AIRAAIRA

Journal of Information Systems and Technology ResearchJournal of Information Systems and Technology Research

A smart grid is a contemporary electrical system that supports two-way communication and utilizes the concept of demand response. In order to increase the smart grids dependability and enhance the consistency, efficiency, and efficiency of the electrical supply, stability prediction is required. The true test for smart grid system designers and specialists will therefore be the increase of renewable energy. With the goal of integrating the electric utility infrastructure into the advanced communication era of today, both in terms of function and architecture, this program has achieved great strides toward modernizing and expanding it. In this study, researchers used the Systematic literature review method which identifies, evaluates and interprets all relevant research results related to certain research questions, certain topics, or phenomena of concern. The study review on how a smart grid applied different deep learning techniques and how renewable energy can be integrated into a system where grid control is essential for energy management. The article discusses the idea of a smart grid and how reliable it is when renewable energy sources are present. Globally, a change in electric energy is needed to reduce greenhouse gas emissions, prevent global warming, reduce pollution, and boost energy security.

This research highlights the potential of AI baseline networks to enhance grid computing performance in areas like resource management, scheduling, and fault tolerance.However, challenges related to computational overhead, scalability, and security must be addressed.Future research should focus on developing lightweight AI models and secure integration mechanisms for large-scale grid systems.

Further research should explore the development of fully autonomous grid computing systems powered by AI, minimizing human intervention and enabling dynamic resource management. Investigating the application of AI baseline networks to edge computing nodes within grids could enable real-time processing at the data source, addressing latency concerns. Additionally, research is needed to develop novel AI algorithms specifically tailored for grid computing, focusing on improving efficiency, scalability, and security while considering the unique challenges of distributed environments. These advancements could revolutionize industries reliant on high-performance computing, offering benefits in areas like climate modeling, financial simulations, and healthcare, while also contributing to a more sustainable and resilient energy infrastructure. The integration of AI and grid computing holds significant promise for optimizing energy distribution, reducing environmental impact, and enhancing the reliability of critical infrastructure.

  1. Blockchain-Based Data Access Control and Key Agreement System in IoT Environment. blockchain based data... mdpi.com/1424-8220/23/11/5173Blockchain Based Data Access Control and Key Agreement System in IoT Environment blockchain based data mdpi 1424 8220 23 11 5173
  2. SCAR: Power Side-Channel Analysis at RTL Level | IEEE Journals & Magazine | IEEE Xplore. scar power... doi.org/10.1109/TVLSI.2024.3390601SCAR Power Side Channel Analysis at RTL Level IEEE Journals Magazine IEEE Xplore scar power doi 10 1109 TVLSI 2024 3390601
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