Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Integrating renewable energy sources into modern electrical grids presents significant challenges due to the inherent variability and intermittency of solar and wind power generation systems. Traditional forecasting methods relying on single-source data streams demonstrate substantial accuracy deficiencies that compromise grid stability and operational efficiency. Multi-modal artificial intelligence represents a transformative paradigm for renewable energy forecasting by simultaneously processing satellite imagery, meteorological data, and historical generation patterns through advanced deep learning architectures. The developed framework employs convolutional neural networks for spatial feature extraction from satellite observations, recurrent components for temporal sequence modeling, and attention mechanisms for adaptive feature weighting across heterogeneous data modalities. Comprehensive experimental validation across diverse geographical regions and renewable energy technologies demonstrates significant performance improvements compared to conventional forecasting methods. The multi-modal integration enables enhanced prediction accuracy through sophisticated data fusion strategies that capture complex spatiotemporal relationships governing renewable energy generation patterns. Real-time prediction capabilities support operational grid management applications, including energy dispatch optimization and grid stability assessment under varying renewable energy penetration scenarios. Cross-regional model performance validation establishes transferability across different climatic conditions while identifying necessary adaptation procedures for maintaining forecasting effectiveness. The integrated framework addresses critical gaps in current forecasting methodologies by leveraging computer vision techniques for atmospheric condition assessment and implementing hierarchical feature learning architectures for multi-resolution data processing.