Artificial Intelligence and Machine Learning in Renewable and Sustainable Energy Strategies: A Critical Review and Future Perspectives

Abstract

Artificial intelligence (AI) and machine learning (ML) are transforming renewable energy tactics by improving effectiveness, dependability, and eco-friendliness. This critical analysis evaluates how AI and ML technologies are being used in different areas of renewable energy. These models have greatly enhanced the forecasting of renewable energy, allowing for accurate predictions that enhance energy production and distribution. AI and ML play a vital role in enhancing renewable energy systems, increasing efficiency, and cutting costs by utilizing advanced analytics and predictive maintenance techniques. AI and ML assist in making real-time decisions and adaptive control in smart grids and energy management to optimize energy distribution and reduce waste. The combination of AI and ML in energy storage systems improves performance through forecasting storage needs and optimizing chargedischarge 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 have the ability to 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.