Advanced artificial intelligence techniques for forecasting, optimization, and management of renewable energy systems
Animesh Kumar Dubey1
Corresponding Author : Animesh Kumar Dubey
Recieved : 10-November-2025; Revised : 15-December-2025; Accepted : 17-December-2025
Abstract
The modern power systems are transformed due to the penetration of renewable energy sources (RESs) such as solar, wind, and hydrogen. Accurate forecasting of renewable generation, load demand, and system-level variables is crucial due to intermittency, nonlinearity, and strong weather dependency. It is important to ensure grid stability. Traditional time-series forecasting techniques used widely but shown limited capability in modeling complex nonlinear relationships and high-dimensional dependencies inherent in renewable energy data. The increasing availability of data from different sources like smart meters, weather stations, satellite imagery, and cyber-physical platforms shifted the current focus toward data-driven forecasting approaches. Artificial intelligence (AI) techniques like machine learning (ML), deep learning (DL), etc. demonstrated significant potential in data-driven forecasting. In this paper a brief review has been presented considering forecasting, optimization, and management techniques for the renewable energy systems. Forecasting models based on deep neural networks (DNNs), convolutional and recurrent architectures, transformers, ensemble learning, reinforcement learning (RL), and quantum-inspired optimization are systematically examined. Emphasis is also placed on hybrid and decomposition-based frameworks. Beyond predictive performance, this study highlights the growing integration of forecasting with optimization and control objectives, including cost minimization, energy efficiency improvement, battery lifetime extension, reliability enhancement, and carbon emission reduction. The scope, granularity, and realism of datasets employed in prior studies are also critically analyzed. The findings indicate that no optimal forecasting model exists; instead, superior performance is achieved through intelligent integration of learning, decomposition, optimization, and control strategies. The insights provided support the development of robust, scalable, and application-oriented artificial intelligence-based forecasting frameworks aligned with sustainable and resilient energy systems.
Keywords
Renewable energy forecasting, Artificial intelligence, Machine learning and Deep learning, Hybrid and ensemble models, Energy management and optimization, Power system stability.
Cite this article
Dubey AK. Advanced artificial intelligence techniques for forecasting, optimization, and management of renewable energy systems. Energy Intelligence and Sustainability. 2025;1(1):10-20. DOI : 10.19101/EIS.2025.11006