Review

Comprehensive Bibliometric Analysis on Smart Grids: Key

Concepts and Research Trends
Kasaraneni Purna Prakash 1 , Yellapragada Venkata Pavan Kumar 2, * , Kasaraneni Himajyothi 3
and Gogulamudi Pradeep Reddy 2

1 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation,

Vaddeswaram 522502, Andhra Pradesh, India; kpurnaprakash@kluniversity.in
2 School of Electronics Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India;
pradeep.19phd7025@vitap.ac.in
3 VIT-AP School of Business, VIT-AP University, Amaravati 522237, Andhra Pradesh, India;
himajyothi.20phd7062@vitap.ac.in
* Correspondence: pavankumar.yv@vitap.ac.in; Tel.: +91-863-2370155

Abstract: Over the years, a rapid evolution of smart grids has been witnessed across the world
due to their intelligent operations and control, smart characteristics, and benefits, which can over-
come several difficulties of traditional electric grids. However, due to multifaceted technological
advancements, the development of smart grids is evolving day by day. Thus, smart grid researchers
need to understand and adapt to new concepts and research trends. Understanding these new
trends in smart grids is essential for several reasons, as the energy sector undergoes a major trans-
formation towards becoming energy efficient and resilient. Moreover, it is imperative to realize the
complete potential of modernizing the energy infrastructure. In this regard, this paper presents a
comprehensive bibliometric analysis of smart grid concepts and research trends. In the initial search,
the bibliometric data extracted from the Scopus and Web of Science databases totaled 11,600 and
2846 records, respectively. After thorough scrutiny, 2529 unique records were considered for the
bibliometric analysis. Bibliometric analysis is a systematic method used to analyze and evaluate the
scholarly literature on a particular topic and provides valuable insights to researchers. The proposed
analysis provides key information on emerging research areas, high-impact sources, authors and
Citation: Purna Prakash, K.; Venkata
their collaboration, affiliations, annual production of various countries and their collaboration in
Pavan Kumar, Y.; Himajyothi, K.;
smart grids, and topic-wise title count. The information extracted from this bibliometric analysis
Pradeep Reddy, G. Comprehensive
Bibliometric Analysis on Smart Grids:
will help researchers and other stakeholders to thoroughly understand the above-mentioned aspects
Key Concepts and Research Trends. related to smart grids. This analysis was carried out on smart grid literature by using the bibliometric
Electricity 2024, 5, 75–92. https:// package in R.
doi.org/10.3390/electricity5010005
Keywords: bibliometric analysis; energy consumption; smart home; smart building; smart meter;
Academic Editors: Andreas Sumper
smart grid
and Eduard Bullich-Massagué

Received: 28 November 2023

Revised: 13 January 2024
Accepted: 29 January 2024 1. Introduction
Published: 1 February 2024
Smart grids are rapidly evolving across the world. This is not only applicable to
macrogrids but also to microgrids namely buildings, homes, and cities [1,2]. Smart grid
technology enables customers, prosumers, and utilities to benefit from effective energy
Copyright: © 2024 by the authors.
management. The key characteristics of smart grids such as bidirectional flow, automatic
Licensee MDPI, Basel, Switzerland. restoration, energy control, and comfort have a significant impact on the energy sector.
This article is an open access article These characteristics empower the utilities to comprehend their customers’ requirements
distributed under the terms and and energy consumption behavior, enabling further handling of various other grid function-
conditions of the Creative Commons alities. Smart grids are of great importance as they can be integrated with renewable energy
Attribution (CC BY) license (https:// resources and contribute towards alleviating environmental pollution and managing the
creativecommons.org/licenses/by/ insistent growth of load profiles [3]. Various works on different trends and concepts are
4.0/). described below.

Electricity 2024, 5, 75–92. https://doi.org/10.3390/electricity5010005 https://www.mdpi.com/journal/electricity

Electricity 2024, 5 76

The literature works on key concepts such as energy consumption behavior, forecasting
energy consumption, load forecasting, the Internet of Things, non-intrusive load monitor-
ing, smart meters and smart meter data, smart home reasoning systems, smart buildings,
smart grids, and assimilation of variable renewable energy resources with power systems
are discussed in Table 1. These works present the details related to the implementation of
various methods and technologies with respect to smart grids.

Table 1. Review of key concepts related to smart grid research.

Year Key Concept Literature Works Carried Out Ref.

A systematic analysis was conducted to enumerate the duplicate records in the energy
2022 [4]
consumption data of smart buildings
A systematic analysis was conducted to identify the behavior of redundancy in smart home
2022 [5]
power consumption
A systematic three-step method was conducted to learn the abnormal records in smart home
2022 Data anomalies [6]
power consumption
Machine learning-based techniques were implemented to handle various data anomalies in smart
2022 [7]
home power consumption
The missing-reading information in smart home power consumption was detected by
2021 [8]
implementing an effective and easy approach
Identification of the hidden patterns and anomalies in smart home power consumption was
2021 Energy [9]
accomplished by executing a framework
consumption
behavior To comprehend the energy requirement of customers, a framework relying on machine learning
2020 [10]
was realized on smart meter data
To predict the energy utilization statistics in smart buildings, a deep learning-based hybrid model
2021 [11]
was executed
Forecasting
energy To gather the details of energy utilization in individual residential buildings, a convolutional
2019 [12]
consumption neural network (CNN) and long short-term memory (LSTM) based model was implemented
A detailed review of conventional and artificial intelligence (AI- based models was performed to
2019 [13]
forecast energy consumption
2021 An online adaptive recurrent neural network (RNN) method was applied for load forecasting [14]
To estimate short-term loads, time-series forecasting was suggested. Further, a business case was
2021 [15]
presented for analyzing various clusters to comprehend the behavior of consumers
Load
forecasting To address challenges and analyze smart meter data, a clustering-based approach was executed
2019 [16]
to achieve productive demand-side management and load forecasting
2019 To forecast short-term loads, a framework relying on LSTM-RNN was recommended [17]
2018 To anticipate the load profiles of residential consumers, a pooling-based deep RNN was executed [18]
A smart meter with IoT technology was discussed to cope with the challenges in the smart grid
2020 [19]
environment
Internet of
Things (IoT) A broad review was conducted on the feasibility of employing advanced metering infrastructure
2019 (AMI) and smart meter technologies in smart grids for reliable monitoring and better power [20]
quality
A deep learning (DL)-based multitask learning approach was executed for NILM in smart meter
2021 [21]
data
Non-intrusive To detect the appliance usage and their patterns, a new NILM approach was implemented to find
2019 [22]
load the appliance utilization patterns for better load identification and estimation
monitoring To realize the appliances’ status in a network, a voltage–current (V-I) trajectory-based NILM
2018 (NILM) [23]
algorithm was implemented
For load profile disaggregation, a novel framework based on “segmented integer quadratic
2018 [24]
constraint programming” and “hidden Markov models” was discussed
Electricity 2024, 5 77

Table 1. Cont.

Year Key Concept Literature Works Carried Out Ref.

A robust, reliable, and ML-based advanced infrastructure was discussed to investigate and
2021 [25]
observe smart meter data and further provide enhanced security to smart meters
Several applications were discussed for monitoring and protecting distribution systems with the
2021 [26]
participation of smart meters
An algorithm was implemented to disaggregate household solar energy consumption by
2021 Smart meters [27]
reducing the parameters of smart meter data
and smart
meter data A scalable strategy was discussed using smart meter data for targeting residential consumers to
2020 [28]
be part of energy efficiency programs that, in turn, reduce consumption
To sense the non-technical losses in the utilities, a methodology based on analyzing consumers’
2019 behavior with the contribution of supervised learning methods on real-time smart meter data [29]
was discussed
A sparse representation approach was presented to reduce voluminous smart meter data and
2017 [30]
further extract unseen patterns in energy consumption data
Smart home
A methodical literature review was performed to study the characteristics, applications, and
2021 reasoning [31]
challenges of smart home reasoning systems
systems
Smart
2020 A CNN model was implemented to calculate building occupancy in real-time by using AMI data [32]
buildings
A study was performed to learn the developments and the challenges involved in the
2021 [33]
implementation of DL technologies in smart grid systems
Two models, viz. sampling approach and AlexNet, were realized to detect the abstraction of
2021 [34]
electricity in smart grids
A thorough survey was performed on the implementation of blockchain technology in smart
2021 [35]
grids to secure them from the cyber world
Smart grids A comprehensive survey was conducted to learn the opportunities, developments, and future
2020 [36]
directions of smart grid technologies
The significance and difficulties of big-data analytics in forthcoming power grids were
2020 [37]
discoursed to guide the researchers
2019 A detailed survey was performed on the problems of privacy and security in smart grid networks [38]
A wide-ranging survey was performed on the security issues of AMI and key safeguarding
2019 [39]
mechanisms in the smart grid
Renewable
A broad review was carried out on the practice of AI in assimilating variable renewable energy
2021 energy [40]
resources with the power systems by reducing the integration costs
integration

1.1. Problem Statement and Rationale of the Proposed Work

Although indexing databases such as Scopus, Web of Science (WOS), EndNote, etc.,
can provide analysis of bibliometric data to some extent, users need to subscribe in order
to gain access. So, individual researchers cannot access the full functionalities of these
databases without a proper license/subscription. Moreover, novice researchers may not
have sufficient knowledge of what to search in these databases related to bibliometric
data on a particular research area. Further, to the best of the authors’ knowledge, this
kind of comprehensive bibliometric analysis on smart grids with respect to identifying key
concepts and research trends is not available in the literature. Thus, being an open-access
article, this paper can be a one-stop solution that can be easily accessible to all researchers
to systematically acquire the crucial information on smart grid literature.
Electricity 2024, 5 78

1.2. Contribution of the Paper

The fundamental objective of this paper is to perform a comprehensive bibliometric
analysis of smart grids, thereby highlighting key concepts and trends related to smart grid
research. By doing this, this paper aims to provide all the key information required for
novice smart grid researchers in one place. This includes emerging research areas and
keywords, high-impact journals, most-cited publications, top authors and their collabora-
tions, most-published affiliating institutions, most-cited countries, and annual production
of various countries and their collaborations. This proposed work is carried out through a
bibliometric analysis approach. It is a quantitative and systematic method used to measure
and assess various aspects of scientific literature and derive valuable insights such as
research impacts, identifying trends, appraising individual/institutional performance, etc.,
that greatly helps upcoming research studies.
Based on the above-mentioned objective of this paper and the literature given in
Table 1, the desired outcomes are derived by framing suitable research questions (RQs)
given as follows. These help in the synthesis of previous research findings by the researchers
and in identifying further research advancements. The primary advantage of answering
these RQs is that, from a vast amount of bibliometric data, it discovers influential studies,
journals, authors, institutions, and countries in the examined area over a period of time.
1. RQ1: What are the important keywords relevant to smart grid research?
2. RQ2: Which keywords are mostly used and how do they co-occur with other keywords?
3. RQ3: What is the annual production of articles from 2017 to 2022?
4. RQ4: Which are the most relevant sources in smart grid research?
5. RQ5: Which authors have published the most relevant articles on this topic?
6. RQ6: Which are the most relevant and impactful affiliations?
7. RQ7: How many annual country-collaborated publications are present in terms of
country, affiliation, and author?
8. RQ8: What is the country-wise scientific production in smart grid research?
9. RQ9: What are the most cited countries in smart grid research?
10. RQ10: Which countries conducted and collaborated on the most relevant studies on
smart grids?
11. RQ11: What are the most cited global documents in smart grid research?
12. RQ12: Which keywords are mostly used and how they are related to other factors
(authors, countries, etc.)?
13. RQ13: What are the themes identified from smart grid research?
14. RQ14: Which are the most popular topics explored and how they are related to other
topics in different clusters?
The implementation of this analysis is carried out using the library “biblioshiny()”
which is available under the bibliometrix package in R. The detailed steps involved in
this bibliometric analysis are discussed in the methodology section. The other sections of
the paper are ordered as follows: Section 2 designates the proposed methodology and its
implementation, Section 3 presents the comprehensive results that are obtained through the
conducted bibliometric analysis, and finally Section 4 summarizes the overall observations
and provides the conclusions of this study.

2. Methodology
To better comprehend the importance of smart grids, a systematic literature review
on smart grids was conducted in the Scopus and WOS databases utilizing the “preferred
reporting items for systematic reviews and meta-analyses (PRISMA)” model as shown
in Figure 1. For this, a search was undertaken using the keywords, viz. energy con-
sumption readings, energy consumption data, power consumption data, power consump-
tion readings, smart building data, smart meter data, smart meter readings, and smart
home data.
 Electricity2024,
Electricity 2024,5 5, FOR PEER REVIEW 5
79

Criteria for extracting records from Scopus and Web of Science (WOS) databases – PRISMA Model

Records identified before screening

Criteria Scopus WOS
Year 2017 – 2022 2017 – 2022
(n = 9014) (n = 1837)
Subject Engineering
area Computer Science
Energy --
Multidisciplinary
(n = 8135)
Document Article, Article, Proceedings Paper, Review Articles,
type Conference Paper, Book Chapters (n = 951)
Identification

Records identified from

Scopus database Review Paper,
(n = 11,600) Book Chapter,
WOS database Conference
(n = 2846)
Review (n = 8074)
WOS Engineering Electrical Electronic
categories Computer Science Information Systems
Computer Science Interdisciplinary Applications
--
Computer Science Artificial Intelligence
Engineering Multidisciplinary
(n = 951)
Language English (n = 7775) English (n = 949)
Considered Out of 7775 All 949 records are considered for further
records records, the first screening
2000 records are
considered for
further screening

Duplicate records excluded

(n = 420, Remaining Records = 2529)
Screening

Records screened Records excluded by Title

(n = 2949) (n = 1426, Remaining Records = 1103)

Records excluded by Abstract

(n = 847, Remaining Records = 256)

Records excluded based on the unavailability of Full-text

Eligibility

Records assessed for

(n = 46, Remaining Records = 210)
eligibility
Records excluded based on the assessment of Full-text
(n = 256)
(n = 170, Remaining Records = 40)
Included

Records included for

the literature review
(n = 40)

Figure 1. PRISMA model for extracting records from Scopus and WOS databases.
Electricity 2024, 5, FOR PEER REVIEW 6

Electricity 2024, 5 80
Figure 1. PRISMA model for extracting records from Scopus and WOS databases.

The
The search
searchprocess
processfor forthe
theproposed
proposed bibliometric
bibliometricanalysis is given
analysis in Figure
is given 2. Further,
in Figure 2. Fur-
the
ther,detailed research
the detailed stepssteps
research and and
flowflowfor for
extraction of of
extraction records
records from
fromthetheScopus/WOS
Scopus/WOS
databases
databases areare shown
shown in in Figure
Figure 3.3. In
In the
the identification
identification stage,
stage, the
the initial
initial search
search extracted
extracted
11,600
11,600 and 2846 records from Scopus and WOS databases, respectively. Several criteria
and 2846 records from Scopus and WOS databases, respectively. Several criteria
such
such asasyear,
year,subject
subjectarea,
area,document
document type, WOS
type, categories,
WOS source
categories, type,type,
source and language were
and language
considered for reducing the number of extracted records. The number
were considered for reducing the number of extracted records. The number of records of records extracted
based on the
extracted considered
based criteria was
on the considered 2000was
criteria out2000
of 7775 from
out of 7775Scopus and 949and
from Scopus from949WOS.
from
These
WOS. records were merged
These records from both
were merged from Scopus and WOS
both Scopus andfiles
WOS [41].
files [41].

Start

Topic selection

Define research objectives

Keyword selection

Existing Decide selection criteria

literature

Selection of database Scopus

and WoS

Data collection

No Selection
Excluded criteria
satisfied?

Yes

Selected for inclusion

Data extraction and

merging

Import data to Bibliometrix

Results visualization and

interpretation

Stop Conclusion

Figure 2. Research steps in the proposed bibliometric analysis.

 Electricity 2024, 5, FOR PEER REVIEW 7

Electricity 2024, 5 81
Figure 2. Research steps in the proposed bibliometric analysis.

Start

ALL ("energy consumption readings" OR

"energy consumption data" OR
"power consumption data" OR
"power consumption readings" OR Search
"smart building data" OR Keywords
"smart meter data" OR
"smart meter readings" OR
"smart home data")

AND

(LIMIT-TO (PUBYEAR, 2022) OR Selection Criteria to Extract Bibliometric Data

(LIMIT-TO (DOCTYPE, "ar")
LIMIT-TO (PUBYEAR, 2021) OR
OR LIMIT-TO (DOCTYPE, "cp")
LIMIT-TO (PUBYEAR, 2020) OR (LIMIT-TO (SUBJAREA, "ENGI") OR
(LIMIT-TO OR LIMIT-TO (DOCTYPE, "re")
LIMIT-TO (PUBYEAR, 2019) OR LIMIT-TO (SUBJAREA, "COMP") OR
(LANGUAGE, OR LIMIT-TO (DOCTYPE, "ch")
LIMIT-TO (PUBYEAR, 2018) OR LIMIT-TO (SUBJAREA, "ENER") OR
"English")) OR LIMIT-TO (DOCTYPE, "cr"))
LIMIT-TO (PUBYEAR, 2017)) LIMIT-TO (SUBJAREA, "MULT"))

AND

Obtained bibliometric data after

selection criteria

Export the bibliometric data

using “.bib” file format

Stop

Figure3.3.Proposed
Figure Proposed search
search process.
process.

Therecords
The records (2949)
(2949) extracted
extracted in the identification stagestage were
werefurther
furtherscrutinized
scrutinizedininthe the
screening stage.
screening stage. During
During this screening, duplicate
duplicate records
records are
are observed.
observed. Here,
Here,duplicate
duplicate
records refer to
records to the
thearticles
articlesrecorded
recorded in in
oneone
database
database thatthat
are also
are recorded in twoinortwo
also recorded moreor
databases.
more The duplicate
databases. records
The duplicate (420) were
records (420) identified and excluded;
were identified this resulted
and excluded; in 2529in
this resulted
unique
2529 records.
unique records.
After reading
After reading the the titles,
titles, 1426 records werewere found
found to to be
be irrelevant
irrelevanttotothethestudy
studyand and
theywere
they wereexcluded.
excluded. This resulted in 1103 records. records. After
Afterreading
readingthe theabstract,
abstract,847
847records
records
werefound
were foundto to be
be irrelevant
irrelevant and they were were excluded.
excluded.This Thisresulted
resultedinin256
256records.
records.Further,
Further,
theeligibility
the eligibility of these 256 records
records was
wasverified
verifiedininthe
theeligibility
eligibilitystage
stagebased
based onon
thetheavaila-
avail-
bility and assessment of the full text. Out of 256 records, 46 records were
ability and assessment of the full text. Out of 256 records, 46 records were found to have found to have
unavailabilityof
unavailability of full
full text
text and they were excluded.
excluded. After
Afterscrutinizing
scrutinizingthetheavailable
availablefullfulltext,
text,
170records
170 recordswere
werefound
foundto to be
be ineligible for the study and and they
theywere
wereexcluded.
excluded.By Byfollowing
following
theabove
the abovecriteria,
criteria, 4040 records
records were
were eligible and included in the literature
literature review.
review.

Extraction
ExtractionofofTopic-Wise
Topic-WiseTitle
TitleCount
Countfrom
fromMerged
Merged Dataset
Dataset
The extraction
The extraction of topic-wise titles and their countisisvital
titles and their count vitalto
tounderstanding
understandingthe theim-
im-
portance
portanceofofthe
the topic
topic in
in smart
smart grid research. The
The process
process starts
startswith
withthe
theinstallation
installationofofthe
the
required packages (readxl, stringr) and the loading of the respective libraries. After this,
the reading of the merged dataset (records included from Scopus and WOS) is executed
Electricity 2024, 5 82

and it is saved into an object. Then the columns in the dataset are observed and the titles
column is selected for extracting the titles by using string-matching. The extracted titles
are saved into a CSV file for further processing. Finally, the count of topic-wise titles is
retrieved and printed. The complete steps of this extraction process are given in Table 2.

Table 2. Extraction process for finding the topic-wise title count from the merged dataset.

Extraction Process Steps

1. Install essential packages (readxl, stringr) and load the libraries to read the Excel file and
perform string-matching
2. Read the merged dataset that contains records from both Scopus and Web of Science and
store them in “object name” using
object name ← read_excel (“path of the excel file”)
3. Read the column names from the “object name” using
colnames (object name)
4. Extract the titles from the “object name” based on the string-matching and save them in the
object “titles”. This is performed using
titles ← object name %>%
filter (str_detect (column name of the title, “title name to be matched|title name to be matched
|. . ..| title name to be matched”))
5. Store the details of extracted titles in a CSV file using
write_csv (titles, “path of the file name to be saved”)
6. Calculate the count of titles and store it in the object “count of titles” using
count of titles ← nrow (titles)
7. Print the object “count of titles” for the count using
print (count of titles)

3. Results and Discussions

The word cloud of the keywords used in literary works is shown in Figure 4. From
this figure, it is evident that the keywords “smart grid”, “machine learning”, “smart meter”,
“deep learning”, “smart meters”, and “energy consumption” are very frequently used
in the smart grid research work (RQ1). Because the size of the appearance denotes how
frequently these keywords are used, if the size of the keyword is big then it represents that
the keyword is frequent, and if the size of the keyword is small then it represents the little
usage of that keyword.
The co-occurrence network of the keywords is shown in Figure 5. From this, the impact
of the keywords and their co-occurrence with the other keywords can be understood. It
is observed that there is a strong connection between the keywords “smart grid” and
“smart meter”. There is another keyword named “machine learning”, which has a high
impact similar to the keyword “smart grid”. It can also be noticed that the keywords “deep
learning”, “energy consumption”, etc., have co-occurrence with the keywords “smart grid”
and “smart meter” (RQ2). The annual scientific production of the works on the smart grids
has increased year by year as shown in Figure 6. From this, it is evident that from the years
2017 to 2022, there is a gradual increment in the production of articles. However, from the
year 2021, there is a drastic change in the annual production, and the highest production of
articles (1150) is observed in the year 2021 and the next highest production of articles (925)
is in 2022 (RQ3).
The top 10 journals that have published research works on smart grids are shown
in Figure 7. From this, it is observed that the journal Energies is in the top place with
132 documents. The journal IEEE Transactions on Smart Grid is in second place with
102 documents published on smart grid research work (RQ4).
The top 10 most relevant authors that have the highest number of documents on smart
grid research work are identified and presented in Figure 8. From this, it is evident that
Electricity 2024, 5 83

Wang Y has published 52 documents (RQ5). The top 10 affiliations that have published
research works on smart grids are shown in Figure 9. From this, it is revealed that Tsinghua
University has published the highest number of articles (57) on smart grid research work.
Electricity2024,
Electricity 2024,5,5,FOR
FORPEER
PEERREVIEW
REVIEW
The North China Electric Power University is in second place with 44 articles published on99
smart grid research work (RQ6).

Figure4.4.Important
Figure Important keywords
keywords related
related to
to smart
smart grid
grid research.
research.
Figure 4. Important keywords related to smart grid research.

Figure 5. Keyword co-occurrence network.

Figure 5. Keyword co-occurrence network.
Figure 5. Keyword co-occurrence network.
 Year
2017 2018 2019 2020 2021 2022
Figure 6. Annual production of articles from 2017 to 2022.

Electricity 2024,
Electricity 5,5FOR PEER REVIEW
2024, The top 10 journals that have published research works on smart grids are shown in84 10
Figure 7. From this, it is observed that the journal Energies is in the top place with 132
documents. The journal IEEE Transactions on Smart Grid is in second place with 102 docu-
ments
1400published on smart grid research work (RQ4).
The top 10 most relevant authors that have1150 the highest number of documents on
1200

Number of Articles
smart grid research work are identified and presented in Figure 8. From this, it is evident
Wang Y has published 52 documents (RQ5). The top925
that1000 10 affiliations that have pub-
lished research
800 works on smart grids are shown in Figure 9. From this, it is revealed that
Tsinghua University has published the highest number of articles (57) on smart grid re-
search600work. The North China Electric Power University is in second place with 44 articles
400 on smart grid research work (RQ6).
published
The distributions of annual
200 109 collaborated
126 140publications from the viewpoints of country,
75
affiliation, and author are presented in Figure 10. The top nine countries were selected for
0
collaboration analysis. This analysis reveals that the highest number of single-country
publications and the highest numberYear of multiple-country collaborative publications on
smart grids are2017
from 2018 2019second-highest
China. The 2020 2021number2022of single-country publications
are from the USA. The second-highest number of multiple-country collaborative publica-
Figure6.6.Annual
Figure Annualproduction
production of articles
of articles fromfrom
2017 2017 to 2022.
to 2022.
tions are from the United Kingdom (RQ7).

The top 10 journals that have published research works on smart grids are shown in
Figure 7. From this, it is observed that the journal Energies is in the top place with 132
documents. The journal IEEE Transactions on Smart Grid is in second place with 102 docu-
ments published on smart grid research work (RQ4).
The top 10 most relevant authors that have the highest number of documents on
smart grid research work are identified and presented in Figure 8. From this, it is evident
that Wang Y has published 52 documents (RQ5). The top 10 affiliations that have pub-
lished research works on smart grids are shown in Figure 9. From this, it is revealed that
Tsinghua University has published the highest number of articles (57) on smart grid re-
search work. The North China Electric Power University is in second place with 44 articles
published on smart grid research work (RQ6).
The distributions of annual collaborated publications from the viewpoints of country
affiliation, and author are presented in Figure 10. The top nine countries were selected for
collaboration analysis. This analysis reveals that the highest number of single-country
Electricity 2024, 5, FOR PEER REVIEWpublications and the highest number of multiple-country collaborative publications 11 on
Figure 7.
smart
Figure 7. Most relevant
grids
Most are from
relevant sources in
in smart
China.
sources Thegrid
smart research.
second-highest
grid research. number of single-country publications
are from the USA. The second-highest number of multiple-country collaborative publica-
tions are from the United Kingdom (RQ7).

Figure
Figure 8.
8. Most
Most relevant
relevant authors
authors in
in smart
smart grid
grid research.
research.

Figure 7. Most relevant sources in smart grid research.

Electricity 2024, 5 85

Figure 8. Most relevant authors in smart grid research.

Figure 8. Most relevant authors in smart grid research.

Figure 9. Most relevant affiliations in smart grid research.

Figure 9. Most relevant affiliations in smart grid research.

The distributions of annual collaborated publications from the viewpoints of country,

affiliation, and author are presented in Figure 10. The top nine countries were selected
for collaboration analysis. This analysis reveals that the highest number of single-country
publications and the highest number of multiple-country collaborative publications on
smart grids are from China. The second-highest number of single-country publications are
from the USA. The second-highest number of multiple-country collaborative publications
are from the United Kingdom (RQ7).
Figure 9. Most relevant affiliations in smart grid research.

Figure 10. Corresponding author countries (multiple-country and single-country publications) in

smart grid research.

The country-wise scientific production of the research articles on smart grids is pre-
sented in Figure 11. The top 10 countries are included in the figure. From this tree plot, it is
confirmed that China has the highest production of research articles (1423). The second
highest number of research articles is produced by the USA (RQ8). The box that has a
larger area represents the highest production of the research articles and the box that has a
smaller area represents the least production of the research articles on smart grids.
 a smaller area represents the least production of the research articles on smart grids.
Figure 10. Corresponding author countries (multiple-country and single-country publications) in
The top 10 countries that have the most cited research articles are presented in Figure
smart grid research.
12. In this figure, the citation count is on the x-axis and the countries’ details are on the y-
axis. Further, it is noticed
The country-wise that China
scientific has theofhighest
production number
the research of citations
articles on smart (3020)
grids isamong
pre- all
Electricity 2024, 5 countries
sented inproducing smart
Figure 11. The topgrid researchare
10 countries work. Australia
included is the From
in the figure. second
thiscountry
tree plot,that
it has
86
theishighest
confirmed that China
number has the highest
of citations production
after China (RQ9).of research articles (1423). The second
highest number of research articles is produced by the USA (RQ8). The box that has a
larger area represents the highest production of the research articles and the box that has
a smaller area represents the least production of the research articles on smart grids.
The top 10 countries that have the most cited research articles are presented in Figure
12. In this figure, the citation count is on the x-axis and the countries’ details are on the y-
axis. Further, it is noticed that China has the highest number of citations (3020) among all
countries producing smart grid research work. Australia is the second country that has
the highest number of citations after China (RQ9).

Figure 11.11.
Figure Country-wise
Country-wisescientific productionininsmart
scientific production smart grid
grid research.
research.

The top 10 countries that have the most cited research articles are presented in Figure 12.
In this figure, the citation count is on the x-axis and the countries’ details are on the y-
axis. Further, it is noticed that China has the highest number of citations (3020) among all
countries producing smart grid research work. Australia is the second country that has the
highest number of citations after China (RQ9).
Figure 11. Country-wise scientific production in smart grid research.

Figure 12. Most cited countries in smart grid research.

A network analysis of the countries that have collaborated to do research on smart

grid areas is shown in Figure 13. The majority of research collaboration comes from China
Figure 12. Most cited countries in smart grid research.
Figure 12. Most cited countries in smart grid research.

A
A network
network analysis
analysis of
of the
the countries
countries that
that have
have collaborated
collaborated to
to do
do research
research on
on smart
smart
grid
grid areas
areas is
is shown
shown in
in Figure
Figure 13.
13. The
The majority
majority of
of research
research collaboration
collaboration comes
comes from
from China
China
with various countries. It is also observed that some countries such as India have very little
collaboration on smart grid research (RQ10).
 country (middle), and author keyword (right) is shown in Figure 15. The analysis estab-
lished which smart grid-related keywords have been used most frequently by different
authors from different countries. The study of the top authors, countries, and keywords
indicated that there are keywords, i.e., “smart grid”, “machine learning”, “smart me-
Electricity 2024, 5 ter(s)”, “deep learning”, “energy consumption” and “energy efficiency”, and the authors
87
Wang Y., Zhang X., Li Y., Li J., Wang X., et al., mainly used these keywords, and published
their articles in the countries China, USA, and India (RQ12).

Figure 13. Country collaboration network in smart grid research.

Figure 13. Country collaboration network in smart grid research.

The top 10 documents that have the highest global citations on the research work
related to smart grids are shown in Figure 14. From this, it is revealed that the documents
from Tsinghua University published by IEEE Transactions on Smart Grid make up the highest
number of most-cited articles (57) on smart grid research work. North China Electric Power
University is in the second position with 44 articles published on smart grid research work
Electricity 2024, 5, FOR PEER REVIEW 14
(RQ11).

Figure 14. Most cited global documents in smart grid-related research.

Figure 14. Most cited global documents in smart grid-related research.

Based
The on Callon’s
research in smarttwo
gridmeasures—centrality andbetween
literature on the relation density—research themes
author (left), authorarecoun-
mapped with four quadrants called niche themes, motor themes, basic
try (middle), and author keyword (right) is shown in Figure 15. The analysis established themes, and
emerging
which smart orgrid-related
declining themes as shown
keywords haveinbeen
Figure
used16.most
Fromfrequently
this, it is identified thatauthors
by different the
motor themes (upper-right) quadrant includes themes such as smart grid, smart
from different countries. The study of the top authors, countries, and keywords indicated meter(s),
demand
that thereresponse, and clustering.
are keywords, These
i.e., “smart themes
grid”, are welllearning”,
“machine developed“smart
and vital in the smart
meter(s)”, “deep
grid research field. Themes in the niche (upper-left) quadrant are COVID-19, simulation,
learning”, “energy consumption” and “energy efficiency”, and the authors Wang Y., Zhang
climate change, cluster analysis, and residential buildings, and are of marginal im-
X., Li Y., Li J., Wang X., et al., mainly used these keywords, and published their articles in
portance. Themes in the lower-left quadrant are emerging or declining themes. Further, it
the countries China, USA, and India (RQ12).
is evident that themes such as deep learning, energy consumption, non-intrusive load
monitoring, smart metering, and time series are emerging themes in this field. In the
lower-right quadrant, machine learning, energy efficiency, the Internet of Things, anom-
aly detection, and big data are identified as basic themes in the smart grid research area
(RQ13).
 climate change, cluster analysis, and residential buildings, and are of marginal im-
portance. Themes in the lower-left quadrant are emerging or declining themes. Further, it
is evident that themes such as deep learning, energy consumption, non-intrusive load
monitoring, smart metering, and time series are emerging themes in this field. In the
lower-right quadrant, machine learning, energy efficiency, the Internet of Things, anom-
Electricity 2024, 5
aly detection, and big data are identified as basic themes in the smart grid research area 88
(RQ13).

Figure 15. Relation between author, country, and keyword.

Figure 15. Relation between author, country, and keyword.

Based on Callon’s two measures—centrality and density—research themes are mapped

with four quadrants called niche themes, motor themes, basic themes, and emerging
or declining themes as shown in Figure 16. From this, it is identified that the motor
themes (upper-right) quadrant includes themes such as smart grid, smart meter(s), demand
response, and clustering. These themes are well developed and vital in the smart grid
research field. Themes in the niche (upper-left) quadrant are COVID-19, simulation, climate
change, cluster analysis, and residential buildings, and are of marginal importance. Themes
in the lower-left quadrant are emerging or declining themes. Further, it is evident that
themes such as deep learning, energy consumption, non-intrusive load monitoring, smart
metering, and time series are emerging themes in this field. In the lower-right quadrant,
machine learning, energy efficiency, the Internet of Things, anomaly detection, and big15data
Electricity 2024, 5, FOR PEER REVIEW
are identified as basic themes in the smart grid research area (RQ13).

Figure 16.
Figure 16. Themes
Themes in
in smart
smartgrid
gridresearch.
research.

The factor analysis, in other words the topic dendrogram, is shown in Figure 17. In
this figure, twenty topics are considered and all the topics are clustered with the appro-
priate topic. These clusters show the relativeness between the topics, which is useful for
understanding the topic’s importance and collaboration (RQ14). The data points of the
topics can be seen on the x-axis to make the clusters, and the distance between clusters
can be seen on the y-axis. The point to be noted concerning the factorial analysis is that
the number of topics can be increased as per the requirement, but here a few topics are
Electricity 2024, 5 89
Figure 16. Themes in smart grid research.

The factor analysis, in other words the topic dendrogram, is shown in Figure 17. In
The factor
this figure, twenty analysis, in other
topics are words and
considered the topic
all thedendrogram, is shownwith
topics are clustered in Figure 17. In
the appro-
this figure,
priate topic. twenty topics are
These clusters showconsidered and all between
the relativeness the topics
thearetopics,
clustered
which with the appro-
is useful for
priate topic. These clusters show the relativeness between the topics, which
understanding the topic’s importance and collaboration (RQ14). The data points of the is useful for
understanding the topic’s importance and collaboration (RQ14). The data
topics can be seen on the x-axis to make the clusters, and the distance between clusters points of the
topics
can can be
be seen on seen on theThe
the y-axis. x-axis to make
point to be the
notedclusters, and the
concerning thedistance
factorialbetween
analysis clusters
is that
can be seen on the y-axis. The point to be noted concerning the
the number of topics can be increased as per the requirement, but here a few factorial analysis
topicsis are
that
the number of
demonstrated fortopics can be increased
understanding as per the requirement, but here a few topics are
purposes.
demonstrated for understanding purposes.

Figure17.
Figure Clusteringofofrelevant
17.Clustering relevanttopics.
topics.

The topic-wise title count is given in Table 3. From this table, it is perceived that the
The topic-wise title count is given in Table 3. From this table, it is perceived that the
highest count (190) is identified with the keyword “Energy Consumption” and the next
highest count (190) is identified with the keyword “Energy Consumption” and the next
highest count (107) is identified with the keywords “Smart Meter Readings|Smart Meter
highest count (107) is identified with the keywords “Smart Meter Readings|Smart Meter
Data” in the titles. Further, the total of topic-wise titles with various keywords is identified
Data” in the titles. Further, the total of topic-wise titles with various keywords is identified
as 828 in the considered bibliometric data.
as 828 in the considered bibliometric data.
Table 3. Topic-wise titles count.

Topics Titles Count

Energy Consumption Readings |Energy Consumption Data 26
Power Consumption Readings|Power Consumption Data 12
Smart Meter Readings|Smart Meter Data 107
Smart Home Data 10
Smart Building Data 1
Smart Grid Data 4
Big Data|Data Analytics|Big Data Analytics|Data Analysis 69
Machine Learning 92
Data Issues|Data Anomalies|Abnormal| Abnormalities|Abnormal
19
Behavior|Abnormal Behavior
Energy Consumption Behavior|Energy Consumption Behavior|Consumer
1
Behavior|Customer Behavior|Consumer Behavior|Customer Behavior
Energy Consumption 190
Forecasting Energy Consumption 2
Energy Sharing|Energy-Sharing 1
Appliance Scheduling 1
Load Scheduling 4
Load Forecasting 106
Load Profiling 5
Internet of Things|IoT|IoT 48
Non-Intrusive Load Monitoring|NILM 42
Electricity 2024, 5 90

Table 3. Cont.

Topics Titles Count

Smart Home Technologies 1
Communication 31
Electricity Theft 39
Renewable Energy 17
Total Count 828

4. Conclusions
The presented comprehensive bibliometric analysis has provided key information
that is beneficial for future researchers of the smart grid domain. From the results and
analysis, it is found that all the RQs are successfully solved and obtained precise results.
The summary of these key findings in various aspects is given as follows.
 Various themes/topics namely smart meter(s), demand response, clustering, resi-
dential buildings, machine learning, energy efficiency, Internet of Things, anomaly
detection, big-data analytics, deep learning, energy consumption, and non-intrusive
load monitoring are identified as key topics in smart grids research. It is also found
that there is a strong connection between the keywords “smart grid”, “smart me-
ter”, and “machine learning”. This implies that the selection of research topics as
a combination of these keywords can lead to successful work. Further, the highest
topic-wise title count (190) is identified with the keyword “energy consumption” and
the next highest count (107) is identified with the keywords “smart meter readings”
and “smart meter data” in the titles.
 There is a gradual increment in the production of articles from 2017 to 2022. However,
from the year 2021, there is a drastic change in the annual production with the highest
production of articles (1150) found in the year 2021 and the next highest production of
articles (925) found in 2022. This showcases the recent growing importance of smart
grid research. Further, most of these research works are published in journals namely
Energies and IEEE Transactions on Smart Grid with the numbers of documents being
132 and 102, respectively.
 The most relevant authors identified from the analysis are Wang Y and Li Y. Similarly,
the affiliations “Tsinghua University” and “North China Electric Power University”
are identified as the most impactful affiliations with the number of articles 57 and
44, respectively.
 This analysis reveals that China and the USA have occupied the top two positions
with the production of articles, the numbers being 1423 and 566, respectively. More-
over, the highest number of single-country publications and the highest number
of multiple-country collaborative publications on smart grids are from China. The
second-highest number of single-country publications are from the USA. The second-
highest number of multiple-country collaborative publications are from the United
Kingdom. Moreover, the top two positions concerned with having the most citations
are occupied by China (3020) and Australia (1279), respectively.
Thus, in summary, conducting bibliometric analysis on smart grids provides a sys-
tematic and data-driven approach to understanding the current state of research, and
identifying key concepts and research trends. It plays a crucial role in advancing knowl-
edge, guiding research efforts, and supporting evidence-based decision-making in the field
of smart grids.

Author Contributions: Conceptualization, K.P.P.; methodology, Y.V.P.K., K.H. and G.P.R.; software,
K.H.; formal analysis, G.P.R.; funding acquisition, Y.V.P.K.; investigation, K.P.P.; resources, K.H.; data
curation, Y.V.P.K., K.H. and G.P.R.; supervision, Y.V.P.K.; validation, Y.V.P.K.; visualization, K.H.;
project administration, Y.V.P.K.; writing—original draft, K.P.P.; writing—review and editing, Y.V.P.K.
and G.P.R. All authors have read and agreed to the published version of the manuscript.
Electricity 2024, 5 91

Funding: This research received no external funding.

Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

References
1. Purna Prakash, K.; Pavan Kumar, Y.V.; Reddy, C.P.; Pradeep, D.J.; Flah, A.; Alzaed, A.N.; Al Ahamdi, A.A.; Ghoneim, S.S.M. A
Comprehensive Analytical Exploration and Customer Behaviour Analysis of Smart Home Energy Consumption Data with a
Practical Case Study. Energy Rep. 2022, 8, 9081–9093. [CrossRef]
2. Purna Prakash, K.; Pavan Kumar, Y.V. Systematic Statistical Analysis to Ascertain the Missing Data Patterns in Energy Consump-
tion Data of Smart Homes. Int. J. Renew. Energy Res. 2022, 12, 1560–1573. [CrossRef]
3. Kasaraneni, P.P.; Yellapragada Venkata, P.K. Analytical Approach to Exploring the Missing Data Behavior in Smart Home Energy
Consumption Dataset. J. Renew. Energy Environ. 2022, 9, 37–48. [CrossRef]
4. Purna Prakash, K.; Pavan Kumar, V.V. A Systematic Approach for Exploration, Behavior Analysis, and Visualization of Redundant
Data Anomalies in Smart Home Energy Consumption Dataset. Int. J. Renew. Energy Res. 2022, 12, 109–123. [CrossRef]
5. Purna Prakash, K.; Pavan Kumar, Y.V. Exploration of Anomalous Tracing of Records in Smart Home Energy Consumption
Dataset. ECS Trans. 2022, 107, 18271–18280. [CrossRef]
6. Kasaraneni, P.P.; Yellapragada, V.P.K.; Moganti, G.L.K.; Flah, A. Analytical Enumeration of Redundant Data Anomalies in Energy
Consumption Readings of Smart Buildings with a Case Study of Darmstadt Smart City in Germany. Sustainability 2022, 14, 10842.
[CrossRef]
7. Kasaraneni, P.P.; Venkata Pavan Kumar, Y.; Moganti, G.L.K.; Kannan, R. Machine Learning-Based Ensemble Classifiers for
Anomaly Handling in Smart Home Energy Consumption Data. Sensors 2022, 22, 9323. [CrossRef] [PubMed]
8. Purna Prakash, K.; Pavan Kumar, Y.V. Simple and Effective Descriptive Analysis of Missing Data Anomalies in Smart Home
Energy Consumption Readings. J. Energy Syst. 2021, 5, 199–220. [CrossRef]
9. Liu, X.; Ding, Y.; Tang, H.; Xiao, F. A Data Mining-Based Framework for the Identification of Daily Electricity Usage Patterns and
Anomaly Detection in Building Electricity Consumption Data. Energy Build. 2021, 231, 110601. [CrossRef]
10. Afzalan, M.; Jazizadeh, F. A Machine Learning Framework to Infer Time-of-Use of Flexible Loads: Resident Behavior Learning
for Demand Response. IEEE Access 2020, 8, 111718–111730. [CrossRef]
11. Syed, D.; Abu-Rub, H.; Ghrayeb, A.; Refaat, S.S. Household-Level Energy Forecasting in Smart Buildings Using a Novel Hybrid
Deep Learning Model. IEEE Access 2021, 9, 33498–33511. [CrossRef]
12. Kim, T.-Y.; Cho, S.-B. Predicting Residential Energy Consumption Using CNN-LSTM Neural Networks. Energy 2019, 182, 72–81.
[CrossRef]
13. Wei, N.; Li, C.; Peng, X.; Zeng, F.; Lu, X. Conventional Models and Artificial Intelligence-Based Models for Energy Consumption
Forecasting: A Review. J. Pet. Sci. Eng. 2019, 181, 106187. [CrossRef]
14. Fekri, M.N.; Patel, H.; Grolinger, K.; Sharma, V. Deep Learning for Load Forecasting with Smart Meter Data: Online Adaptive
Recurrent Neural Network. Appl. Energy 2021, 282, 116177. [CrossRef]
15. Shaukat, M.A.; Shaukat, H.R.; Qadir, Z.; Munawar, H.S.; Kouzani, A.Z.; Mahmud, M.A.P. Cluster Analysis and Model Comparison
Using Smart Meter Data. Sensors 2021, 21, 3157. [CrossRef]
16. Khan, Z.A.; Jayaweera, D. Smart Meter Data Based Load Forecasting and Demand Side Management in Distribution Networks
with Embedded PV Systems. IEEE Access 2020, 8, 2631–2644. [CrossRef]
17. Kong, W.; Dong, Z.Y.; Jia, Y.; Hill, D.J.; Xu, Y.; Zhang, Y. Short-Term Residential Load Forecasting Based on LSTM Recurrent
Neural Network. IEEE Trans. Smart Grid 2019, 10, 841–851. [CrossRef]
18. Shi, H.; Xu, M.; Li, R. Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN. IEEE Trans. Smart Grid 2018,
9, 5271–5280. [CrossRef]
19. Avancini, D.B.; Rodrigues, J.J.P.C.; Rabêlo, R.A.L.; Das, A.K.; Kozlov, S.; Solic, P. A New IoT-based Smart Energy Meter for Smart
Grids. Int. J. Energy Res. 2021, 45, 189–202. [CrossRef]
20. Al-Turjman, F.; Abujubbeh, M. IoT-Enabled Smart Grid via SM: An Overview. Future Gener. Comput. Syst. 2019, 96, 579–590.
[CrossRef]
21. Cimen, H.; Cetinkaya, N.; Vasquez, J.C.; Guerrero, J.M. A Microgrid Energy Management System Based on Non-Intrusive Load
Monitoring via Multitask Learning. IEEE Trans. Smart Grid 2021, 12, 977–987. [CrossRef]
22. Welikala, S.; Dinesh, C.; Ekanayake, M.P.B.; Godaliyadda, R.I.; Ekanayake, J. Incorporating Appliance Usage Patterns for
Non-Intrusive Load Monitoring and Load Forecasting. IEEE Trans. Smart Grid 2019, 10, 448–461. [CrossRef]
23. Wang, A.L.; Chen, B.X.; Wang, C.G.; Hua, D. Non-Intrusive Load Monitoring Algorithm Based on Features of V–I Trajectory.
Electr. Power Syst. Res. 2018, 157, 134–144. [CrossRef]
24. Kong, W.; Dong, Z.Y.; Ma, J.; Hill, D.J.; Zhao, J.; Luo, F. An Extensible Approach for Non-Intrusive Load Disaggregation with
Smart Meter Data. IEEE Trans. Smart Grid 2018, 9, 3362–3372. [CrossRef]
Electricity 2024, 5 92

25. Elsisi, M.; Mahmoud, K.; Lehtonen, M.; Darwish, M.M.F. Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing,
Monitoring, and Securing Smart Meters. Sensors 2021, 21, 487. [CrossRef]
26. Chakraborty, S.; Das, S.; Sidhu, T.; Siva, A.K. Smart Meters for Enhancing Protection and Monitoring Functions in Emerging
Distribution Systems. Int. J. Electr. Power Energy Syst. 2021, 127, 106626. [CrossRef]
27. Brown, J.; Abate, A.; Rogers, A. Disaggregation of Household Solar Energy Generation Using Censored Smart Meter Data. Energy
Build. 2021, 231, 110617. [CrossRef]
28. Liang, H.; Ma, J.; Sun, R.; Du, Y. A Data-Driven Approach for Targeting Residential Customers for Energy Efficiency Programs.
IEEE Trans. Smart Grid 2020, 11, 1229–1238. [CrossRef]
29. Buzau, M.M.; Tejedor-Aguilera, J.; Cruz-Romero, P.; Gomez-Exposito, A. Detection of Non-Technical Losses Using Smart Meter
Data and Supervised Learning. IEEE Trans. Smart Grid 2019, 10, 2661–2670. [CrossRef]
30. Wang, Y.; Chen, Q.; Kang, C.; Xia, Q.; Luo, M. Sparse and Redundant Representation-Based Smart Meter Data Compression and
Pattern Extraction. IEEE Trans. Power Syst. 2017, 32, 2142–2151. [CrossRef]
31. Mekuria, D.N.; Sernani, P.; Falcionelli, N.; Dragoni, A.F. Smart Home Reasoning Systems: A Systematic Literature Review.
J. Ambient. Intell. Hum. Comput. 2021, 12, 4485–4502. [CrossRef]
32. Feng, C.; Mehmani, A.; Zhang, J. Deep Learning-Based Real-Time Building Occupancy Detection Using AMI Data. IEEE Trans.
Smart Grid 2020, 11, 4490–4501. [CrossRef]
33. Massaoudi, M.; Abu-Rub, H.; Refaat, S.S.; Chihi, I.; Oueslati, F.S. Deep Learning in Smart Grid Technology: A Review of Recent
Advancements and Future Prospects. IEEE Access 2021, 9, 54558–54578. [CrossRef]
34. Aldegheishem, A.; Anwar, M.; Javaid, N.; Alrajeh, N.; Shafiq, M.; Ahmed, H. Towards Sustainable Energy Efficiency with
Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks. IEEE Access 2021, 9, 25036–25061.
[CrossRef]
35. Zhuang, P.; Zamir, T.; Liang, H. Blockchain for Cybersecurity in Smart Grid: A Comprehensive Survey. IEEE Trans. Ind. Inf. 2021,
17, 3–19. [CrossRef]
36. Dileep, G. A Survey on Smart Grid Technologies and Applications. Renew. Energy 2020, 146, 2589–2625. [CrossRef]
37. Kezunovic, M.; Pinson, P.; Obradovic, Z.; Grijalva, S.; Hong, T.; Bessa, R. Big Data Analytics for Future Electricity Grids. Electr.
Power Syst. Res. 2020, 189, 106788. [CrossRef]
38. Kumar, P.; Lin, Y.; Bai, G.; Paverd, A.; Dong, J.S.; Martin, A. Smart Grid Metering Networks: A Survey on Security, Privacy and
Open Research Issues. IEEE Commun. Surv. Tutor. 2019, 21, 2886–2927. [CrossRef]
39. Ghosal, A.; Conti, M. Key Management Systems for Smart Grid Advanced Metering Infrastructure: A Survey. IEEE Commun.
Surv. Tutor. 2019, 21, 2831–2848. [CrossRef]
40. Boza, P.; Evgeniou, T. Artificial Intelligence to Support the Integration of Variable Renewable Energy Sources to the Power System.
Appl. Energy 2021, 290, 116754. [CrossRef]
41. Kasaraneni, H.; Rosaline, S. Automatic Merging of Scopus and Web of Science Data for Simplified and Effective Bibliometric
Analysis. Ann. Data. Sci. 2022. [CrossRef]

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.