Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review

• Published in: Electronics (Basel) Vol. 12; no. 1; p. 242
• Main Authors: Mazhar, Tehseen, Irfan, Hafiz Muhammad, Haq, Inayatul, Ullah, Inam, Ashraf, Madiha, Shloul, Tamara Al, Ghadi, Yazeed Yasin, Imran, Elkamchouchi, Dalia H.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Artificial intelligence; Automation; Building management systems; Cybersecurity; Data compression; Electronic devices; Energy consumption; Energy industry; Energy management; HVAC; Internet access; Internet of Things; Machine learning; Mobile computing; Remote monitoring; Sensors; Smart buildings; Smart grid; Smartphones; Software; System effectiveness; Pakistan
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12010242

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.