Energy Demand Forecasting Using Fused Machine Learning Approaches

Abstract

The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission , and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to feature-based data provided a better result. Lastly, decision-based fusion applied to both datasets to predict power consumption through smart meters and get better results than previous techniques. The established model smart meter with automatic load control increases the effectiveness of energy management. The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which is better than the existing approaches.

Full text

Energy Demand Forecasting Using Fused Machine Learning Approaches
Taher M. Ghazal
1,2
, Sajida Noreen
3
, Raed A. Said
4
, Muhammad Adnan Khan
5
,*
, Shahan
Yamin Siddiqui
3,6
, Sagheer Abbas
3
, Shabib Aftab
3
and Munir Ahmad
3
1
Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebansaan Malaysia (UKM), Bangi, 43600,
Selangor, Malaysia
2
School of Information Technology, Skyline University College, University City Sharjah, Sharjah, 1797, UAE
3
School of Computer Science, NCBA&E, Lahore, 54000, Pakistan
4
Canadian University Dubai, Dubai, UAE
5
Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, Korea
6
School of Computer Science, Minhaj University Lahore, Lahore, 54000, Pakistan
*Corresponding Author: Muhammad Adnan Khan. Email: adnan@gachon.ac.kr
Received: 21 April 2021; Accepted: 04 June 2021
Abstract: The usage of IoT-based smart meter in electric power consumption
shows a significant role in helping the users to manage and control their electric
power consumption. It produces smooth communication to build equitable elec-
tric power distribution for users and improved management of the entire electric
system for providers. Machine learning predicting algorithms have been worked
to apply the electric efficiency and response of progressive energy creation, trans-
mission, and consumption. In the proposed model, an IoT-based smart meter uses
a support vector machine and deep extreme machine learning techniques for pro-
fessional energy management. A deep extreme machine learning approach applied
to feature-based data provided a better result. Lastly, decision-based fusion
applied to both datasets to predict power consumption through smart meters
and get better results than previous techniques. The established model smart meter
with automatic load control increases the effectiveness of energy management.
The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy
consumption with a smart meter which is better than the existing approaches.
Keywords: Feature fusion; deep extreme learning; SVM; decision-based fusion;
smart meters; energy; EDF-FMLA
1 Introduction
Recently, the application of a keen meter for controlling and overseeing electric force utilization is one of
the advances that help both clients just as electric forces and providers. It is expected that 70% of the
universe’s populace, more than 6 billion, will live in urban communities and encompass districts by 2040,
so urban communities should be savvy. IoT is the interconnection of different incorporated processing
gadgets on the Internet, which allow them to speak with one another. This improves the personal
satisfaction of the end client. The expansion in the Internet of things (IoT) has reached out in the local
This work is licensed under a Creative Commons Attribution 4.0 International License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
Intelligent Automation & Soft Computing
DOI:10.32604/iasc.2022.019658
Article
ech
T
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applications and the day-by-day activities [1–3]. This idea of IoT in homes is recommended to screen and
spare vitality while coming to and keeping up a specific degree of comfort. The standard existing home
automation systems practice basic technology such as Bluetooth, Zigbee, wi-fi, Arduino, gsm, etc. Each
technology has some benefits but weaknesses also. Research should be ended to lessen and identify their
disadvantages [4].
The IoT application has become this standard in this 21st century due to the prevalent usage of the web,
headway of PDA development, and expanded desires for versatile correspondence. The prerequisite for the
internet of things propels for home computerization structure has extended because of the augmentation in
light of a legitimate concern for equality between the house and the rest of the world. The ever-expanding
worldwide populace drives the interest for power. In any case, the present framework foundation is
incapable of fighting the rising vitality prerequisite. On the one hand, the electric frameworks become
wasteful and fragile due to outdated gear and innovations of the current electric lattice [5].
The administration organizations worldwide are concentrating on decreasing Carbon dioxide (Co2)
discharges because of expanding natural mindfulness, what's more, involving guidelines. At present, to
meet the ever-developing vitality request, power plants are utilizing oil, gas, coal, what's more, atomic
force. The Smart Grid changes the current matrix to work all the more helpfully, responsively, and
naturally. Electronic force molding, control of the creation, and conveyance of power are significant parts
of the brilliant network [6].
A critical fragment of the smart grid is the Advanced metering infrastructure (AMI). AMI is
unquestionably not a single development, in any case, an organized course of action of smart meters,
trades frameworks, and data the chief’s systems that engage two-route correspondence among utilities
and customers. Keen meters are critical sections of the smart grid, enabling a robotized variety of fine-
grained (regularly reliably or few seconds) essentialness use data [7].
The smart meter has made conceivable novel sort of administrations. For instance, a Savvy meter (SM)
compromises adaptable power evaluating plans, in which shoppers are charged by the hour of vitality
utilization. The information created by SM is immense and gets weak to be prepared with regular
procedures. MDA enables service organizations to make their organizations progressively effective;
shoppers set aside their cash by utilizing less vitality at top times. Likewise, utilities and purchasers can
comprehend their power use designs from the point-by-point examination of meter information. Along
these lines, it is both practical and green [8].
With shrewd meter innovation, it is conceivable to profit by request flexibility and better decisions on tax
plans. In this way, determining gives the clients the way to relate power use conduct with utilization cost.
Clients may likewise profit by anticipating arrangements through more superior comprehension of their
vitality utilization and future projections, permitting them to all the more likely deal with their vitality
bills. Additionally, at whatever point, specific controllers of a segment of the robust warming and cooling
loads can design the movement of these stacks to dodge load shedding [9]. There are numerous
difficulties in determining the issue of energy estimating since 1990 utilizing different strategies
comparing Artificial neural network (ANN) [9–11], neuro-fuzzy method, and fuzzy logic [12].
Further approaches consist of time series analysis [10] and Support vector regression (SVR) [13].
Feature fusion-based technique is used to estimate standard energy capacity for each hour regularly using
SM data. Deep extreme machine learning technique is a data handling framework enlivened by the way
natural apprehensive frameworks process the data.
540 IASC, 2022, vol.31, no.1

2 Literature Review
A massive part of the Internet of things (IoT) based keen meters is viewed as a strategy to accomplish
energy proficiency, feasible turn of events, and the capability of improving the quality, dependability, and
effectiveness of intensity of power supply. These results demonstrate the significance of the character
limit regarding significant inherent capacity, deal, and circulation of electrical force flexibly [14]; an
intelligent meter was proposed along with the applied GRU model, one artificial neural network adequate
energy supervision. Power consumption data collected to train the GRU model with the proposed smart
meter. The applied smart meter has programmed power capacity and real-time observation function, and
energy control function through power consumption prediction. A reference value is determined to
control the energy using Root mean squared error (RMS), which is one of the performance evaluation
indexes. The author confirmed that the smart meter with automatic energy control increases energy
supervision productivity [15].
The recently proposed technique utilizes Clara bunching for gathering the entire dataset into three
different gatherings relying upon their essential feeding value, which future used with arrangement
models artificial neural network and support vector machine to gauge with apparatus devoured extra
energy inside different timeframes [16]. In this study, a novel illustration of highlight significance
investigation as applied to brilliant meter order for non-private structures. The smart meter information is
monstrous, and experts frequently need to utilize worldly knowledge to separate metadata data about
structures [17].
An analytic data context for bunching housing clients based on their load features has been presented.
The structure utilized a definition approach to decrease hourly load information from 4,000 Swedish clients
in a particular time and temperature stretches. This information was a contribution to the K-implies
calculation that gathered the clients. The group results were remotely approved on review information
from 94 clients and indicated that the structure could recognize electric and non-electric warming
frameworks, along with social viewpoints associated with the family unit arrangement. An affectability
examination of model boundaries was also performed, indicating that the bunching was touchy to
changes in separation boundaries. In this way, it should be tended to when bunching will be made, for
example, tax plan for DSO. It was talked about that some potential mistakes were connected with the
climate information as the climate station was situated in another geographic zone [18].
The author’s study presents an adaptable strategy for focusing on private clients for EE programs that
attention on lessening pointless homegrown energy utilization and supplanting low proficient cooler coolers
by utilizing shrewd meter information and day by day temperature information. A tale technique is proposed
to recognize baseload [18]. In this study, the author proposed a period recurrence highlight mix-based family
trademark ID approach utilizing shrewd meter information. First, a few recurrence area highlights are
separated using discrete wavelet change notwithstanding traditional time-space measurable highlights.
Second, the arbitrary backwoods calculation is used to choose a subset of significant highlights and
eliminate redundant data in the first list of capabilities. Third, a help vector machine is utilized as a
classifier with the chosen highlights’contribution to gathering the family unit qualities. Finally,
contextual investigation using the practical information from Ireland demonstrates that the proposed
approach shows better execution after consolidating the recurrence space highlights [19].
To guarantee clients’protection and forestall pernicious clients from altering power information, a
security insurance plan of decentralized keen meter for brilliant home climate dependent on consortium
blockchain is proposed in this paper, which tackles the clients’protection issue and the danger of
concentrated stockpiling. Hypothetical examination demonstrates that the plan ensures clients’protection
and has attributes of privacy and enforceability. The security of data downloaded by power organizations
dependent on the consortium blockchain is a problem worth concentrating on later on [20,21]. Machine
IASC, 2022, vol.31, no.1 541

learning approaches like the Fuzzy system [22], Neural Network [23,24], DELM [25,26] and SVM [26] are
robust candidate solutions in the field of smart health and smart city [24].
3 Proposed EDF-FMLA System Model
The cloud and decision-based fusion model for an intelligent smart meter load forecast system are
proposed to predict the load. The preprocessing technique was used for handling the missing values. The
moving average method was used for taking missing values in the datasets. The DELM has been applied
for smart meter load prediction in the feature-based dataset in the application layer. In the evaluation
layer, the accuracy and miss rate of the purposed EDF-FMLA Model were investigated. If the learning
criteria are not met in both conditions, then the system must retrain, whereas if the learning criteria were
completed, the data are stored on the cloud, and the next step is the decision-based fusion empowered
with fuzzy logic activation. Decision-based fusion entangled with fuzzy logic determines whether the
fused feature predicts load or not, shown in Fig. 1.
After the preprocessing step, the proposed EDF-FMLA model imports fused data from the cloud to
predict energy consumption. If the energy consumption is not indicated, then the model reschedules it. If
energy consumption is indicated, then a fused database for intelligent prediction (activation layer) is
created, and energy consumption prediction is imported from the cloud.
3.1 Deep Extreme Learning Machine
The Deep extreme learning machine (DELM) is an effective method that is primarily used for prediction.
Fig. 2 represents the DELM architecture that consists of an input layer, hidden layers, and output layer. In the
input layer, the various features are used as input. Four hidden layers are used in the proposed EDF-FMLA
model.
For the mathematical model of the proposed EDF-FMLA, the input layer represents in Eq. (1), and the
output layer of the 1
st
layer represents in Eq. (2)
bi¼m1þX
n
j¼1
ajixj
 (1)
mi¼1
1þebi
;where i¼1;2;3...;x:(2)
The feedforward propagation for the 2
nd
layer to the output layer in Eq. (3):
buk¼mkþX
d
i¼1
yiuk¼1mk¼1
i
 (3)
The output layer in the activation feature is indicated in Eq. (4):
muk¼1
1þebk1
u
;where l ¼2;3...x;(4)
bl
uk¼mkþX
dm
i¼1
yiukmk
i

where k ¼1;2;3...4:(5)
542 IASC, 2022, vol.31, no.1

Figure 1: Proposed energy consumption prediction from smart meter EDF-FMLA model
IASC, 2022, vol.31, no.1 543

The backpropagation error is written as in Eq. (6):
e¼1
2X
u
Targetlmk¼4
u

2;(6)
Targetl, and mk¼4
lRepresent the chosen and calculated outputs, correspondingly.
Eq. (7) imitates the rate of the weight change that is written for the output layer:
Da /
@e
@a
Dmi;lk¼4¼e@e
@mk¼4
i;l
(7)
By adding the chain rule, in Eq. (8):
Dmi;lk¼4¼e@e
@mk
l
@mk
l
@blk
@blk
@mi;lk
(8)
where,
Applying the chain rule (substituting Eq. (8)), to obtain the weight value revised as shown in
Eq. (9):
Dmi;lk¼4¼e Targetllk
l

llk1llk

lk
i

;
Dlk
i;l¼eplkmk
i(9)
Figure 2: Deep extreme learning machine model for the proposed EDF-FMLA model
544 IASC, 2022, vol.31, no.1

where plk¼Targetlmlk

mlk1llk

;and so on:
Daj;ik/ X
l
@e
@mlk
@mlk
@blk
@blk
@mik
"#
@lik
@bik
@bik
@aj;ik
;
Daj;ik¼eX
l
@e
@mlk
@mlk
@blk
@blk
@mik
"#
@mik
@bik
@bik
@aj;ik
;
Daj;ik¼eX
l
Targetllk
l

mlk1mlk

lik

"#
mlk1llk

hj;
Daj;ik¼eX
l
plkyi;lk

"#
lik1lik

hj;
Daj;ik¼epikhj;
where pik¼P
l
plkyi;lk


lik1lik

.
Then the weights are the updating and biases between them, the output and hidden layers in
Eq. (10):
yþ
i;lk¼4¼yi;lk¼4þdek¼4Dmi;lk¼4(10)
The weight and bias alterations between the input and hidden layers are represented in Eq. (11).
aþ
j;ik¼aj;ikþdekDaj;ik;(11)
deis the learning rate of the modal, and the value of deis between 0 and 1. The margin of the model
depends upon the vigilant selection of the value of de.
3.2 Support Vector Machine
In the application layer, the SVM is used for the prediction of load with a smart meter. It is a supervised
ML algorithm that can be implemented in both regression and classification problems, but SVM is mainly
used for classification tasks. K-fold cross-validation method is applied for the SVM algorithm in the
forecast phase. The cross-validation method is applied to test the effectiveness of the model. K-fold sets
are used for all data samples contributed, just as in the training and test phases. 4-fold, 10-fold, 14-fold,
and 20-fold sets are applied to the processed data in the proposed model. In the performance evaluation
layer, accuracy, miss rate, and other statistical parameters are designed to estimate the outcomes of the
proposed model. The proposed model classifies the smart meter load prediction conditions into two
classes, namely negative, positive. The negative case means no load prediction, and the record will not
proceed. It would not be updated in the cloud database and positively signify the load prediction and data
will be updated in the cloud database. The proposed EDF-FMLA can be stated mathematically as:
Given the equation of the line as,
s2¼ws1þy(12)
where ‘w’denotes the slope of the line and ‘y’represents the intersect, so, Eq. (12) written as
ws1s2þy¼0
IASC, 2022, vol.31, no.1 545

Suppose, l¼s1;s2
ðÞ
Tand m¼w;1ðÞthen above equation becomes as
m:lþy¼0(13)
Eq. (12) obtain from 2-dimensional vectors. Eq. (12) can work for other dimensions, the Eq. (13) shows
the general equation of hyper lane.
The direction of a vector l¼l1;l2
ðÞ
Tis written as m
m¼l1
l
jjjj
þl2
l
jjjj (14)
where,
l
jjjj
¼ffiffiffiffiffiffiffiffiffiffiffi
l2
1þl2
2
q
As we know that
cos bðÞ¼ l1
l
jjjj
and sin bðÞ¼ l2
l
jjjj
Eq. (3) can also be written as
m¼ðcos bðÞ;sin bðÞÞ
m:l¼m
jjjj
l
jjjj
cos aðÞ
Let, a¼mb
Applying cos function on both sides of the equation,
cos aðÞ¼cos mbðÞ
cos aðÞ¼cos mðÞcos bðÞþsin mðÞsin bðÞ
where,
cos mðÞ¼ m1
m
jjjj
and sin mðÞ¼ m2
m
jjjj
where,
l¼ðcos mðÞ;sin mðÞÞ
cos aðÞ¼m1
m
jjjj
l1
l
jjjj
þm2
m
jjjj
l2
l
jjjj
mT:l¼m1l1þm2l2
m
jjjj
l
jjjj
m:l
m
jjjj
l
jjjj
¼m1l1þm2l2
m
jjjj
l
jjjj
m:l¼m
jjjj
l
jjjj
m1l1þm2l2
m
jjjj
l
jjjj

546 IASC, 2022, vol.31, no.1

m:l¼X
c
i¼1
mili(15)
The dot product can be computed as Eq. (15) from multidimensional vectors. Let
f¼mk:lþxðÞ
If sign (g) < 0.5 then energy optimization is negative, if the sign (g) is greater than and equal to 0.5 and
less than 1.5 then energy optimization is positive or yes.
Given a dataset S, we compute the energy optimization as follows,
fi¼nim:lþyðÞ
In the following equation, g represents the functional margin of the dataset
g¼min
i¼1...cfi
The largest f will be selected by taking hyperplanes as the geometric margin of the dataset and is
represented by g. The main objective is to take an optimal hyperplane which can be attained by finding
the appropriate values of y and m:
The Lagrangian function is defined in the following equation,
hm;y;wðÞ¼
1
2m:mX
c
i¼1
bin:m:lþyðÞ1½
Y¼mhm;y;wðÞ¼mX
c
i¼1
binili¼0 (16)
Y¼xhm;y;w
ðÞ
¼X
c
i¼1
bini¼0 (17)
From the above two equations, we get
m¼X
c
i¼1
biniliand X
c
i¼1
bini¼0 (18)
After substituting the Lagrangian function cwe get
mb;yðÞ¼
X
c
i¼1
bi1
2X
c
i¼1X
c
j¼1
bibjninjlilj
thus
max
bX
c
i¼1
bm1
2X
c
i¼1X
c
j¼1
bibjninjlilj(19)
Subject to bi0;i¼1...:c;P
c
i¼1
bini=0
Lagrangian multipliers technique is long to Karush-Kuhn-Tucker (KKT) conditions due to constraints
have inequalities. The new requirement of KKT states that
IASC, 2022, vol.31, no.1 547

bimimi:lþyðÞ1½¼0 (20)
lis the optimal point and bis the positive value and afor the other points are 0
So,
nimi:lþyðÞ1ðÞ¼0:(21)
These are called support vectors, which are the closest points to the hyperplane. According to the above
Eq. (21)
mX
c
i¼1
binili¼0
m¼X
c
i¼1
binili(22)
To compute the value of x we use the following equation
nimi:lþyðÞ1ðÞ¼0 (23)
Multiplying both sides by miin Eq. (12), we get
n2
imi:lþyðÞni
ðÞ¼0
where n2
i=1
mi:lþyðÞni
ðÞ¼0
y¼nimi:l(24)
Then
y¼1
VX
v
i¼1
nimi:l
ðÞ (25)
The numbers of support vectors are V that will make the hyperplane used to make predictions. The
hypothesis function is defined as follows,
pm
i
ðÞ¼ 0ifm:lþx,0:5
1ifm:lþx0:5
 (26)
The point that lies on the hyper-plane will be considered class 0 (negative), and the point that lies down
the hyperplane will be categorized as 1 (positive). The primary purpose of the SVM is to discover the best
hyper-plane that can distinguish the data proficiently and accurately.
3.3 Decision-Based Fusion Empowered with Fuzzy Logic
The proposed decision-based fusion modal empowered with fuzzy logic is based on knowledge,
expertise, and logical reasoning ability. The Fuzzy Logic modal can manage the uncertainty and
imprecision of data usage in a proper method. The proposed cloud and decision-based fusion for smart
meter load forecasting system using the hierarchical DL SMFB modal mathematically modal is written as
follow:
548 IASC, 2022, vol.31, no.1

mDELMlayer1\SVM layer 1ðdelm;svmÞ¼min½mDELMlayer1ðdelmÞ;mSVMlayer1ðsvmÞ
Fig. 3 represents the decision-based fusion diagram for the detection of energy consumption from smart
meters.
R
1
edf
= if DELM layer 1 is positive and SVM layer 1 is positive, then energy consumption is positive
R
2
edf
= if DELM layer 1 is positive and SVM layer 1 is negative, then energy consumption is positive
R
3
edf
= if DELM layer 1 is negative and SVM layer 1 is positive, then energy consumption is positive
R
4
edf
= if DELM layer 1 is negative and SVM layer 1 is negative, then energy consumption is negative
Fig. 4 displays the output of the energy consumption consisting of DELM layer 1 and SVM layer 1. If
SVM layer 1 is 80 to 100 and deep extreme 75 to 100, results will be positive, and energy consumption is
entirely predicated. If SVM layer 1 is 0 to 60 and DELM 0 to 75, then results will be negative it will not
predict the energy consumption from smart meters.
Figure 4: Proposed EDF-FMLA model rule surface for decision-based fusion
Figure 3: Proposed EDF-FMLA lookup diagram for decision-based fusion
IASC, 2022, vol.31, no.1 549

4 Proposed EDF-FMLA Modal Results
Matlab Tool 2019 is utilized for simulation and results based on an intelligent decision support system
for the EDF-FMLA model empowered with deep learning based on a Support vector machine. The proposed
Cloud and decision-based fusion for smart meter energy consumption prediction (EDF-FMLA) model were
developed to predict load consumption from smart meters. In the proposed model using MATLAB
simulations, results are obtained for prediction. The proposed EDF-FMLA model consists of two
approaches, the SVM and DELM. In layer 1 the SVM and DELM approaches were used on 25575 and
2576 fused samples, correspondingly. For both methods, 80 percent of the fused samples were used for
training purposes, and 20%were used for validation. The accuracy and miss rates of the proposed
EDF-FMLA model were compared with the other existing approaches.
Miss rate ¼O1
T0
þO0
T1
T0þT1
(27)
Accuracy ¼O0
T0
þO1
T1
T0þT1
(28)
Average percentage ¼Percentage of SVM layer 1ðÞþPercentage pf DELM layer 1ðÞ
Sample size 1 þSample size 2 (29)
The proposed model detected either a smart meter predicts the load or not.
Tab. 1 represents the detection of the proposed EDF-FMLA model for training and used 80% fused
samples. In Layer 1 the SVM Attained 98.9% and a 1.1 miss rate. DELM layer 1 obtained
84.27 accuracy and a 14.73 miss rate. The average performance of the purposed EDF-FMLA model layer
1 achieved 91.58 accuracy and a 7.9 miss rate.
Tab. 2 represents the detection of the proposed EDF-FMLA model for validation and used 20% fused
samples. In Layer 1 the SVM Attained 97.4% and a 2.6 miss rate. DELM layer 1 obtained 84.1 accuracy and
a 14.99 miss rate. The average performance of the purposed EDF-FMLA model layer 1 achieved
90.70 accuracy and an 8.7 miss rate.
Table 1: Layer 1 performance for the proposed EDF-FMLA model (training)
80 percent fused samples for training
Approaches Accuracy Miss rate
SVM layer 1 98.9 1.1
DELM layer 1 84.27 14.73
Average performance proposed EDF-FMLA Layer 1 91.58 7.9
Table 2: Layer one performance for the proposed EDF-FMLA model (validation)
20 percent fused samples for validation
Approaches Accuracy Miss rate
SVM layer 1 97.4 2.6
DELM layer 1 84.01 14.99
Average performance proposed EDF-FMLA Layer 1 90.70 8.7
550 IASC, 2022, vol.31, no.1

Tab. 3 lists the overall performance of the proposed EDF-FMLA model for the training and validation
phases. The proposed EDF-FMLA model achieved 98.07 overall accuracy and a 2.93 miss rate in the training
phase, and in the validation phase obtained 97.04 accuracy and a 2.96 miss rate. The proposed EDF-FMLA
model layer 1 achieved 98.07 accuracy, which is better than the existing approaches. The proposed EDF-
FMLA model also detects energy consumption from smart meters which achieve accuracy for validation
97.04 phase.
Tab. 4 represents a contrast between the state-of-the-art methods and the proposed EDF-FMLA model.
The proposed EDF-FMLA layer 1 model achieved 97.04 accuracy for predicting energy consumption with a
smart meter which is better than the existing approaches.
5 Conclusion
The usage of IoT-based Smart Meter in electric power consumption shows a significant role in helping
users manage and control their electric power consumption. It produces smooth communication to build
equitable electric power distribution for users and improved the entire electric system for providers.
Machine learning predicting structures have been worked to apply the electric efficiency and response of
progressive energy creation, transmission, and consumption. The proposed IoT-based smart meter uses
deep extreme machine learning techniques to predict meter energy consumption. The proposed
EDF-FMLA model controls the power consumption relying on and decision-based fusion. A deep
extreme machine learning approach applied to feature-based data provided a better result. The proposed
EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which
is better than the existing approaches.
Acknowledgement: Thanks to our families & colleagues who supported us morally.
Funding Statement: The authors received no specific funding for this study.
Table 3: Layer 2 overall performance for the proposed EDF-FMLA model
Overall performance Accuracy Miss rate
Proposed EDF-FMLA model (training) 98.07 2.93
Proposed EDF-FMLA model (validation) 97.04 2.96
Table 4: Proposed EDF-FMLA model in-contrast with literature
Approaches Accuracy
Linear regression [27] 68.58
Polynomial regression [27] 76.87
DecisionTree regression [27] 81.33
BP.ANN [28] 88.30
DELM 84.01
Proposed EDF-FMLA model 90.70
IASC, 2022, vol.31, no.1 551

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the
present study.
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