AI-Driven Optimization of Renewable Energy Systems: A Comprehensive Review | IJCT Volume 12 – Issue 5 | IJCT-V12I5P77

The rapid advancement of artificial intelligence (AI) has opened new avenues for optimizing renewable energy systems, enhancing their efficiency by up to 20%, improving forecasting accuracy by 15-30%, and enabling better integration into existing power grids through real-time decision-making. This comprehensive review explores the application of AI techniques in optimizing various renewable energy sources, including solar, wind, hydro, and bioenergy. By examining recent research and over four detailed case studies, we identify key AI methodologies-such as machine learning, deep learning, and reinforcement learning-their quantifiable benefits, persistent challenges, and emerging future prospects. This paper aims to provide a holistic understanding of how AI-driven optimization can accelerate the transition towards a sustainable energy future, potentially reducing greenhouse gas emissions from renewable systems by up to 25% and improving economic returns across installations by 10-15%.

The integration of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), into renewable energy systems has ushered in notable advancements in forecasting, performance optimization, fault detection, and maintenance across solar, wind, and hydro sectors. In solar energy, models such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have improved solar irradiance forecasting and enhanced photovoltaic (PV) system efficiency while enabling predictive maintenance for panels and inverters. Similarly, AI approaches like recurrent neural networks (RNNs) and support vector machines (SVMs) have led to more accurate wind speed forecasting, optimized turbine performance, and facilitated early fault detection in wind energy systems. For hydroelectric applications, LSTM and artificial neural network (ANN) models have refined water flow forecasting and turbine optimization, as well as enabled AI-driven fault detection systems for reduced downtime and operational costs. Together, these sector-specific successes demonstrate the transformative effect of AI on renewable energy production, establishing a foundation for more efficient, reliable, and sustainable energy systems. The continued evolution of AI techniques and expansion of high-quality data promise even greater capabilities in real-time optimization and resource management, supporting a future where renewable energy is increasingly economical and dependable. This review underscores both the significant progress achieved and the ongoing potential for AI to drive the next wave of innovation in the renewable energy domain

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