SS 04
The rapid expansion of Renewable Energy Systems (RESs), such as solar Photovoltaic (PV) and wind power has significantly increased the importance of accurate forecasting for reliable grid operation, energy trading, and optimal energy management. However, the inherent intermittency and uncertainty associated with RESs make forecasting problematic. Traditional statistical models often fail to capture the complex mathematical “nonlinear” relationships between timestamp, weather variables, system conditions, and the associated power production. These forecasting challenges can lead to sudden power imbalances and added strain on grid operations.
Recent advances in data-driven soft computing techniques have demonstrated strong potential to address these challenges. For instance, Machine Learning, Deep Learning (DL), and Artificial Intelligence (AI) approaches, e.g., Artificial Neural Networks (ANNs), Echo State Networks (ESNs), and Ensemble Learning, have been successfully proposed and successfully applied in practice to improve forecasting accuracy and robustness. Additionally, modern optimization techniques and metaheuristic algorithms such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Genetic Algorithm (GA), and emerging nature-inspired optimizers have enhanced model hyperparameter tuning and feature selection processes, thereby boosting the forecasting accuracy and robustness. The renewable energy sector is advancing due to better data and faster computing, which helps forecasting models learn more accurately and operate closer to real time. These improvements allow operators to respond quickly to changing solar or wind conditions, making reliable forecasting tools increasingly important.
The aim of this special session is to bring together researchers and practitioners to share, exchange and communicate their current challenges and problems, their original and high-quality solutions in the field of data-driven modeling, forecasting, and optimization methods for RESs, demonstrating how soft computing approaches can enhance renewable energy prediction and system performance in practice, for solar, wind, and other renewable energy technologies. It will also offer space for discussion on emerging trends, practical deployment issues, and lessons learned from real‑world implementations. By gathering multiple perspectives, the session seeks to advance the development of accurate, scalable, and reliable forecasting solutions.
Topics of interest include, but are not limited to:
- RESs, including solar and wind systems
- Data-driven soft computing methods for renewable energy forecasting
- Machine learning, deep learning, and artificial intelligence techniques for renewable energy forecasting
- Metaheuristic optimization for model hyberparameter tuning and feature selection
- Short-, medium-, and long- term energy forecasting models
- Hybrid physical-data driven forecasting approaches
- Uncertainty quantification and probabilistic forecasting
- Data analytics and big data methods for RESs
- Smart grid applications and renewable energy integration
- Real-world industrial and operational applications of AI-based forecasting
The session aims to attract high-quality contributions that demonstrate both theoretical developments, and practical implementations and deployment of data-driven soft computing forecasting methods in RESs. Contributions demonstrating real‑world performance, operational challenges, and measurable improvements in forecasting accuracy are especially valuable, as they help develop reliable, scalable forecasting solutions for modern RESs.
