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“smart Grids And Iot: Enhancing Efficiency And Reliability In Energy Distribution”
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Asia/pacific* Internet Of Things Spending To Reach $436 Billion In 2026, Says Idc
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Analyzing IoT Challenges and Solutions in Smart Grids Using AI and Machine Learning Techniques: An Overview
By Tehseen Mazhar Tehseen Mazhar Scilit Preprints.org Google Scholar 1, Hafiz Muhammad Irfan Hafiz Muhammad Irfan Scilit Preprints.org Google Scholar 2, Inayatul Haq Inayatul Haq Scilit Preprints.org Google Scholar 3, Inam Ullah Inam Ullahsorg Scholar. , * , Madiha Ashraf Madiha Ashraf Scilit Preprints.org Google Scholar 5, Tamara Al Shloul Tamara Al Shloul Scilit Preprints.org Google Scholar 6, Yazeed Yasin Ghadi Yazeed Yasin Ghadi Scilit Preprints.org Google Scholar 7, Imran Google Imransorg. Scholar 8 and Dalia H. Elkamchouchi Dalia H. Elkamchouchi Scilit Preprints.org Google Scholar 9
Understanding The Role Of Smart City & Its Components In The Iot Era
Department of Information Technology, College of Computer and Information Sciences, Princess Noorah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Received: 26 November 2022 / Revised: 23 December 2022 / Accepted: 26 December 2022 / Published: 3 January 2023
With the help of machine learning, complex tasks can be completed completely independently. In a smart grid (SG), computers and mobile devices can facilitate indoor temperature control, security monitoring and routine maintenance. The Internet of Things (IoT) is used to connect various components of smart buildings. As the IoT concept spreads, SGs are being integrated into larger networks. IoT is an important part of SGs because it provides services that improve everyone’s life. Existing life support systems have been determined to be safe and effective in sustaining life. The primary objective of this study is to determine the motivation for installing IoT devices in smart buildings and networks. In this sense, the infrastructure that supports IoT devices and their components is critical. Remote configuration of smart grid monitoring systems can improve the safety and comfort of building occupants. Sensors are required to operate and monitor everything from consumer electronics to SG. Networked devices should consume less power and be remotely monitored. The authors aim to help develop solutions based on AI, IoT and SGs. In addition, the authors explore networking, machine intelligence, and SG. Finally, we review research on SG and IoT. Several components of an IoT platform are subject to debate. The first part of this paper discusses the most common machine learning methods for predicting building energy demand. The authors then discuss the IoT and how it works, in addition to the SG and smart meters needed to obtain real-time energy data. Next, we explore how different components of SG, IoT, and ML are integrated and functional using a simple architecture with layers organized into entities that communicate with each other through connections.
The invention of the Internet of Things (IoT) is one of the most important technological breakthroughs of the 21st century. IoT is a network of connected hardware, software, and physical nodes that enable the collection and distribution of data. Data exchange between multiple infrastructures and devices is referred to as “IoT” [1]. Without them, access to the Internet is impossible. Internet Protocol controls Internet devices. The Internet allows customer-to-customer communication online. Networked “things” aspire to provide everyday objects with the ability to share data and information independently and at regular intervals. A structure that uses connected data, technology and mechanisms is called an “intelligent building”. Analytics and automation to control key services including HVAC, lighting, heating, safety equipment and air conditioning HVAC devices with intelligent control [2]. It is highly beneficial for HVAC systems to use less energy if they have the power to be implemented when energy demand is high. Challenging parts of this duty include detecting and locating faults and minimizing power consumption in vacant parts of the facility [3].
Pdf) Design, Implementation, And Deployment Of An Iot Based Smart Energy Management System
Integration of renewable energy sources, Smart Grid (SG) management, energy trading, energy system flexibility and negative pricing, energy management, arbitrage and pricing, and SG financial transactions are some of the recommendations to improve the performance of SG blockchain technology and cryptocurrencies. resulting in a flatter load profile and economic advantage. In addition, previous researchers [have] made changes to block-chain algorithms so that online user communities can use Bitcoin to transact in markets and energy systems using more secure methods [4]. Writers also use digital money in their energy infrastructure. A long-term view can be just as useful as a more immediate one when trying to detect and mitigate potential threats. Writers pay little attention to enduring challenges, known problems, or workarounds. Using blockchain-based digital currencies, energy industry participants must address security concerns if they are to be efficient and competitive.
Many more articles are available covering a wide range of topics affecting all human cultures. For example, the essay highlights the issue of global warming, which is causing substantial changes in almost every part of the world. In this study, the electrical power used by common household appliances is decomposed using the electrical line decomposition (ELD) method. ELDs today depend on computer science techniques and artificial intelligence (AI). An optimized full set empirical model decomposition and wavelet packet transform, or OCEEMD-WPT, is also used. This idea was created to show how the end user perceives changes in power line noise. Therefore, the collection of vital information required for the operation of the network is significantly more efficient [5].
Using sensors or smart meters, the power grid can become SG. These powerful sensors transmit a lot of data. This will help you understand the behavior of the network and make predictions. The huge amount of data required to connect and store thousands of IoT nodes makes this impossible [6]. Automatic Encoders (AE) approach data entropy encoding to represent previously compressed content with less data. These strategies use AI and deep learning. Until recently, the data spectrum made compression impossible. The proposed data compression method uses AE models. Spectral windows improve compression and entropy.
IoT devices and technology can improve SG through real-time monitoring, new pricing methods, dynamic energy management, and self-healing. Intrusions are more significant in SG converted network components and services. The researchers studied attacker and defender payoffs using real devices and honeypots. Both the attacked and defended games contain uncertain NE and Bayesian NE matching conditions. The authors suggested an increase in worse outcomes under disequilibrium conditions. If the accused accepts the attack and surrenders, he can submit. The simulations show that both games were balanced offensively and defensively. Defense recognizes and rewards aggressors. By interacting with a certain number of real devices and honeypots, previous research has explored how attackers and defenders can cooperate to maximize their payoff [7].
Iot 2022 In Review: The 10 Most Relevant Iot Developments Of The Year
SGs use automation, sensors and remote control to increase comfort, safety and energy efficiency. IoT sensors monitor “smart” building elements. This knowledge can improve the interior. IoT-based “SG” that controls building temperature, security and maintenance is made more accessible by smartphones and tablets. The IoT’s ability to connect multiple sensors allows it to collect and analyze data in real time, leading to more innovative and user-friendly buildings [8]. A fire alarm is a must for SGs. A smart IoT fire protection system is needed to prevent property damage and save lives. Weka and J48 are used; Previous research has shown energy use patterns and behaviors. They are then categorized based on how much energy they use [9]. With machine learning and big data for the home, the HEMS-IoT smart energy management system reduces the total energy consumption of the home and ensures the comfort and safety of its residents. The system relies heavily on machine learning and large amounts of data to analyze and classify how efficiently energy is used, identify trends in human behavior, and maintain a high level of comfort for building occupants [10]. The authors investigated many security holes in IoT software. The following survey was used to find practical security solutions. Previous researchers have noted
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