di-
rection and speed (x5, x6), whereas ground cover is not affected by
training and validation results since it slightly changes over time for
specific zones. So, herein, we applied the hybrid model to the data
collected from the multi-climatic zone. According to this CMM struc-
ture, it provided parameters and weights and their arguments by
computation in order to estimate the pedestrians’ thermal sensations in
terms of a zone of psychrometric thermal comfort.
Then, the hybrid model is able to identify the layer parameters and
weights of CMM structure according to the model domain and covered
area by zones configurations are given in Fig. 3, where 14 zones are
covered the majority of Basra city area (181Km2
). The outdoor thermal
comfort model is highly nonlinear, and hence a large number of the
dataset implemented to model inputs is necessary to obtain a robust
model output.
In order to explain how the algorithm extracts a specific output’s
parameters from the data structure, as shown in Fig. 2, let us first divide
the entire data into symmetric groups into finite sets. These groups
consist of a package of CMM layers, which are arranged regularly
throughout 3D space. The 8D multi-layered structure enables the al-
gorithm to specify a specific group by knowing the set of inputs x8, x7
and x6, then, x4 and x3 it will specify the package as surrounded by a
red circle as indicated in Fig. 2, whereas the hybrid layer can be spe-
cified by x5, after which the parameters can be obtained by inputs x2
and x1. Then it is possible to obtain the output from these parameters
and weights.
3. Methodology
Unlike many other atmospheric modeling aspects, it is quite limited
and specialized in particular application domains of outdoor thermal
comfort, the aim here is to enable a hybrid model to handle any dis-
turbance associated with the input vectors X(t) on the Regine conditions
that can be conjunct layers’ clustering weight with layers’ parameters.
The issues of layers’ clustering weight design can be presented in short
as follows:
3.1. Design of the layers’ clustering weight
Proposed layers’ clustering weight in this dynamical model is based
on fuzzy systems (FS) using Takagi-Sugeno (TS) Algorithm. In the lit-
erature, many different methods are used for generating TS-FS from
data. Within the approach of the layers’ clustering weight, three dif-
ferent methods exist, namely the Regularized Numerical Optimization
(RENO), Adaptive Network-based Fuzzy Inference Systems (ANFIS) and
the cluster-based generation of FS (genfs2). All three approaches are
evaluated in this study, genfs2 is demonstrated as appropriate for model
mapping in a low space structure and memory and this structure used in
nonlinear system identification. The model of the outdoor thermal
system is normally a nonlinear multi-input multi-output (MIMO)
modal, the input variables are as follows: Wind speed (Beaufort), wind
direction, hours of daylight, hours of sunlight, longitudinal, latitudinal,
altitude (0−13 m), ground cover (surface albedo). While the output
variables are outdoor temperature and RH, both variables can be re-
presented by the following equations:
Input vectors:
= ∀ = ⋯ = ∀ = ⋯X x i m y y p k[ ] 1, 2, , [ ] 1, 2,p i p,
Output vector:
=ˆy y[ ]p
The advantage of the conventional fuzzy system over the TS algo-
rithm is that it is easier for grouping the output variables into different
clusters and then the fuzzy rule generates a novel hybrid vector
(parameters and weights) for each of these clusters. These rules can be
grouped into 7 clusters as the following nonlinear mapping:
R ⋯
⋯
⋯ ⋯
=
⋯ ⋯ ⋯ = ⋯ ⋯ ⋯
⋯ =
− −
−
y M y M
y M y M y M
y M x M x M
x M Y X
F y y y x x x a b ω y F y y y
x x x a b ω y
: if is and is
and is and is and is
and is then ( )
( , , , ; , ) , ( , ,
, ; , )
i
n
i
d y n
i
d y
k
n
i
d y n
i
d y n
i
d y
k
n z
i
d y
i
d x
i
d x
m i
d x
i
i k m i i i i i
k
k
m i i i
k
i
k
TS
1
( )
2
( )
( )
1
1 ( )
2
1 ( )
( )
1
( )
2
( )
( )
1
1 2 1 2
1 1
1 2
1 2
k
k
m
1 2
1 2
1 2
(1)
where F(Y, X; ai, bi) is an arbitrary constant, linear or nonlinear func-
tion, subscript i is a rule number, subscript m is the number of input
variables, subscript k is the number of output variables, Mi is the set of
linguistic terms that are defined for an antecedent variable x, d(y1), …,
d(yk), d(x1), …, d(xm) are descriptive terms for linguistic values, such as
positive big or negative significant, ωi is the basis function, ai and bi are
the consequent parameters functions, X = [y1, y2 … yk, x1, x2 … xm]T
is
the input vector variables’ vector and n, n-1, ….,n-z are the sampling
time.
Fig. 4. Premise membership’s functions and the basis regarding clustering data of average temperature.
5

The TS-FS consequents can be represented by the basis function ωi,
which is described by normalizing these maximum fulfillment degrees
of antecedents’ rule and the output vector of the model Yi(X). The
clustering of the output vector data set can be represented regarding the
premise membership’s functions and basis, which is shown in Fig. 4.
The adapted equation model output vector of Yi(X) to efficiently fit
the data set profile. With respect to verification, the nonlinear equation
yi be required to modulate either using the manual tuning of the
equation parameters ai and bi, or implementing a Gauss-Newton algo-
rithm. The limitations of manual tuning include being time-consuming
and have to be repeated so many times to obtain a satisfying result for
the parameters, those are highly nonlinearly related. Accordingly, the
proposed algorithm (Gauss-Newton method for nonlinear regression) is
responsible for optimizing the model output vector factors and handling
of the target-specific signal output. The optimization technique is ap-
plied based on the deviation between the measured and predicted data
obtained by a training process using the set of objective criteria, which
will be analyzed briefly in Section 3.3 to understand how it has been
performed.
3.2. Identification of the hybrid layers model
Hybrid layers can be identified in the simplest way from the sys-
tematic clustering and rule extraction using the TS-FS. To begin with
identification, clustering methods are crucial for grouping input vector
parameter values to calculate centroid Ci m
j
, and standard deviation σi m
j
,
can be simply formulated as follows:
⎜ ⎟= ⎛
⎝
∗ − ⎞
⎠
σ
N X X( max ( ) min ( ))
8
i m
j j
,
(2)
The antecedents' membership functions are built on the Gaussian
distribution xM ( )m
i
m due to its smooth function and the value of degrees
of membership is determined by the following Equation:
=
⎛
⎝
⎜⎜
− ⎞
⎠
⎟⎟
ˆxM ( ) ℓm
i
m
x C
σ
1
2
|| ||
( )
p m i m
j
i m
j
, ,
,
2
(3)
Thus, structural clustering algorithm for model networks is exactly
analogous to the corresponding formula of Eq. (1). Where each center
point and qualities of a cluster are associated with the number of rules
or membership functions. The process of extracting the rules based on
antecedent’s membership function values xM ( )m
i
m , and TS-FS rule
consequents are implemented to obtain the conjunctive model output as
follows:
R
∑ ∑
⋯
⋯
⋯ ⋯
= = = = + +⋯+
+⋯+ +
− −
−
=
y M y M
y M y M y M
y M x M x M
x M
Y X Y X ω y y f b a x a x
a x a
: if is and is
and is
then ( ) ( ) ,
i
n
i
d y n
i
d y
k
n
i
d y n
i
d y n
i
d y
k
n z
i
d y
i
d x
i
d x
m i
d x
j
i
i
N
i i i ij j
im m i
TS
1
( )
2
( )
( )
1
1 ( )
2
1 ( )
( )
1
( )
2
( )
( )
1
1 1 1 1 1
0
k
k
m
1 2
1 2
1 2
(4)
where the subscript j stands for the cluster number
With a focus on applying fuzzy rule ith
in Eq. (4) to understand how
to calculate the cluster's value (in the consequent part) for each input
attributes (in the antecedent part). Let us clarify it by using three
Gaussian membership functions (N = Negative, Z = Zero, and P =
Positive) for each of ten input items that are organized by universes of
discourse. Where the input vector X can be described by y1 is the out-
door temperature, y2 is the outdoor RH, x1 is the time, x2 is the lati-
tudinal, x3 is the longitudinal, x4 is the altitude, x5 is the wind speed, x6
is the wind direction, x7 is the hours of daylight, x8 is the hours of
sunlight. Accordingly, since rule consequent takes a singleton (crisp)
value that leads to easily interpretable output, which can be easily
expressed by the fuzzy rule ith
as follows:
R −
− ⋯
Tem n RH n
time n x n
: if . ( 1) is(N, Z, P) and (
1) is(N, Z, P) and ( ) is(N, Z, P) and ( ) is(N, Z, P)
i
TS
8 (5)
= + +⋯+ +⋯+ +Y X ω b a x a x a x athen ( ) ( )i
n
i i ij j im m i1 1 1 0
The inference of singleton output membership functions for equn.
(5) can be demonstrated by a diagram in the form of a fuzzy rule where
each rule generates two values (ω y,i i), as clarified in Fig. 5, which
shows an efficient process of defuzzification by using the Center of
Singletons (CoS) algorithm.
From Eq. (5), the CoS method with fuzzy consequences is used to
defuzzified output vector model into a crisp numeric value, as it is clear
from the following Equation.
=
∑
∑
=
=
Y X
β u y
β j u
( )
( )
( )
i
N
i i
i
N
i
1
1 (6)
Fig. 5. Describes each rule in a TS-FS model generates two values of (ω y,i i).
6

where N stands for hesitant fuzzy linguistic term sets (HFLTSs) and
β u( )i is for the jth
component in current data vector, which is reflecting
the value of the jth
channel and can be presented in Eq. (7).
= ∧ ∧ ⋯ ∧ ≤ ≤ˆ ˆ ˆβ u μM x μM x μM x i N( ) ( ) ( ) ( ), 1i i
d x
i
d x
i
d x
m
( )
1
( 2)
2
( )m1
(7)
According to the Eq. (6) developed by (Friedman, 1991), which is
the output vector of a fuzzy singleton model belonging to a general class
of the universal model output vector, normalizing degrees of rule
fulfillment by using the product t-norm, yielding the weight, that can be
obtained as follows:
∑=
∑
∑
==
= =
Y X
β u y
β j u
ω y( )
( )
( )
i
N
i i
i
N
i i
N
i i
1
1 1 (8)
where =
∑ =
ω i
β u
β u
( )
( )
i
i
N
i1
, = ∏ =
β u β j u( ) ( )i j
m
i j1
In respect to the proposed models with singleton output function in
Eq. (6), it can be considered equivalent to be a nonlinear equation. The
output vector model equation which expresses parameters and weights
relation can be given as follows:
∑= −
=
−Y X ω a( ) (1 ℓ )
i
N
i i
b x
1
i
(9)
The weights (ωi) and parameters (ai and bi) in Eq. (9) can be de-
termined by calibrating them from the consequent and antecedent
space in Eq. (1) by comparing with measured data collected from the
zones. The rational coefficients are easier to systemize as two types of
weights and layer parameters in the memory space. The weights and
parameters in this structure are correlated to the input vector of the
model (x1, x2, …, x8), which depends on the structure of a system dy-
namics model relation. Also, be viewed through a crossover from 8D
toward 2D layer structure as shown in Fig. 6, where the values of
parameters ai with respect to variation in inputs x1 and x2 into a layer
while holding the other inputs fixed.
The network of the hybrid model can be designed using an in-
tegrated spatial framework for model structure development. This can
be achieved by arranging all the weights and layers into two separate
spaces for the implementation of online and offline learning algorithms
strategies. The illustration of each operating input-transformation-
output process link toward the network is shown in Fig. 2. The first
layers (layers' parameters) of the network are formulated by following
the conventional training strategy for the data set, whereas the strength
training weights of the neural network can be identified by mutating
the basis function of clustering information. The two types of layers,
parameters, and weights are tuned by the Gauss-Newton method for
nonlinear regression algorithm (Homod, Sahari, Almurib, & Nagi, 2012;
Homod, Sahari, Almurib, & Nagi, 2014). The scheme of model con-
struction consists of input space, memory space for model parameters,
the weights of the visual memory space and the output space. From
visual mapping of data from an input space X into an output space Y,
and it is evident from Fig. 2 that the model is a typical nonlinear multi-
input multi-output (MIMO) signals, where the input vector are (x1, x2,
…, x8), and the output is outdoor temperature and RH. The concept of a
hybrid model, block diagrams, architectural model, and a computa-
tional model is represented as a networked memory structure as shown
in Figs. 2 and 6. This approach is attractive because it is suitable for
storing fuzzy relations into the CMM structure.
3.3. Learning of a hybrid model of layers
The input/output vector data set values derived from the field
measurement of the Basra city is represented by several hyper-ellip-
soidal clusters, and merging them into seven groups, as demonstrated in
Fig. 4. The integrated hybrid layers model output vector is carried out
primarily by an identified set of TS-FS, as in the following.
R ⋯
⋯
⋯ ⋯
− −
−
y M y M
y M y M y M
y M x M x M
x M
: if is and is
and is and is is
and is
i
n
i
d y n
i
d y
k
n
i
d y n
i
d y n
i
d y
k
n z
i
d y
i
d x
i
d x
m i
d x
TSK
1
( )
2
( )
( )
1
1 ( )
2
1 ( )
( )
1
( )
2
( )
( )
k
k
m
1 2
1 2
1 2
(10)
∑= −
=
−Y X ω athen ( ) (1 ℓ )
i
N
i i
b x
1
i
In mathematics, the output rule function Ri
TSK
called a piecewise or
a hybrid function, which are possible to subdivide into small para-
boloidal segments obtained by Y(X), where it is related to the location
of cluster centres. Thus, running the TS-FS algorithm to generate a set of
clusters to get the model structure, and afterward needs to compare its
output vector with real-time system output. Then, model error analysis
is investigated before conducting the task learning for the hybrid
model. The main goal of the learning model layers is intended to obtain
model target values to perfectly fit real data by reducing its error. Due
to this, the TS-FS model error can be calculated as a measure of model
precision; it can be done simply by following these steps:
∑= ⋅
=
ˆy ω f
i
N
i i
1 (11)
= ⋯ ⋯ ⋯ ⋯ ⋯ ⋯ ⋯ ⋯G ω x ω x ω x ω ω x ω x ω x ω ω x ω x
ω x ω
[
]
j m i i j i m i r r j
r m r
1 1 1 1 1 1 1
(12)
Fig. 6. Specify the network parameter values of ai with respect to x1 and x2.
7

Table 1
Model input parameters for zones location information.
Case Location Name Zone Latitude Longitude Altitude Wind speed Wind direction Daylight Sunlight
Training Al Qurnah 1 ° ′
31 01 ° ′
47 25 4m 1−31 km/h NE, SE, ESE 11.81-13.96 h 10.5–14.3 h
Ad Dayr 2 ° ′
30 47 ° ′
47 34 4m 0−28 km/h SE, NE, NNE, 11.98-13.88 h 10.5–14.2 h
AlAmn AlDakhili 3 ° ′
30 28 ° ′
47 46 6m 0.5−34 km/h SSE, SE, ESE 10.11-14.06 h 10.4-14.2 h
Al Rumaila 4 ° ′
30 34 ° ′
47 19 8m 3−46 km/h SE, ESE, SSE 10.91-14.02 h 10.6–14.3 h
Zubayr 5 ° ′
30 12 ° ′
47 42 13 m 2−44 km/h NE, N, WSW 11.73-13.94 h 10.4–14.4 h
Basrah AlQadimah 6 ° ′
30 30 ° ′
47 49 4m 0−23 km/h NE, SE, ESE 10.63-14.06 h 10.3–14.2 h
Shatt Al-Arab 7 ° ′
30 44 ° ′
47 50 2m 0−27 km/h NNE, SE, SSE 10.11-14.06 h 10.4–14.3 h
Um Qasr 8 ° ′
30 02 ° ′
47 55 9m 1−33 km/h NE, SE, ESE 11.05-14.03 h 10.5–14.3 h
Faw 9 ° ′
29 58 ° ′
48 11 2m 0−32 km/h SSE, SE, ENE 12.02-13.45 h 10.4–14.3 h
Validation Al Madina 1 ° ′
31 06 ° ′
47 15 3m 1−32 km/h ENE, N, SSE 10.11-14.06 h 10.6-14.4 h
Al-Hartha 2 ° ′
30 41 ° ′
47 44 4m 0−28 km/h SSW, SE, NE 10.74-14.01 h 10.5–14.2 h
Shu'aiba 3 ° ′
30 25 ° ′
47 40 13 m 2−37 km/h NNE, NE, SSE 10.16-14.04 h 10.4–14.3 h
Khor Al Zubair 4 ° ′
30 13 ° ′
47 46 16m 0−34 km/h WNW, N, SE 10.68-13.37 h 10.4–14.3 h
Abu Al Khasib 5 ° ′
30 19 ° ′
48 02 3m 0−22 km/h SSE, SE, ESE 11.01-13.86 h 10.5–14.3 h
Fig. 7. Comparison process between measured and simulated values of the outdoor av. temperature (°C) and RH (%) for training zone 1.
Fig. 8. Comparison process between measured and simulated values of the outdoor av. temperature (°C) and RH (%) for training zone 2.
8

= ⋯ ⋯ ⋯ ⋯ ⋯ ⋯P a a a a a a a a a a a a[ | | ]i j m i i j i m i r i j r m m
T
1 1 1 1 0 1 0 1 0(13)
= ⋅ˆy G P (14)
= − ⋅e y G P (15)
The objective of the learning algorithm by Gauss-Newton method
for nonlinear regression is to bind the singleton output vector of model
Y(X) variations as tightly as possible to form the hyper-ellipsoidal
generated by an algorithm, thus, via optimizing value for individual
outdoor situations for ωi, ai and bi in Eq. (10). The Gauss-Newton
method for nonlinear regression algorithm is based on determining the
values of the model output parameters that will provide tight con-
vergence to observations from the field measurement by an iteration
manner. By implementing the Gauss-Newton algorithm, the con-
sequential tuning method of the nonlinear output model is used with
the observational data obtained from the field measurement. The pur-
pose of this subsection is to describe the numerical approach in a step
by step manner for offline learning techniques to demonstrate how it is
done, and also to illustrate the correlate in the nonlinear equation and
the observation data set, which can be calculated using the following
equation based on error Eq. (15).
= ⋅ +y G P e (16)
= +y ω f x a b e( ; , )i i i i (17)
where yi stands for the dependent variable obtained from the field
measurement, ω f x a b( ; , )i i stands for the simulated or predict the
model output vector that is affected by the independent variable that is
highly correlated with xi and the nonlinear relationship between
parameters a and b, which are represented as a networked memory
structure, and ei is a residual random error for individual ˆyi and mea-
surement yi. The expanded form of nonlinear integrated hybrid layers of
the model is represented by a Taylor series around the parameter op-
timal values and truncated after the first derivative as follows: the
Fig. 9. The maximum absolute error between measured and simulated values of the outdoor temperature (°C) and RH (%) for training zone 1.
Fig. 10. The maximum absolute error between measured and simulated values of the outdoor temperature (°C) and RH (%) for training zone 2.
9

Taylor series expansion close to the cluster centre point-position
ω a b( , , )i regarding three variables to represent nonlinear integrated
hybrid layers of the model. Hence it can be performed numerically in
the construction of a partial derivative with respect to sampling time
intervals as follows:
= +
∂
∂
+
∂
∂
+
∂
∂
+f x f x
f x
a
Δa
f x
b
Δb
f x
ω
Δω( ) ( )
( ) ( ) ( )
i q i q
i q i q i q
1
(18)
where q stands for the initial start value and the time period, q+1
stands for the predicted value, = −+Δa a aq q1 , = −+Δω ω ωq q1 and
= −+Δb b bq q1 .
The Eq. (18) is subtracted from Eq. (17), yielding the following
predicted Equation:
− =
∂
∂
+
∂
∂
+
∂
∂
+y f x
f x
a
Δa
f x
b
Δb
f x
ω
Δω e( )
( ) ( ) ( )
i i q
i q i q i q
i
(19)
As pointed out by previous works (Homod, Abood, Shrama, &
Alshara, 2019), a popular standard for storing data in computers is
often in a matrix form so that matrix methods are used intensively to
provide fast access to specific digital data. Thus, the predicted Equation
can be represented by using a matrix symbol coding scheme as follows:
= +D Z ΔA E{ } [ ]{ } { }q (20)
where vector {D} measures the difference between the real values de-
pending on the data set and the function values, [Zq] stands for the
partial derivatives in matrix form, this structure containing all differ-
ence of the function that is evaluated at the initial guess q, {E} is for a
residual random error vector, which is reduced and eliminated through
the iteration scheme and {ΔA} is for the adjusted parameters in vector
form to get the optimized value by Δω, Δa and Δb.
The following normal equation is obtained by applying the linear
least-squares regression of the error theory to Eq. (20):
=ΔA
Z Z
Z D
1
[ ] [ ]
{[ ] }
q
T
q
q
T
(21)
As a final point, many numerical methods exist for solving para-
meter values of the vector {ΔA} in Eq. (21), such as implementing the
recursive Gauss-Newton method for nonlinear regression algorithm to
estimate {ΔA}, it is recommended to maintain the initial estimates be a
sufficiently narrow margin of error for the optimal parameter estimates
or the iterative process takes a long time to converge to obtain accurate
parameters value (Homod et al., 2019).
Fig. 11. Comparison process between measured and simulated values of the outdoor av. temperature and RH for validation zone 1.
Fig. 12. Comparison process between measured and simulated values of the outdoor av. temperature and RH for validation zone 2.
10

4. Related assessment tools
This section summarises two indicator tools used to evaluate the
proposed model performance, and thus used as the benchmarks for
analysis, interpretation, presentation, and organization of numerical
facts or data collected systematically, which are as the following:
4.1. Statistical indices
To have a clearer assessment of the performance of the proposed
model against the real-time data set, this study has evaluated the model
according to criterion for statistical indices such as the Maximum ab-
solute error (MxAE),), root mean squared error (RMSE), mean absolute
error (MAE), relative absolute error (RAE) and coefficient of determi-
nation ( r2
), and these indices are defined as:
• Sum of Squares due to Error (SSE);
∑= −
=
SSE y yp( )
i
n
i i
1
2
(22)
• Sum of Squares due to Regression (SSR);
∑= −
=
SSR yp y( )
i
n
i
1
2
(23)
• Total Sum of Squares (SST);
∑= −
=
SST y y( )
i
n
i
1
2
(24)
• Maximum absolute error;
= −Max AE y yp. max | |i i (25)
• Root mean squared error;
=
∑ −
−
=
∑
−
=
−
= =
RMSE
y yp
N
ε
N
SSE
N
( )
2
ˆ
2 2
i
N
i i i
N
1
2
1
2
(26)
• Mean absolute error;
∑= −
=
MeanAE
N
y yp
1
| |
i
N
i i
1 (27)
• Relative absolute error;
=
∑ −
∑ −
=
=
RAE
Y Yp
Y Y
| |
| |
i
n
i i
i
n
i
1
1 (28)
• Coefficient of determination;
= = −r
SSR
SST
SSE
SST
12
(29)
where yi yi are the set of field measurement data, ypi are the set of model
outputs, y p y, are the set of the mean value of all real records points
and N is the number of testing samples.
4.2. Effective temperature index
Thermal sensation and comfort of human being is the condition of
mind that expresses comfortable status with the thermal environment
and can be assessed by many tools, such as effective temperature index
(Farajzadeh, Saligheh, Alijani, & Matzarakis, 2015). The range value of
effective temperature index is from -20 to +30 °C, where a very cold is
ranged (-20 to -10 °C), the cold is ranged (-10 to 1.67 °C), the very cool
is ranged (1.67–15.5 °C), the cool with comfort is ranged
Table2
Outlineoftrainingandvalidationresults.
MxAERMSEMAERAEr2
CaseZoneRHAv.TmMn.TmMx.TmRHAv.TmMn.TmMx.TmRHAv.TmMn.TmMx.TmRHAv.TmMn.TmMx.TmRHAv.TmMn.TmMx.Tm
Training10.00210.08750.08480.09090.00090.04430.04460.04520.00070.03740.03810.03850.01050.00530.00600.00500.99880.99940.99920.9992
20.00220.08840.08530.09170.00090.04560.04430.04450.00070.03900.03780.03790.00710.00530.00590.00460.99850.99950.99920.9992
30.00220.08790.08480.09010.00090.04360.04380.04510.00070.03720.03720.03830.00700.00520.00580.00490.99840.99910.99960.9989
40.00210.08790.08480.09160.00090.04480.04430.04590.00070.03820.03800.03880.01040.00510.00580.00460.99830.99920.99920.9991
50.00210.08770.08540.09160.00090.04370.04420.04580.00080.03720.03760.03910.01050.00510.00580.00470.99930.99920.99890.9994
60.00220.08810.08420.09090.00090.04540.04400.04380.00080.03880.03770.03720.00710.00550.00590.00480.99870.99910.99930.9993
70.00220.08750.08480.09090.00090.04400.04430.04500.00070.03770.03770.03800.00820.00540.00590.00500.99860.99920.99920.9992
80.00220.08840.08550.09170.00100.04490.04220.04530.00080.03830.03610.03810.01140.00510.00550.00450.99930.99920.99910.9992
90.00210.08860.08530.08940.00100.04400.04350.04480.00080.03760.03730.03810.01250.00510.00580.00460.99860.99920.99910.9990
Validation10.00260.10620.10160.10880.00120.04680.04570.04790.00100.03960.03900.04020.01330.00540.00600.00500.99920.99910.99910.9991
20.00270.10700.10360.10770.00110.04900.04740.04850.00090.04140.04030.04060.00950.00580.00630.00510.99890.99880.99900.9993
30.00270.10670.10410.11010.00120.04870.04790.04850.00100.04070.04060.04060.01130.00560.00630.00500.99890.99910.99940.9992
40.00270.10420.10410.10910.00120.04700.04510.04680.00100.03970.03800.03930.01360.00540.00590.00470.99850.99900.99960.9993
50.00280.10560.10350.10990.00120.04760.04630.04920.00100.04020.03940.04080.01160.00560.00610.00510.99880.99920.99910.9991
11

(15.5–17.8 °C), the comfort is ranged (17.8–22.2 °C), the warm with
comfort is ranged (22.2–25.6 °C), the very warm is ranged
(25.6–27.5 °C), the sultry is ranged (27.5–30 °C). The effective tem-
perature can be determined by a linear empirical equation as presented
in (Chowdhury, Rasul, & Khan, 2008) by:
= − − × ⎛
⎝
− ⎛
⎝
⎞
⎠
⎞
⎠
ET T T
RH
0.4( 10) 1
100 (30)
where ET is effective temperature, T is temperature and RH is relative
humidity
5. Simulation results and discussion
5.1. Physical and theoretical model description
Following an implementation evaluation to ensure the computa-
tional process modeling is being implemented as designed; the next step
is the real point of collecting dataset. 14 zones of measurements and
field observations are scattered to large modeling domains, replicated
instruments setup for each zone in Basra, the largest city in southern
Iraq. Here, the quantitative and qualitative methods were used in col-
lecting information and data for conducting training needs assessments
as well as validation needs. Table 1 outlines inputs that model needs for
zone locations information, in addition, it requires further input para-
meters, such as time (in seconds, minutes, hours, days, weeks and
months), wind speed in m/s, wind direction in (°), hours of daylight in
hours, and hours of sunlight in hours. However, excluding atmospheric
pressure and ground cover from collecting datasets to reduce compu-
tation demand for network model; that is because outdoor temperature
and RH had no evident effects on atmospheric pressure fluctuation.
Moreover, atmospheric pressure is implicitly represented by wind di-
rection and speed or Beaufort (x5, x6); whereas ground cover or surface
albedo is not affected by training and validation results since it slightly
changes over time for specific zones (there is no greenery and
Fig. 13. The maximum absolute error between measured and simulated values of the outdoor temperature and RH for validation zone 1.
Fig. 14. The maximum absolute error between measured and simulated values of the outdoor temperature and RH for validation zone 2.
12

Fig. 15. Outdoor temperature (°C) response for the hybrid model due to variation in time and zone location.
Fig. 16. Daily loop cycle for instantaneous temperature (°C) for one-year period and zoom in on a typical summer day.
Fig. 17. The proﬁle of outdoor condition given in terms of its thermodynamic properties by Psychrometric chart for one-year period.
13

vegetation, to be changed). Fig. 3 shows zones in the Basra city where
the sample’s data set are collected for each zone. The routines collection
or measurement of data set are obtained simultaneously for both
training and validation zones by a wireless data logger during the
period from 2014 to 2016.
5.2. Model training
The entire set of field measurement data of 9 training zones is di-
vided into two subsets; the actual training subset, and the test subset,
where each subset represents one year of daily data records. The test
subset is not involved in the learning phase of the model and it is used
to evaluate the performance of the structure model. Based on field
Fig. 18. The behave of the daily effective temperature cycle of air outdoor condition, top halve shows full-year cycle, and the bottom one shows the diurnal period.
Fig. 19. The energy consumption results based on the heating/cooling coil load variation for zone 1 and 8.
Fig. 20. A comparison of energy consumption results based on the heating/cooling coil load variation between RLF and proposed model.
14

measurement data for the training subset (4,822,416 samples) of 10
variables, the relationship between model output and inputs data set is
trained. Through applying a training subset of data for each of the zone,
hence, the real-time implementation computational cost reduction due
to one of the advantages that the proposed technique offered. That is
because the proposed method needs a smaller number of iterations to
achieve the training/learning process, which is implemented using the
Gauss-Newton algorithm to tune model weights and parameters for the
fitting of nonlinear regression clustering that combine model based on
training subset data. The test subset data for the remaining year
(2015–2016) go into the input of the model to start running it and the
output set are recorded. To expose the performance of the outdoor
temperature (°C) and RH (%), the route of them within one year com-
pared with field measurement data are plotted for the zones 1 and 2 as
shown in Figs. 7, 8. Appendix A includes supplementary Figures (zones
3–9) to illustrate testing results that supplement extra information
about model performance.
As can be seen from the two Figs. (7,8), the design of CMM structure
and the implementation of the Gauss-Newton method for nonlinear
regression algorithm to tune model parameters and weighs illustrated
considerable performance. Here, the maximum absolute error (MxAE),
root mean squared error (RMSE), mean absolute error (MAE), relative
absolute error (RAE) and coefficient of determination ( r2
) between the
values of measured data for the average temperature and RH and the
values obtained from the hybrid model output were 8.75 *10−2
, 2.1
*10-3
, 4.43*10−2
, 9*10-4
, 3.74*10−2
, 7*10-4
, 5.3*10-3
, 1*10−2
, 0.9988
and 0.9994 respectively for the zone 1. For better clarity, Figs. 9, 10
show the absolute error of the hybrid model in comparison to ob-
servation data for the zones 1 and 2. Appendix A also presented the rest
Figures (zones 3–9) to show model performance.
5.3. Model validation
In addition to dataset required as model training, data are also re-
quired to validate model output vectors, but the model performance can
be characterized by data that were not used in model training and
testing. The system for real-time data set collection is timely im-
plemented within 2015–2016 for 5 widespread zones. In order to
achieve confidence for the margin error obtained, the number of vali-
dation samples that are required to carry out the margin error assess-
ment needs to be equal to or less than the training data set. For this
purpose, 2,679,120 could be a proper number of samples per year for 5
zones. After the model has been identified by training and testing
processes, the validation dataset is supplied to the inputs model to start
running the model. Since the validation was based on the model do-
main, the applied validation samples were calculated for each output
sample according to the accuracy that formed the model. The results of
a test validation show lower model variance and tighter targets are
feasible as shown in Figs. 11, 12 for the validation zones 1 and 2. Ap-
pendix A as well includes additional Figures (zones 3–5) to illustrate
validation results that supplement extra information about model per-
formance.
Then, the average values for temperature and RH are calculated
according to the statistical calculation of the model results, a difference
between it and actual observation values are the margin of maximum
absolute error does not exceed 0.107 °C for temperature and 2.8 10−3
%
for RH, as shown in Table 2. Then, the hybrid model is to be approved
to the traditional models according to literature, where the proposed
model can identify forecasting outdoor condition more accurately than
the former structure model techniques. As a result of the margin of
errors for the hybrid model are less than other methods such as regional
atmospheric modeling system (RAMS) and the climate version of RAMS
(ClimRAMS). For instance, the proposed model error is much less than
the ClimRAMS, which presented by Liston et al. (Liston & Pielke, 2001)
where the maximum absolute error temperature was 1.7 °C. As well, the
model's performance can be evaluated by varying many climate zones
to validate, it would be useful to change the model domain, and even so
similar results have been observed in the output errors of the model as
shown in Figs. 13 and 14 for validation zones 1 and 2.
In order to confirm that the set of independent variables that affect
an outdoor condition’s model domain has varied to distinguish model
responses, the computation of model output is implemented using only
the most vital variables affecting output. For this purpose, time
(months) and location (zones) are specified as a dataset to model inputs
due to significant influence on the model output, while holding the
other inputs fixed as illustrated in Fig. 15.
The model structure achieved reliable high performance to accu-
rately select the tuned parameters and weights of two different ma-
trices, which vastly distributed storage resources across the network
structure. The instantaneous temperature varies so strongly over a one-
year range that it would be the appropriate validation test to the pro-
posed structure of the CMM approach, such a test has proved the
model’s ability to simulate more complicated scenarios, like outdoor
instantaneous temperature. The daily loop cycle for instantaneous
temperature for periods up to one year is executed with the greatest
exactness and response fidelity, as evident in output simulation in
Fig. 21. A comparison of the results of the power consumption between zone 1 and 8 for two models.
15

Fig. 16. To demonstrate the margin error of the model, the typical
summer day is magnified as shown in Fig. 16.
5.4. Psychrometric chart and effective temperature analysis for model
output
Eventually, this model can then be used as a criterion for any cli-
matic zone anywhere in the city area to check out whether any zone
conserves energy or not. The study aims to provide an initial energy
assessment for various climate zones by using an integrated proposed
model with the psychrometric process model, and thus to inform de-
cision-making and policy formulation toward improving the sustain-
ability of planning the city. On the basis of outdoor thermal comfort, it
also demonstrates that the incorporation of the Psychrometric chart
model can significantly change the outcome of conservation of energy
and planning city. To integrate the proposed model with the
Psychrometric chart, which is used to analyze the data acquired from
model simulated profiles to represent the change in thermal char-
acteristics and properties of the outdoor air state. According to ASHRAE
Standard 55-04 (ASHRAE, ANSI/ ASHRAE standard 55, 2004) and ISO-
7730 (ISO 7730, 2005), they present the concept of comfort zone based
on the Psychrometric chart and operative temperature, then describe
independent variables and then validated it in the test set (Homod &
Sahari, 2014; Homod, Sahari, Almurib et al., 2014). The comfort zone is
constructed by using the principle of the thermal comfort region. As
well, one of the methods usually used for assessing the human thermal
comfort is an effective temperature index, which is a dependent vari-
able in Eqn. (27). From Eqn. (27), to obtain effective temperature
supplying it by model output. In this work, the investigation of outdoor
thermal condition for two different zones is carried out by using the
Psychrometric chart and effective temperature as tools for predicting
the difference. This runs for one year then magnified and focuses on a
typical summer day in order to illustrate the behavior of both simulated
and observed climate data to check to match them as shown in Figs. 17
and 18. As can be seen from the two figures, the results are almost
identical with each other, where both show that the outdoor conditions
are passing through the comfort zone in some period, where at that
time negates the need to use an HVAC system. Both analysis methods
demonstrate how different zone weather can work with relative energy
change to extend the experience and use of outdoor conditions. Ac-
cording to the assessment of the hybrid model is now used primarily as
a criterion for evaluating the climatic zones in terms of energy saving,
as well as used for forecasting temperature and RH.
5.5. Energy analysis for model output
From the previous subsection (5.4.), both Figs. (), the best one of the
two zones for energy saving is the longest length of process line crosses
in the comfort zone, because the process line length is reflecting time
factor and the energy saving (Homod, Sahari, Almurib et al., 2014).
Based on this as a criterion for evaluating energy saving, this indicates
that zone No. 1 is more suitable than zone No. 8 for energy saving. That
reasonable and ecological meaningful, which meets the nature of
southern zones, where it is warm, humid sea atmosphere which so
greatly influences the climate of the Persian Gulf, unlike northern
zones, has a huge potential in terms of energy saving. Although, both
methods, the Psychrometric chart, and effective temperature are iden-
tical to analyze climatic zones in terms of energy saving, using a Psy-
chrometric chart is much easier to distinguish the climatic region pos-
sessing the desired requirements.
The outdoor conditions have a significant impact on energy use in
HVAC systems. The proposed model adds together all of the inputs
(outdoor conditions) and calculates outputs (temperature and RH), then
they passed on to the Psychrometric model. By these outputs, the
Psychrometric model can calculate the amount of energy per kg of dry
air of the outdoor air condition. The Psychrometric model calculation
depicts the difference in the kilojoules per kilogram of dry air re-
presented as kJ/kg of heating/cooling and dehumidifying coil load for
the HVAC systems as shown in Fig. 19. From Fig. 19, it is important to
distinguish between cooling and heating loads, where cooling loads are
much greater than the heating loads due to the dehumidification as-
sociated with the cooling process that relatively high energy required to
remove the water vapor from the processed air. Therefore, when the
zone is located nearby the sea, it is strongly influenced by the maritime
climate and consumes more energy in HVAC systems such as zone 8 in
Fig. 19.
5.6. Comparison: proposed and RLF model
A comparison of the proposed model with diff ;erent strategies is
required since only validation tests are performed and verified; ac-
cordingly, a comparison of the proposed model to model identified in
earlier studies is needed to investigate its performance. There are nu-
merous models to forecast the cooling/heating load of the buildings, as
it was previously mentioned. However, the model of residential load
factor (RLF) was accepted to be the closest one to forecast the cooling
and heating load requirements of residential buildings. The RLF is
modeled by many features to estimate cooling and heating load like
internal gains, furniture, building’s structure, occupants, lighting, out-
door air (infiltration and ventilation) and solar radiation incident of the
building’s envelope, further, it depends upon outdoor/indoor tem-
peratures and so on. The two models are run under the same zone
conditions to conduct a fair comparison to evaluate the performance of
the proposed model in different zones. The comparing result along with
the cooling/heating load between the proposed and RLF model revealed
a slight difference due to different strategies used for each model that
lead to different results as shown in Fig. 20. When looking closely at
parts of the heating load, it is obviously the RLF model exhibited the
lowest heating load than the proposed model due to the RLF method
considering the effect of internal heat gains, whereas the two features of
building’s structure and internal heat gains are characterized by mutual
interaction and reciprocal influence, which can be seen clearly from the
trace of the curve of cooling loads. Furthermore, from a comparison of
two diff ;erent strategies for forecasting of cooling/heating load of re-
sidential buildings, the proposed model response to outdoor variation is
more sensitive than the RLF model, as evident from the smooth curve of
the RLF model and the rough curve of the proposed model. Regarding
the analysis of the Figs. 19 and 20, one can conclude that the power
requirement of an HVAC system is largely derived from the relevance
heating/cooling coil load mapping as shown in Fig. 21. The evidence
from the findings of this Figure indicates that different zone locations
show savings of more than 50 % of HVAC system power. The selected
two or more zones to be tested by the proposed model under con-
sideration for the relevance of the location are influenced by the climate
sea.
6. Conclusion
The hybrid multi-layer technique is adopted by the current study to
overcome a challenge at building's load forecasting, and its simulation
results yield promising results that indicate the great potential and
feasibility to identify the nonlinear system. Validation results have
pointed out that the model characterized by a small uncertainty has
yielded a good data fit and achieved a high degree of R2
which is no less
than 0.9988 for temperature and 0.9985 for RH. From the results of this
study, it is possible to draw general conclusions. Thus, the hybrid model
identification of outdoor thermal comfort has been developed and is
used to simulate diurnal, seasonal, annual, and synoptic cycles of
temperatures and RH variables based on the interactions within Basra
city climate. The full-year integrated hybrid model has been developed
and validated against both numerical simulation results and field ob-
servations, these tests are examined in a broad range against
16

instantaneous and monthly mean, maximum and minimum tempera-
ture and RH based on diurnal cycle variation. Prior to analyzing the
validation results, an important finding of this study must be noted was
that simulation results cannot depend on the CMM structures alone in
spite of its effect on improving model performance, because there are
many other factors that affect the fit with observations. The most no-
table factors used optimization algorithm (Gauss-Newton method),
combine parameters and weight layers in a single structure and speci-
fied model inputs based on the ranking of the most effective variables
on output, evident from Figs. 7–14. Thus, the compatible model of
CMM structures is turned out by the Gauss-Newton algorithm to obtain
the best fit with observations and field measurements. The investigation
of compatible model structures in the Basra city is not just important to
the outdoor thermal comfort, but also various energy zones have been
appointed and estimated energy consumption in buildings. Part of the
aim of this paper is to classify the energy saving of climate zones based
on outdoor conditions, by using statistical indices to interpret simula-
tion model results, where it was gratifying as shown in Table 2. The
classification result for climatic zones was reasonable and ecologically
meaningful and reflects the nature of southern zones, where it is hot
and humid climates.
7. The future of work
The main recommendations for the future work could be extending
this work for the full-year multiple-Regine or global-scale climate
model. Thus, based on RAMS, nine-dimensional model structure as-
similation (9DMSA), expand the existing model packages to work in
progress. 9DMSA involves the effective integration of multi-regional-
dependent observational data into a predictive model. This can be done
based on the RAMS by adjoining multi-regional model and run as in
network model structure. Furthermore, on the basis of an initial as-
sessment of climatic zone energy, it also demonstrates that the in-
corporation of building thermal insulation can significantly change the
outcome of planning the city.
Declaration of Competing Interest
None.
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:https://doi.org/10.1016/j.scs.2020.102091.
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