Submission Deadline: 30 April 2027 View: 79 Submit to Special Issue
Prof. Dr. Carlos Vargas-Salgado
Email: carvarsa@upvnet.upv.es
Affiliation: Instito de ingeniería energética, Universitat Politecnica de Valencia, Valencia, Spain
Research Interests: hybrid energy systems, microgrids, PV systems, wind systems, storage systems
Dr. Dácil Diaz-Bello
Email: dadiabel@upvnet.upv.es
Affiliation: Instituto de Ingeniería Energética, Universitat Politecnica de Valencia, Valencia, Spain
Research Interests: bio-inspired algorithms, AI, genetic algorithms, energy systems, artificial neural networks
Accurate forecasting of power generation, electricity demand, and energy-related variables is a cornerstone for the reliable, resilient, and efficient operation and management of modern power systems. The rapid increase in renewable energy integration, electrification of transport, distributed energy resources, and active demand-side participation introduces significant variability and uncertainty, challenging both system operation and planning. As a result, traditional forecasting and management techniques are increasingly insufficient to support real-time operation, network management, and strategic decision-making.
In this context, advanced artificial intelligence (AI), machine learning, and data-driven approaches have emerged as key enablers to improve forecasting accuracy and to support intelligent grid and energy management. These techniques allow system operators, aggregators, and energy managers to anticipate system states, optimize resource allocation, and enhance the flexibility and robustness of power systems at different spatial and temporal scales.
The aim of this Special Session is to provide a forum for recent advances in power, load, and energy forecasting and their integration into grid operation and energy management frameworks, with special emphasis on AI-based, hybrid, and uncertainty-aware approaches. The scope covers short-, medium-, and long-term forecasting and management applications in power systems, smart grids, microgrids, and integrated energy systems, considering both centralized and decentralized operational environments.
This Special Session seeks to bridge methodological developments with real-world applications, fostering the exchange of innovative models, comparative studies, and practical implementations. Contributions addressing uncertainty quantification, explainable AI, and the embedding of forecasting models into decision-making, control, optimization, and grid management strategies are particularly encouraged.
Suggested Themes
· Power and load forecasting for grid operation and planning in smart grids and microgrids
· Renewable generation forecasting (solar, wind, and hybrid systems) and its impact on grid management
· AI, machine learning, and deep learning techniques for energy forecasting and network operation
· Probabilistic and uncertainty-aware forecasting for power system security and resilience
· Hybrid physics-based and data-driven models for forecasting and grid management
· Forecast-driven energy management systems and optimal grid operation
· Forecasting and management of energy storage systems, EV charging infrastructure, and demand response
· Explainable AI, model robustness, and trustworthiness in forecasting and grid decision support
· Integration of forecasting models into control, optimization, and power system management frameworks
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