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A Smart Grid Prerequisite: Survey on Electricity

Demand Forecasting Models and Scope Analysis of

Demand Fsorecasting in Bangladesh

Samiul Islam

Department of Computer Science and

Engineering

BRAC University

Dhaka, Bangladesh

[email protected]

Amin Ahsan Ali


Engineering

University of Dhaka

Dhaka, Bangladesh

[email protected]

Moinul Zaber


Engineering

University of Dhaka

Dhaka, Bangladesh

[email protected]

Abstract–Electricity supply via smart grid mechanism is

gaining importance in many country’s priority lists. A detailed

study on electricity forecasting is required to ensure a smooth

transition to the smart grid. Forecasting is evident to ensure better

management of generation plants, supply grids and the

transmission system. This article focuses on demand forecasting

study as a preparation of smart grid, presents a technical

survey/review of several forecasting methods which have been

done earlier. This paper also conducts a study highlighting the

forecasting scenario, performs scope analysis from developing

countries’ context and presents analytical results of a short-term

forecasting. As a candidate, these have been discussed on the basis

of electricity load data of Dhaka, the capital city of Bangladesh;

one of the world’s most highly populated city. Electricity

distribution has been handled in Dhaka city by two companies,

DESCO and DPDC. This article discusses on few average sized

DESCO zones of its total 16 and emphasized more on Shah Ali

zone. Two selective feature based forecasting methods have been

also proposed and results have been shown to support that

forecasting will help to see the unseen.

Index Terms –Demand forecasting, Smart Grid, Developing

countries, Scope analysis.

I. INTRODUCTION

Electricity is a prerequisite for economic prosperity. If the

current global energy consumption pattern continues, by 2030

overall consumption will be increased by 50% [1]. These

energy transitions are more visible in a country experiencing an

economic shift. A country while in its developing phase, should

ensure efficient use of its resources, proper management,

prioritisation of needs, forecast the demand etc. In a developing

country, the industrial sector generally consumes 45% to 50%

of the total commercial energy [2]. Electricity as one of the fuels

of the development thus needs to be managed in an organised

fashion. Policy makers need to come up with a better plan than

they currently own where statisticians, researchers, data

scientists, practitioners can come into play. A number of data-

driven models have been proposed by the researchers to predict

future demand. These models differ from situation to situation

based on the input data, aggregation level, frequency, time span,

economic status of the region, incorporation of renewable

energy resources etc. for short-term, medium-term or long-term

prediction analysis.

Smart grid (SG), designed with digital technologies

channels a two-way communication between the customers and

the utility. It includes different operational and energy measures

through smart meters, smart appliances and energy efficient

resources [3]. Another way of saying, SG is an advanced

electricity transmission and distribution network that uses

information, communication, and control technologies to

improve the economy, efficiency, reliability, and security of the

grid [4]. Many countries worldwide now either have switched

or are planning to replace their old electric grid system with the

SG. For example, in 2010, the US government spent $7.02B on

its SG initiative, while the Chinese government used $7.32B for

its SG program [5]. In Bangladesh, The distribution system loss

is high and the customers face daily planned load shedding. To

address the power crisis and other problems, the conventional

distribution system should be restructured to the smart

distribution system, a part of SG. Though it is a very new and

expensive concept, yet Bangladesh Government has shown

positive approach [6]. Through the SG, demand forecasting

even in end-user level can be achieved which will provide the

necessary information for having a better awareness of

individual usage pattern and efficient pricing strategy, in

addition with, planning for growth [7]. As with SG, this is

ensured that more granular level forecasting can be performed,

before switching we need to understand the different

forecasting techniques and their applicability. This transition is

not a straightforward concept to acquire all of a sudden. Moving

from old conventional grid systems to SG, there are several

prerequisites to ensure. While this is being planned, currently

available data, patterns in it, techniques those can be used

should be observed thoroughly as a preparation step for the final

goal. This article can bring a positive impact towards it.

Load prediction may depend on several factors including

time, social, economic, and environmental variables by which

the pattern will form various complex variations [8], [9]. Social

and environmental factors are sources of randomness found on

the load pattern [10]. There are some prediction models based

on architectonic features such as heat loss surface, building

shape factor, building heated volume and so on [5], [6], or

housing type and socioeconomic features such as age of the

dwelling, size of the dwelling, monthly household income,

2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)21 - 23 Dec 2017, Dhaka, Bangladesh

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number of household members, etc. [11] along with historical

load data.

The main contributions of this paper are (i) a review of load

forecasting techniques, (ii) load forecasting scope analysis and

(iii) a preliminary model and results of load forecasting. Section

II of this paper presents a review of load forecasting

methodologies, section III discusses load forecasting scopes,

section IV comprises results of a short forecasting on the

available data while section V concludes the paper.

II. REVIEW OF LOAD FORECASTING METHODOLOGIES

On the basis of input data, expected outcome forecasting

methodologies differ, so does models. Hence, to understand

how forecasting works, a study on forecasting in inevitable.

This section contains a summarised technical review of a few

electricity demand/load forecasting related research. This

summary focuses on a particular research at a time. It will

summarise the data related information followed by the

models/algorithms used and performance. Research chosen

here are published on or after 2012. Primarily around 10 such

forecasting papers were chosen and from there 4 papers have

been selected and discussed here as because of page limitation.

Selected papers differ in both long and short-term prediction

and also in space and context which helps us understand the

applicability of different algorithms in various context. Goal

here is to discuss and analyse recent approaches taken by the

researchers related to developing countries (those who haven't

yet switched to SG with one exception where forecasting

technique on smart grid is discussed to point out how SG helps

in more granular level prediction) and also extend the view of a

previously published review paper of Suganthi L, Samuel A.A.

back in 2012 [2]. Their research was not specifically on

electricity load forecasting rather was on overall energy

demand prediction. Before going into the reviews of selected

papers, a brief summary from the view of electricity load

prediction on Suganthi L, Samuel A.A.’s review paper is

included here. From there, the discussion will be taken further

by classifying those recent researches based on different

datasets used and also on different conclusions were drawn.

Suganthi L and Samuel A.A’s review paper discusses

several models of load prediction highlighting the uses in

previous research. Demand is changing over time, so is data and

models. Moreover, performance is varying for previously

introduced models against the same data sources. So, with such

changes in trend, an updated review is also needed. In that

energy forecasting review paper [2], models like time-series,

regression, econometric, decomposition, genetic algorithms

etc. have been discussed. Here, we are promoting similar sort

of models those have been used recently. On the basis of the

articles selected here, techniques can be classified as Time

series models, Regression analysis, Group method of data

handling, Classifiers, Fuzzy inductive reasoning and Hybrid

techniques. Here, each model and its performance have been

discussed article-wise with a data description of each research

on which that model was executed.

Long-term forecasting: Kaytez F. et al. conducted

comparative analysis of regression models, neural networks and

least squares support vector machines [12]. This research has

been carried out on Turkish electricity transmission data which

spans over the time span of 1970~2009. The indicators that

were used as an independent class variable to predict the net

electricity consumption were the number of population,

installed capacity of the power generation, gross electricity

generation and total subscription. The Turkish Electricity

Transmission Company (TEIAS) statistical database was used

for Turkey’s total installed capacity and gross electricity

generation [11], [13], [14], and the Turkish Statistical Institute

(TIE) database was used for population data [15]. Both the

number of subscribers and the net electricity consumption

values for Turkey were taken from Turkish Electricity

Distribution Company (TEDAS) and other private electricity

distribution companies [16]. Data were split into 2:1 ratio for

training and testing purposes respectively while the process was

validated using 2010~11 data. A multilayer feedforward

backpropagation neural network was one of three models used

in prediction of the net electricity consumption. Multiple linear

regression models was another method used in addition to the

least square support vector machine (LS-SVM) to estimate the

target class. The predictive accuracy of all the model was

impressive but the LS-SVM outperforms other models in

various performance indicators [17].

Short-term forecasting: A short-term forecasting research

work has been performed on Korean hourly electricity load

data. This one day ahead of load forecasting technique was

performed on a single Korean electric load data set sourced by

Korea Electric Power Corporation. This has been carried out on

a sample set of two weeks of data where weekdays’ data was

used for forecasting load for another immediate weekday and

likewise, weekend data was used to predict the load of another

subsequent weekend.

The data was classified before making a forecasting model

using K- means and k-NN to eliminate error from calendar-

based classification. Then Artificial neural network (ANN),

Group Method of Data Handling (GMDH) in addition to Simple

Exponential Smoothing (SSE) were used to design the

predictive model. They also gave a comparative study of the

models. GMDH which had the least Mean Absolute Percentage

Error (MAPE) provided the best predictive accuracy in

compared to other models. GMDH uses Kolmogrov-Gabor

polynomial which is an inductive self-organizing data-driven

approach which provides scintillating performance in

modelling small data set. In this study, the data set was very

small as mentioned earlier and other than historical load data no

other sources have been used to predict the load which assists

GMDH to be the best performer because of having strength in

providing predictive accuracy in small data-driven predictive

design [17].

Another short-term load forecasting research was done by

Lee C.M, and Ko C.N. Electric load data set of the year 2007 of

Taiwan has been used here to perform 24 hours ahead of load

forecasting. Three different patterns of load data are utilised to

evaluate the effectiveness of the proposed algorithm. Working

days were considered from Monday to Friday, Saturday as

weekends and Sunday and other national holidays as holidays.


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Whole year’s data was divided into these 3 groups and different

training and testing data have been selected from each group to

perform and evaluate forecasting efficiency [18].

Previous 41 days of weekdays data used as an indicator to

predict the load of the 42nd date. Likewise, few weekends and

holidays data was used to predict a load of the subsequent

weekend and holiday. Here, a model was proposed to improve

the accuracy of the short-term load forecasting which integrated

radial basis function neural network (RBFNN), support vector

regression (SVR), and adaptive annealing learning algorithm

(AALA). Determining the initial structure of the RBFNN using

an SVR was the first step in designing the proposed

methodology. With the intention of optimising the initial

parameters of SVR-RBFNN (AALA-SVR-RBFNN), an AALA

along with the time-varying learning rates was applied. In order

to overcome the stagnation for searching optimal RBFNN, a

particle swarm optimisation (PSO) was applied to

simultaneously find promising learning rates in AALA. Finally,

the short-term load demands were predicted by using the

optimal RBFNN. Comparing DEKF-RBFNN, GRD-RBFNN,

SVR-DEKF-RBFNN, and ARLA-SVR-RBFNN with the

proposed AALA-SVR-RBFNN, the error of MAPE is reduced

by 44.94%, 68.79%, 12.50%, and 20.97%, respectively.

Short-term forecasting (consumer specific): Another

research has been carried out in small scale with some new

fields as predictors. Here, data of three buildings of the

Universitat Politecnica de Catalunya (UPC-Technical

University of Catalonia) was obtained; 1) the Library of the

ETSEIAT2 faculty in Terrassa of more than 500 m2; 2) the bar

of the ETSAV3 faculty in Sant Cugat of 150 m2; 3) a building

with different classrooms at FIB4 faculty in Barcelona of 630

m2. For determining the scalability of the models in any type of

building, three zones with different profiles of consumption and

locations are used here affecting different climatology

(temperature, humidity, solar radiation, etc.), consumption

patterns, schedules and working days. There was a one-hour

data acquisition frequency. Therefore there are 24 recordings

per day per location. For all three zones, the data set comprise

a whole year of electricity consumption, from 13/11/2011 to

12/11/2012. Training and test data were split respectively into

91% and 9%. The testing data consists of 35 different days

distributed equally through the whole year; meaning around 9

days per season. For understanding the seasonal effect, the

whole dataset was split into four chunks; autumn, winter, spring

and summer [19].

In this research, two Soft Computing techniques and one

traditional statistical method were used for the hourly energy

forecasting in buildings and the techniques were Random

Forest (RF), Artificial Neural Network (ANN), Fuzzy Inductive

Reasoning (FIR) and Auto Regressive Integrated Moving

Average (ARIMA), respectively. At first, a feature selection

methodology was applied to previous consumption data to dig

out features having the highest impact on prediction. The design

of the predictive model was divided into two stages: 1) Feature

selection applied before training to all the methods 2) All the

models used without any feature selection. In this study, FIR

outperformed, most of the cases, all the methodologies

followed by the RF and NN. Regarding ARIMA which had the

least errors but when it came to predictive accuracy, ARIMA

was far from most of other methodologies [19].

TABLE I. SUMMARY OF FORECASTING TECHNIQUES

Ref Data used Time span

&Country

Subgroup

ing

Methodology

12 Load data Number of

population

Installed capacity Gross electricity

generation

Total subscription

40 years

Turkey

Multilayer feedforward backpropagation neural

network

Multiple linear regression models

Least square support

vector machinea

17 Load data 2 weeks

Korea

Weekday and

Weekend

Artificial neural network

Group Method of Data

Handlinga

Exponential Smoothing

18 Load data 1 year

Taiwan

Weekday,

Weekend

and Holiday

Radial basis function

neural networka

Support vector regression Adaptive annealing

learning algorithm

19 Load data (Data of 3

buildings of Universitat

Politecnica de Catalunya were

used)

1 year

Spain

Autumn,

Winter, Spring and

Summer

Random forest

Artificial neural network

Fuzzy Inductive

Reasoninga

Auto-Regressive

Integrated Moving Avg. a. Best performing methodology in each technique

In light of these research and from the summary of TABLE

I, it has been seen that to perform load forecasting, along with

historical load/transmission data, the number of population, the

number of subscription, power generation statistics,

meteorological data etc. are required. For more granular level

forecasting, data is needed from the smart meters (smart grid is

required to perform this level of forecasting) about particular

household’s load requirements at different hours of the day,

randomness in requirements, appliances used, shape and size of

the household etc. (last technique of TABLE I is such

approach).

In many research, data has been further subdivided into

some groups to ensure better forecasting and to accommodate

seasonal impacts and occasional impacts (subgrouping column

of TABLE I). The timespan of dataset also matters. Therefore,

a detailed study of the available data, finding patterns,

understanding the relevance of heterogeneous sources etc. is a

huge concern before starting forecasting. Hence, such review of

forecasting techniques and scope analysis is a must.

III. SCOPE ANALYSIS OF DEMAND FORECASTING IN

BANGLADESH

Bangladesh is one of the developing countries in the world

with an area of around fifty-seven thousand square miles.

Country’s demand in the energy sector is increasing highly

while the scenario of electricity utility structure (generation,

supply, distribution) is very complex. This section includes

details of the electricity sector in Bangladesh, how distribution

works, how legacy methodologies are being used to predict

electricity demand/load and future prospects.


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Under the jurisdiction of the Ministry of Power, Energy and

Mineral Resources (MPEMR), the Power Division (PD)

oversees the whole electricity utility. Electricity is generated by

the Bangladesh Power Development Board (BPDB), a

company spun off from BPDB, Independent Power Producers

(IPPs) and private power producers. Generated electricity is

supplied via the Power Grid Company of Bangladesh’s (PGCB)

power grid to the capital area and then distributed by Dhaka

Power Distribution Company (DPDC) and Dhaka Electricity

Supply Company (DESCO); local areas are supplied by BPDB

and West Zone Power Distribution Company Limited

(WZPDCL); and farming areas are supplied by Palli Bidyuit

Samity (PBS). A few generation plants (SIPPs) are directly

distributing to the distributors while others are delivering

through the national power grid. Under these distributors, there

are several substations from where power goes area specific

feeders. Feeders are directly responsible for consumer-end

distribution. So, from the top level, we can name this tier flow

as: Generation > Transmission (PGCB) > Distribution

(Substation) > Feeder > Consumer end.

Forecasting always will have uncertainty as we are

predicting a future state which depends on several 'frequently

changing' data like, meteorological data, economic status,

consumption behaviour etc. We all that can ensure is a good

forecasting method with a minimum possible error. A proper

management and good forecasting require detailed and

necessary data flow from consumer end & feeders to

Distributors/Substations (for understanding area specific need

and ensuring timely distribution) & to Generation plants (to

design an optimum generation schedule for satisfying the need).

Monthly billing data/consumption data, hourly load

variability data and load shedding data are available through

Dhaka Electric Supply Company (DESCO) and Dhaka Power

Distribution Company (DPDC). Mainly Shah Ali, an averagely

sized zone of those 16 belongs to DESCO with an area of 5.15

square km and a population over 100 thousand has been used to

determine scopes and evaluate forecasting performance.

In every substation (there can be one or more substations

under one zone), under each feeder, the load has been recorded

hourly which means 24 entries each day. In Bangladesh, these

entries are not maintained in a digitised format rather it is being

handwritten in a logbook. As the actual load data is not

digitised, a summarised report is being sent to the upper level

on which legacy methods of load prediction works. Because of

this aggregation (summarised report), we lose some key factors

like, feeder wise load, the difference in peak hours across

different feeders etc. which could lead to better management.

Fig. 1 Supply/Demand (Max) Balance in a Day: in 2015, available

capacity was enough to satisfy maximum demand

Fig 1 represents a comparison between installed capacity,

available capacity and maximum demand for the year 2013 to

2015 [20]. Approximately 30% of installed capacity was not

available due to decreases in the output and thermal efficiency

and failures of power generators mainly due to the insufficient

periodic maintenance etc. However, it is clear that in the year

2015, available capacity was sufficient to satisfy maximum

demand. However, load shedding is still a huge concern here in

Bangladesh. Without knowing area specific demand, even if

with available resources, demand cannot be satisfied. Thus,

aggregated data and legacy method needs to be restructured.

Moreover, In Dhaka, electricity consumers are increasing day

by day. If Pallabi, Shah Ali and Baridhara, three zones of

DESCO have been considered, number of connections has been

almost doubled from 2010 to 2015. An increment in number of

consumers is equivalent to the complexity of management

which demands a better forecasting. There are occasional

impact, seasonal impact on load variability, the effect of

industrial working hours or feeder-wise pattern difference in

same substation etc. These also support the claim that better

forecasting can be achieved with the data in hand and data from

meteorological department. After a successful transition to the

smart grid, more granular level data will be available; so,

another level of betterment in load forecasting could be

achieved.

Fig. 2 Hourly Average Load of Kalyanpur: loads are almost stationary

throughout the day where loads in winter are around 40 kWh less than the loads

in summer

Fig. 3 Hourly Average Load of Shah Ali: the graph is following a hockey stick pattern. From 12 am to 7 am, the load remains almost stationary and lowest

while during 8 am to 11 pm, for half of this range, load rises and then gradually

falls. Loads in winter are around 40 kWh less than the loads in summer

0

5000

10000

15000

2013 2014 2015

Meg

aw

att

Year

Installed Capacity Available Capacity Maximum Demand

0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Avera

ge L

oa

d

Hours of the day

winter summer

0

50

100

150

200

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Avera

ge L

oa

d

Hours of the day

winter summer


694

Fig 2 and 3 represents a comparison between winter and

summer season in Kalyanpur and Shah Ali substation of Shah

Ali zone respectively. As, during winter, people most likely do

not use room heaters and during summer many people use ACs,

it is clearly visible that in summer, consumption is quite high.

However, in a particular season (summer or winter), in

Kalyanpur substation, the load varies a little throughout the day

while in Shah Ali, from midnight to 7 AM in the morning it

remains almost same but then gradually rising till 12 PM,

mostly remain constant till 8 PM and then again gradually drops

till midnight. The main point of bringing these statistics is to

clear the point that even in the same zone, different substations

act differently. As the load data are available from the logbook

of previous years, substation-wise or feeder-wise forecasting

could lead to a better management and distribution in the

electricity sector.

Fig 4 represents a comparison of the percentage of load

shedding occurrence on a day of a week between different

feeders of Shah Ali substation. Around 30% of load shedding

occurs on Sunday while 50% occurs in first two weekdays

(Sunday and Monday). For some feeders, i.e. Mazar Sharif and

Ahmed Nagar this crosses even 70%. So, satisfying the need of

only two days of a week can reduce the load shedding for more

than 50%.

Fig. 4 Percentage of Load Shedding Occurrence on a Day at Shah Ali:

More than 50% load shedding occurs during first two days of a week

Here, the only snapshot of entire data has been used to

show the statistics and showcased a glimpse of what can be

achieved. Forecasting at this granular level will ensure better

management, efficient generation and distribution which might

have a positive impact on reducing load shedding as well.

IV. LOAD FORECASTING AND PERFORMANCE ANALYSIS

To forecast load, granular level data is a prerequisite such

as half-hourly or hourly historical load data, generation

capacity, meteorological data (like hourly temperature, rain

prediction index, humidity etc.), wealth index of an area,

population of an area, number of active connection, frequency

and impact of load shedding etc. More in-depth data ensures

more accurate forecasting generally. It has been mentioned

before that hourly load data are kept in a handwritten log book.

Because of such limitation, four parts of data has been digitised

from two substations (two parts from each substation) in a one-

week span. One week of summer and one week of winter data

has been digitised to compare seasonal load variability. On the

basis of above scope analysis, with the currently available

sources, a model has been proposed to perform a short-term

load prediction. For performing forecasting, one-week of

summer data of Shah Ali substation has been taken from 15th

May 2017 to 21st May 2017; a total of 7 days (1st 6 days for

training the model and the 7th day for testing). Daily

temperature has been web scrapped from Weather Underground

website [21] and based on the maximum and minimum

temperature, a uniform distribution has been run to mimic the

hourly temperature.

Two different forecasting model has been proposed

varying the inputs and has shown that better forecasting can be

achieved with more granular level data. These two models have

been denoted as ‘5 input model’ and ‘8 input model’. In 5 input

model, an hour of the day, whether this is a peak hour or not,

due point, temperature (hourly) and previous hour’s load have

been used as features to predict future demand. In 8 input

model, along with the features of the first model, load of

24hours earlier, average load of the previous day and peak

hours’ average load of previous day have been used as

predictors. We then run a neural network with 70% data in

training, 15% in validation and 15% in testing to create a neural

net. Later, testing data of 21st may 2015 which was not a part of

training process was sent to judge.

Fig. 5 Workflow of the load forecasting network buildup and performance

evaluation

Fig 5 represents the flow of our proposed model.

Underlined input sources are only for 8 input model. Both the

train data (that has been used to build the model) and a separate

test data has been run through the model. Fig 6 and 7 represent

actual and predictive output comparison. Green line denotes the

actual output while blue line signifies the predictive outcome.

Fig. 6 Performance of the training set that has been used to train the

network in 5 (left) and 8 (right) input model

0% 20% 40% 60% 80% 100%

A.N(Ahmed Nagar)

D.S(Darussalam)

G.T(Gabtoli)

J.H(J.Housing)

M.P(MP RMU)

M.S(Mazar Sharif)

R.N.I(RM Industry)

S.A.B(Shah Ali Bag)

S.B.J(S Buddizibi)

Sec-2 (Section-2)

Sec-7 (Section-7)

Sunday Monday Tuesday Wednesday Thursday Friday Saturday


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Fig. 7 Performance of the test set that has not been used to train the

network in 5 (left) and 8 (right) input model

TABLE II. PERFORMANCE ANALYSIS LOAD FORECASTING MODELS

5 input model 8 input model

Acc

ura

cy Dataset known to model:

(100-6.4817) = 93.5183%

Dataset unknown to model:

(100-8.0615) = 91.9385%

Dataset known to model:

(100-2.0672) = 97.9328%

Dataset unknown to model:

(100-5.1488) = 94.8512%

We have computed accuracy using MAPE formula and it

turns out our 8 input predictive model is performing better. We

have achieved around 95% of accuracy while in 5 input model,

this is around 90% for unknown datasets (those not have been

used to train the network). Few more sources have been used in

8 input model which results in a better forecasting. TABLE II

represents the accuracy of the models and comparison shows

that 8 input model performs better.

V. CONCLUSION

Proper management of electric energy is crucial for the

development of any country. An accurate and adaptive

electricity demand prediction can efficiently address the ever-

asking question, how much electricity needs to be generated.

Moreover, precise accuracy of prediction of electric demand

will not only assists in better distribution but also will minimize

load shedding. In this article, we proposed two very basic

models of forecasting using neural network. We are not

suggesting to implement these right away; these are only

showcased to prove that betterment is possible and further study

is needed to design implementation level load forecasting

policies.

ACKNOWLEDGMENT

We acknowledge research fellow of Data and Design Lab,

University of Dhaka: Mr. A T M Shakil Ahamed and Mr. Md.

Samiul Islam, for assisting us reviewing previous research

articles.

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