Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review
With the assistance of machine learning, difficult tasks can be completed entirely on their own. In a smart grid (SG), computers and mobile devices may make it easier to control the interior temperature, monitor security, and perform routine maintenance. The Internet of Things (IoT) is used to conne...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/1/242 |
| _version_ | 1797625952640958464 |
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| author | Tehseen Mazhar Hafiz Muhammad Irfan Inayatul Haq Inam Ullah Madiha Ashraf Tamara Al Shloul Yazeed Yasin Ghadi Imran Dalia H. Elkamchouchi |
| author_facet | Tehseen Mazhar Hafiz Muhammad Irfan Inayatul Haq Inam Ullah Madiha Ashraf Tamara Al Shloul Yazeed Yasin Ghadi Imran Dalia H. Elkamchouchi |
| author_sort | Tehseen Mazhar |
| collection | DOAJ |
| description | With the assistance of machine learning, difficult tasks can be completed entirely on their own. In a smart grid (SG), computers and mobile devices may make it easier to control the interior temperature, monitor security, and perform routine maintenance. The Internet of Things (IoT) is used to connect the various components of smart buildings. As the IoT concept spreads, SGs are being integrated into larger networks. The IoT is an important part of SGs because it provides services that improve everyone’s lives. It has been established that the current life support systems are safe and effective at sustaining life. The primary goal of this research is to determine the motivation for IoT device installation in smart buildings and the grid. From this vantage point, the infrastructure that supports IoT devices and the components that comprise them is critical. The remote configuration of smart grid monitoring systems can improve the security and comfort of building occupants. Sensors are required to operate and monitor everything from consumer electronics to SGs. Network-connected devices should consume less energy and be remotely monitorable. The authors’ goal is to aid in the development of solutions based on AI, IoT, and SGs. Furthermore, the authors investigate networking, machine intelligence, and SG. Finally, we examine research on SG and IoT. Several IoT platform components are subject to debate. The first section of this paper discusses the most common machine learning methods for forecasting building energy demand. The authors then discuss IoT and how it works, in addition to the SG and smart meters, which are required for receiving real-time energy data. Then, we investigate how the various SG, IoT, and ML components integrate and operate using a simple architecture with layers organized into entities that communicate with one another via connections. |
| first_indexed | 2024-03-11T10:03:46Z |
| format | Article |
| id | doaj.art-3a059bf385044536821ae57b6978a407 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T10:03:46Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-3a059bf385044536821ae57b6978a4072023-11-16T15:13:05ZengMDPI AGElectronics2079-92922023-01-0112124210.3390/electronics12010242Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A ReviewTehseen Mazhar0Hafiz Muhammad Irfan1Inayatul Haq2Inam Ullah3Madiha Ashraf4Tamara Al Shloul5Yazeed Yasin Ghadi6Imran7Dalia H. Elkamchouchi8Department of Computer Science, Virtual University of Pakistan, Lahore 51000, PakistanDepartment of Computer Science, Islamia University Bahawalpur, Bahawalnagar 62300, PakistanSchool of Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaBK21 Chungbuk Information Technology Education and Research Center, Chungbuk National University, Cheongju 28644, Republic of KoreaDepartment of Computer Science, NCBA&E Multan Campus, University in Multan, Multan 60650, PakistanLiwa College of Technology, Department of General Education, Abu Dhabi P.O. Box 41009, United Arab EmiratesDepartment of Computer Science, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab EmiratesDepartment of Biomedical Engineering, Gachon University, Incheon 21936, Republic of KoreaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaWith the assistance of machine learning, difficult tasks can be completed entirely on their own. In a smart grid (SG), computers and mobile devices may make it easier to control the interior temperature, monitor security, and perform routine maintenance. The Internet of Things (IoT) is used to connect the various components of smart buildings. As the IoT concept spreads, SGs are being integrated into larger networks. The IoT is an important part of SGs because it provides services that improve everyone’s lives. It has been established that the current life support systems are safe and effective at sustaining life. The primary goal of this research is to determine the motivation for IoT device installation in smart buildings and the grid. From this vantage point, the infrastructure that supports IoT devices and the components that comprise them is critical. The remote configuration of smart grid monitoring systems can improve the security and comfort of building occupants. Sensors are required to operate and monitor everything from consumer electronics to SGs. Network-connected devices should consume less energy and be remotely monitorable. The authors’ goal is to aid in the development of solutions based on AI, IoT, and SGs. Furthermore, the authors investigate networking, machine intelligence, and SG. Finally, we examine research on SG and IoT. Several IoT platform components are subject to debate. The first section of this paper discusses the most common machine learning methods for forecasting building energy demand. The authors then discuss IoT and how it works, in addition to the SG and smart meters, which are required for receiving real-time energy data. Then, we investigate how the various SG, IoT, and ML components integrate and operate using a simple architecture with layers organized into entities that communicate with one another via connections.https://www.mdpi.com/2079-9292/12/1/242Artificial Intelligence (AI)Internet of Things (IoT)machine learningSmart Grid (SG)smart buildings |
| spellingShingle | Tehseen Mazhar Hafiz Muhammad Irfan Inayatul Haq Inam Ullah Madiha Ashraf Tamara Al Shloul Yazeed Yasin Ghadi Imran Dalia H. Elkamchouchi Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review Electronics Artificial Intelligence (AI) Internet of Things (IoT) machine learning Smart Grid (SG) smart buildings |
| title | Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review |
| title_full | Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review |
| title_fullStr | Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review |
| title_full_unstemmed | Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review |
| title_short | Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review |
| title_sort | analysis of challenges and solutions of iot in smart grids using ai and machine learning techniques a review |
| topic | Artificial Intelligence (AI) Internet of Things (IoT) machine learning Smart Grid (SG) smart buildings |
| url | https://www.mdpi.com/2079-9292/12/1/242 |
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