The rapid penetration of renewable energy systems has intensified operational challenges related to intermittency, decentralized architectures and data-driven decision making. In addition, artificial intelligence (AI) techniques are being increasingly adopted into renewable energy applications, as shown in the existing studies. However, these studies are often fragmented, focusing on isolated forecasting or optimization tasks without providing an integrated design perspective. This study addresses this gap by systematically reviewing and synthesizing recent AI-based approaches for forecasting, optimization and control of renewable energy systems.

A structured literature analysis was conducted covering machine learning, deep learning and reinforcement learning techniques applied to solar, wind, hydropower, energy storage, and hybrid renewable systems. Forecasting models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Convolutional Neural Networks (CNNs) and hybrid architectures, were comparatively analyzed with respect to prediction horizon, data requirements, and operational relevance. Optimization and control strategies based on genetic algorithms, particle swarm optimization, and reinforcement learning were examined for battery scheduling, hybrid system sizing and market participation.

The analysis shows that these deep learning-based forecasting models consistently outperform old traditional statistical approaches. These modes enabled improved operational planning and higher renewable penetration. Predictive maintenance using SCADA data significantly enhances fault detection and asset reliability, while learning-based optimization techniques support adaptive and resilient energy system operation. Based on these findings, a unified AI-enabled design framework is proposed to guide the selection and integration of forecasting, optimization and control techniques for renewable energy systems.