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Journal of Intelligent Systems and Internet of Things
Volume 3 , Issue 1, PP: 32-42 , 2021 | Cite this article as | XML | Html |PDF

Title

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

  Navod Neranjan Thilakarathne 1 * ,   Rohan Samarasinghe 2 ,   Mohan Krishna Kagita 3 ,   Surekha Lanka 4 ,   Hussain Ahmad 5

1  University of Colombo, SRILANKA
    (navod.neranjan@ict.cmb.ac.lk)

2  University of Colombo, SRILANKA
    (rohan@ict.cmb.ac.lk)

3  Charles Sturt University, AUSTRALIA
    (mohankrishna4k@gmail.com)

4  Stamford international university, THAILAND
    (surekha.lanka@stamford.edu)

5  Gomal University, PAKISTAN;
    (hussaintajazai@gmail.com)


Doi   :   https://doi.org/10.54216/JISIoT.030103

Received: March 13, 2021 Accepted: June 10, 2021

Abstract :

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.

Keywords :

Smart Grid , IoT , Internet of Things , Machine Learning , Deep Learning , Cyber-Physical Systems

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Cite this Article as :
Style #
MLA Navod Neranjan Thilakarathne , Rohan Samarasinghe , Mohan Krishna Kagita , Surekha Lanka , Hussain Ahmad. "Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions." Journal of Intelligent Systems and Internet of Things, Vol. 3, No. 1, 2021 ,PP. 32-42 (Doi   :  https://doi.org/10.54216/JISIoT.030103)
APA Navod Neranjan Thilakarathne , Rohan Samarasinghe , Mohan Krishna Kagita , Surekha Lanka , Hussain Ahmad. (2021). Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions. Journal of Journal of Intelligent Systems and Internet of Things, 3 ( 1 ), 32-42 (Doi   :  https://doi.org/10.54216/JISIoT.030103)
Chicago Navod Neranjan Thilakarathne , Rohan Samarasinghe , Mohan Krishna Kagita , Surekha Lanka , Hussain Ahmad. "Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions." Journal of Journal of Intelligent Systems and Internet of Things, 3 no. 1 (2021): 32-42 (Doi   :  https://doi.org/10.54216/JISIoT.030103)
Harvard Navod Neranjan Thilakarathne , Rohan Samarasinghe , Mohan Krishna Kagita , Surekha Lanka , Hussain Ahmad. (2021). Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions. Journal of Journal of Intelligent Systems and Internet of Things, 3 ( 1 ), 32-42 (Doi   :  https://doi.org/10.54216/JISIoT.030103)
Vancouver Navod Neranjan Thilakarathne , Rohan Samarasinghe , Mohan Krishna Kagita , Surekha Lanka , Hussain Ahmad. Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions. Journal of Journal of Intelligent Systems and Internet of Things, (2021); 3 ( 1 ): 32-42 (Doi   :  https://doi.org/10.54216/JISIoT.030103)
IEEE Navod Neranjan Thilakarathne, Rohan Samarasinghe, Mohan Krishna Kagita, Surekha Lanka, Hussain Ahmad, Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 3 , No. 1 , (2021) : 32-42 (Doi   :  https://doi.org/10.54216/JISIoT.030103)