Role of IoT and Machine Learning in Smart Grid

Garg, V. K.; Sharma, Sudhir

doi:10.1007/978-981-16-8774-7_58

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…However, such power consumption information may reveal information about consumers' lifestyles.Based on this, Mohammed et al (2018) proposed an effective method using symmetric key encryption and hash operation to collect power consumption information while protecting consumers' privacy. Garg et al (2022) tried to focus on the IoT and machine learning in the smart grid to help smart grid makes decisions. Through this investigation and research in this section, it is concluded that power companies can mine valuable information from massive information to provide users with better power services.…”

Section: Intelligent Iot Applications During Load Acquisitionmentioning

confidence: 99%

“…Communication technology is an important way to realize interconnection between various nodes of the IoT, which allows data and resources to be exchanged between communication nodes and connected devices and people Garg et al (2022).Computing is process of using software and hardware or computer systems to perform and complete specific tasks, such as computers performing scientific calculations or other applications.…”

Section: Intelligent Collection and Scheduling Inmentioning

confidence: 99%

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A survey of intelligent load monitoring in IoT-enabled distributed smart grids

Gan

Zeng

Liu

et al. 2023

IJAHUC

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Power Load Monitoring has been a research hotspot since a few years ago. With development of artificial intelligence, construction of smart grid has become the most important part of power load monitoring. At the same time, task scheduling mechanism combined with the distributed IoT(Internet of Things) improves efficiency of smart grid. In this paper, applications of cloud/edge platform in the data acquisition, processing and scheduling of the IoT is introduced step by step, as well as applications and differences of artificial intelligence algorithm in each step, including Data Acquisition, Load Disaggregation, Load Forecasting and so on. Finally, combined with various optimization methods, future research directions are prospected, including data and network security issues, challenges faced by cloud/edge architecture, adaptive fine-grained Load Disaggregation, Load Forecasting.

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Section: Intelligent Iot Applications During Load Acquisitionmentioning

confidence: 99%

Section: Intelligent Collection and Scheduling Inmentioning

confidence: 99%

A survey of intelligent load monitoring in IoT-enabled distributed smart grids

Gan

Zeng

Liu

et al. 2023

IJAHUC

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“…Smart grids enhance energy availability and efficiency, providing uninterrupted power supply to cities, towns, and businesses by minimizing power wastage, reducing faults, and optimizing power supply to cope with high energy demands [157,158]. Power-grid failures are rare, but they result in the loss of millions of dollars, blackouts, and social ataxia.…”

Section: Smart Gridsmentioning

confidence: 99%

Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective

Bzai

Alam²,

Dhafer

et al. 2022

Electronics

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Machine learning (ML) allows the Internet of Things (IoT) to gain hidden insights from the treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge and data patterns. Without ML, IoT cannot withstand the future requirements of businesses, governments, and individual users. The primary goal of IoT is to perceive what is happening in our surroundings and allow automation of decision-making through intelligent methods, which will mimic the decisions made by humans. In this paper, we classify and discuss the literature on ML-enabled IoT from three perspectives: data, application, and industry. We elaborate with dozens of cutting-edge methods and applications through a review of around 300 published sources on how ML and IoT work together to play a crucial role in making our environments smarter. We also discuss emerging IoT trends, including the Internet of Behavior (IoB), pandemic management, connected autonomous vehicles, edge and fog computing, and lightweight deep learning. Further, we classify challenges to IoT in four classes: technological, individual, business, and society. This paper will help exploit IoT opportunities and challenges to make our societies more prosperous and sustainable.

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