ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN RENEWABLE ENERGY, A CRITICAL REVIEW OF CURRENT TRENDS AND FUTURE DIRECTIONS

Abstract

This critical review provides an overview of current trends and future directions in applying artificial intelligence (AI) and machine learning (ML) in renewable energy. AI and ML are applied in various aspects of renewable energy systems, including forecasting, optimization, and control. The review highlights the potential of AI and ML to improve the efficiency and reliability of renewable energy systems and identifies future directions for research and development. The combination of AI and ML in energy storage systems improves performance by forecasting storage needs and optimizing charge-discharge cycles, resulting in a more effective utilization of stored energy. Additionally, AI and ML aid in lessening the environmental footprint of renewable energy through process optimization and emission reduction. The review further discusses how AI, IoT, blockchain, and edge computing interact in renewable energy applications. IoT devices allow for collecting data in real-time, which, when paired with AI and ML, improves the responsiveness and efficiency of systems. Blockchain technology guarantees secure and transparent transactions, with edge computing enabling quicker data processing at the origin, further enhancing renewable energy systems. This in-depth overview highlights how AI and ML can drastically change renewable energy, providing analysis on the latest progress and upcoming possibilities. It offers guidelines for future studies and advancements in this crucial area.