Advanced Forecasting Methods for Sustainable Power Systems

Dear Colleagues,

The increasing penetration of renewable energy sources (RES), particularly wind and photovoltaic (PV) systems, is transforming modern power systems and is introducing significant variability and uncertainty in both generation and demand. Unlike conventional dispatchable units, RES are inherently dependent on meteorological conditions, making accurate forecasting a key enabler for ensuring system reliability, flexibility, and efficient operation.

In this context, advanced forecasting methods play a crucial role in supporting power system planning, operation, and control. The integration of data-driven approaches, such as machine learning and deep learning techniques, with physical and hybrid models, offers new opportunities to improve the accuracy and robustness of short-term and long-term predictions. 

This Topic aims to gather contributions from academia and industry focusing on innovative forecasting methodologies for sustainable power systems. Topics of interest include, but are not limited to, renewable generation forecasting, demand prediction, uncertainty quantification, probabilistic approaches, and their applications to enhance resilience, stability, and energy management in future low-carbon power systems.

Dr. Cristina Ventura
Dr. Santi Agatino Rizzo
Topic Editors