Manuscript Topics

Load forecasting plays a crucial role in optimizing the operation of power systems, enhancing grid stability, and achieving efficient energy management. With the rapid integration of renewable energy sources and the increasing complexity of demand-side dynamics, traditional forecasting methods face challenges in handling nonlinear patterns, real-time adaptability, and multi-source data fusion. Artificial intelligence techniques, such as machine learning, deep learning, and hybrid AI models, have shown great potential in addressing these challenges by leveraging high-dimensional data, improving forecasting accuracy, and enabling proactive decision-making. Recent advances in AI-driven load forecasting have focused on addressing key issues such as computational efficiency, computational complexity, interpretability, uncertainty quantification, and seamless integration with emerging smart grid technologies.

This special issue aims to collect, showcase, and disseminate cutting-edge research findings on AI-based load forecasting methods and their applications in modern power systems. Interested topics include, but are not limited to:

• AI-driven renewable power forecasting models for solar and wind energy generation
• Integrated load and renewable power forecasting frameworks for grid balancing
• Novel AI/ML theories and frameworks for short-term and long-term load forecasting
• AI models with low computational complexity to meet real-time forecasting needs
• Hybrid models combining physical-based methods and data-driven AI techniques
• Uncertainty quantification and probabilistic load forecasting
• Load forecasting for renewable energy resources and microgrids
• Transfer learning and federated learning for cross-regional load forecasting
• AI applications in demand response and peak load management
• Edge computing-based AI solutions for real-time renewable power forecasting in distributed systems
• Interpretable AI models for transparent and explainable load forecasting
• Adaptive AI load forecasting models for extreme weather or grid emergencies
• Integration of AI-based load forecasting with energy market optimization

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