scholarly journals Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions

2021 ◽  
pp. 32-42
Author(s):  
Navod Neranjan .. ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Smart Grid ◽  
Power Systems ◽  
Energy Distribution ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Directions ◽  
Architectural Elements ◽  
Efficient Power

In the 21st century, the Smart Grid (SG), also known as the next-generation power grid, arose as a substitute for inefficient power systems, ensuring a reliable and efficient power supply. It is projected to improve the reliability and efficiency of energy distribution while having minimal side effects because it is coupled with modern communication and computation capabilities. The huge infrastructure it possesses, as well as the system's underlying communication network, has resulted in a large number of data that necessitates the use of diverse approaches for proper analysis and decision making. When it comes to analyzing this huge amount of data and generating significant insights from it, big data analytics, machine learning (ML), and deep learning (DL), all play a key role. These insights are useful for anomaly detection, fraud detection, price confirmation, fault detection, monitoring energy consumption, and so on. Hence constant and continuous data analysis is an essential part, of the modern smart grid, for its existence. Inspired by providing a reliable and efficient energy distribution, this paper explores and surveys the smart grid architectural elements, ML and DL based applications, and approaches in the context of SG. In addition in terms of ML and DL based data analytics, this paper highlights the limitations of the current research and, highlights future directions as well.

scholarly journals Optimizing Hadoop Performance for Big Data Analytics in Smart Grid

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Mukhtaj Khan ◽  
Zhengwen Huang ◽  
Maozhen Li ◽  
Gareth A. Taylor ◽  
Phillip M. Ashton ◽  
...  
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Power Systems ◽  
Data Analytics ◽  
High Performance ◽  
Gene Expression Programming ◽  
Big Data Analytics ◽  
Fluctuation Analysis ◽  
Grid Applications ◽  
Hadoop Framework

The rapid deployment of Phasor Measurement Units (PMUs) in power systems globally is leading to Big Data challenges. New high performance computing techniques are now required to process an ever increasing volume of data from PMUs. To that extent the Hadoop framework, an open source implementation of the MapReduce computing model, is gaining momentum for Big Data analytics in smart grid applications. However, Hadoop has over 190 configuration parameters, which can have a significant impact on the performance of the Hadoop framework. This paper presents an Enhanced Parallel Detrended Fluctuation Analysis (EPDFA) algorithm for scalable analytics on massive volumes of PMU data. The novel EPDFA algorithm builds on an enhanced Hadoop platform whose configuration parameters are optimized by Gene Expression Programming. Experimental results show that the EPDFA is 29 times faster than the sequential DFA in processing PMU data and 1.87 times faster than a parallel DFA, which utilizes the default Hadoop configuration settings.

8 Smart grid–based big data analytics using machine learning and artificial intelligence: a survey

2021 ◽  
pp. 241-278
Author(s):  
Subin Koshy ◽  
S. Rahul ◽  
R. Sunitha ◽  
Elizabeth P. Cheriyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Smart Grid ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Grid Based

Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions

2020 ◽  
Vol 38 ◽  
pp. 100303 ◽  
Author(s):  
Safa Ben Atitallah ◽  
Maha Driss ◽  
Wadii Boulila ◽  
Henda Ben Ghézala
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Future Directions ◽  
Development Review

scholarly journals Emergence and Evolution of Big Data Analytics in HIV Research: Bibliometric Analysis of Federally Sponsored Studies 2000-2019

2021 ◽  
Author(s):  
Chen Liang ◽  
Shan Qiao ◽  
Bankole Olatosi ◽  
Tianchu Lyu ◽  
Xiaoming Li
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Federal Agencies ◽  
Research Topics ◽  
Aids Research ◽  
Hiv Aids

AbstractBackgroundThe rapid growth of inherently complex and heterogeneous data in HIV/AIDS research underscores the importance of Big Data analytics. Recently, there have been increasing uptakes of Big Data techniques in basic, clinical, and public health fields of HIV/AIDS research. However, no studies have systematically elaborated on the evolving applications of Big Data in HIV/AIDS research. We sought to explore the emergence and evolution of Big Data analytics in HIV/AIDS-related publications that were funded by the US federal agencies.MethodsWe identified HIV/AIDS and Big Data related publications that were funded by seven federal agencies (i.e., NIH, ACF, AHRQ, CDC, HRSA, FDA, and VA) from 2000 to 2019 by integrating data from NIH ExPORTER, MEDLINE, and MeSH. Building on bibliometrics and Natural Language Processing methods, we constructed co-occurrence networks using bibliographic metadata (e.g., countries, institutes, MeSH terms, and keywords) of the retrieved publications. We then detected clusters among the networks as well as the temporal dynamics of clusters, followed by expert evaluation and clinical implications.ResultsWe harnessed nearly 600 thousand publications related to HIV/AIDS, of which 19,528 publications relating to Big Data were included in bibliometric analysis. Results showed that (1) the number of Big Data publications has been increasing since 2000, (2) US institutes have been in close collaborations with China, Canada, and Germany, (3) University of California system, MD Anderson Cancer Center, and Harvard Medical School are among the most productive institutes and started using Big Data in HIV/AIDS research early, (4) Big Data research was not active in public health disciplines until 2015, (5) research topics such as genomics, HIV comorbidities, population-based studies, Electronic Health Records (EHR), social media, precision medicine, and methodologies such as machine learning, Deep Learning, radiomics, and data mining emerge quickly in recent years.ConclusionsWe identified a rapid growth in the cross-disciplinary research of HIV/AIDS and Big Data over the past two decades. Our findings demonstrated patterns and trends of prevailing research topics and Big Data applications in HIV/AIDS research and informed fast evolving research areas including HIV comorbidities, genomics, secondary analysis of EHR, social media, machine learning, and Deep Learning as future directions of HIV/AIDS research.

Advanced Deep Learning Applications in Big Data Analytics

2021 ◽  
pp. 1-28
Author(s):  
Venkatesan Manian ◽  
Vadivel P.
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Internet ◽  
Infrastructure Development ◽  
Learning Approaches ◽  
The Internet Of Things

This chapter analyzes the Internet of Things (IoT), its history, and its tools in brief. This chapter also explores the contribution of IoT towards the recent development in infrastructure development of nations represented as smart world. This chapter also discuss the contribution of IoT towards big data analytics era. This chapter also briefly introduce the smart bio world and how it is made possible with the internet of things. This chapter also introduces the machine learning approaches and also discusses the contribution of Internet of Thing for this machine learning. This chapter also briefly introduces some tools used for IoT developments.

scholarly journals A survey on big data analytics with deep learning in text using machine learning mechanisms

2018 ◽  
Vol 7 (2.21) ◽  
pp. 335
Author(s):  
R Anandan ◽  
Srikanth Bhyrapuneni ◽  
K Kalaivani ◽  
P Swaminathan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Specific Information ◽  
Dynamic Framework ◽  
Wide Range

Big Data Analytics and Deep Learning are two immense purpose of meeting of data science. Big Data has ended up being major a tantamount number of affiliations both open and private have been gathering huge measures of room specific information, which can contain enduring information about issues, for instance, national cognizance, motorized security, coercion presentation, advancing, and healing informatics. Relationship, for instance, Microsoft and Google are researching wide volumes of data for business examination and decisions, influencing existing and future progression. Critical Learning figuring's isolate odd state, complex reflections as data outlines through another levelled learning practice. Complex reflections are learnt at a given level in setting of all around less asking for thoughts figured in the past level in the dynamic framework. An indispensable favoured perspective of Profound Learning is the examination and culture of beast measures of unconfirmed data, making it a fundamental contraption for Great Statistics Analytics where offensive data is, everything seen as, unlabelled and un-arranged. In the present examination, we investigate how Deep Learning can be used for keeping an eye out for some essential issues in Big Data Analytics, including removing complex cases from Big volumes of information, semantic asking for, information naming, smart data recovery, and streamlining discriminative errands .Deep learning using Machine Learning(ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the front line as of late mostly attributable to the advert of huge information. ML counts have never been remarkable ensured while tried by gigantic data. Gigantic data engages ML counts to uncover more fine-grained cases and make more advantageous and correct gauges than whenever in late memory with deep learning; on the other hand, it exhibits genuine challenges to deep learning in ML, for instance, show adaptability and appropriated enlisting. In this paper, we introduce a framework of Deep learning in ML on big data (DLiMLBiD) to guide the discussion of its opportunities and challenges. In this paper, different machine learning algorithms have been talked about. These calculations are utilized for different purposes like information mining, picture handling, prescient examination, and so forth to give some examples. The fundamental favourable position of utilizing machine learning is that, once a calculation realizes what to do with information, it can do its work consequently. In this paper we are providing the review of different Deep learning in text using Machine Learning and Big data methods.  

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