CIPARLABS Team at IEEE WCCI 2026

 

CIPARLABS took part in IEEE WCCI 2026 with a broad set of scientific contributions and organizational activities, reflecting the group’s research agenda across Computational Intelligence, machine learning, complex systems, smart grids, bioinformatics, and sustainable energy management.

Within the Special Session «Scientific ML and Bio-Physical Sensing», the group presented the paper «Decoding Functional Multiplicity: Graph Learning Approaches to Multifunctional Proteins». The work addresses the prediction of protein multifunctionality from three-dimensional molecular structures. Proteins are represented as residue contact networks, enabling the use of graph-based learning methods, including simplicial-complex embeddings, graph kernels, and Graph Neural Networks. The study frames the problem as a multi-label classification task over first-level Enzyme Commission classes and shows that topological structural representations can capture meaningful signals related to the functional multiplicity of proteins.

Two further contributions were presented in the Special Session «Smart Energy, Grid, and Infrastructure», including IJCNN SS35 «Computational Intelligence Techniques for Observable Smart Grid and Sustainable Energy Systems».

The first paper, «Forward–Forward Learning for Imbalanced Tabular Predictive Maintenance on a Real-World Smart-Grid Fault Dataset», investigates a stabilized Forward–Forward learning formulation for predictive maintenance in medium-voltage smart grids. The study evaluates layer-local learning on an imbalanced real-world fault dataset from the Rome power distribution grid, comparing it with standard tabular baselines and back-propagation-based MLPs. The results show that Forward–Forward learning is competitive in terms of PR-AUC and F1-score, while Random Forests remain the strongest overall baseline in the considered setting. The work also analyzes calibration, goodness-margin dynamics, stabilization mechanisms, and timing performance, offering a detailed view of the practical viability of Forward–Forward learning for smart-grid fault detection.

The second paper, «Simulation of Microgrid Energy Management under Battery Degradation Costs: a PPO-Based Reinforcement Learning Approach», focuses on residential microgrid energy management with photovoltaic generation and battery storage. The study introduces a degradation-aware simulation framework in which the battery system is modeled through a Battery Management System based on an equivalent circuit model with State-of-Health-dependent parameters. A Proximal Policy Optimization controller is trained to determine battery charge and discharge setpoints and is compared with a rule-based controller and an oracle Model Predictive Control benchmark. The results show that the learned policies outperform the rule-based baseline and approach the oracle MPC behavior in medium battery-utilization regimes, balancing short-term energy costs with long-term battery degradation.

Alongside the scientific presentations, CIPARLABS also contributed to the organization of IEEE WCCI 2026 through two Special Sessions.

The Special Session «AI for Energy and Resource Analytics», chaired by Enrico De Santis, included the session «Computational Intelligence and AI Applications for Sustainable Energy Management in Smart Grids and Energy Communities», CISEM. The session was organized by Enrico De Santis, Antonello Rizzi, and Danial Zendehdel from the Department of Information Engineering, Electronics and Telecommunications at Sapienza University of Rome.

Session page
https://sites.google.com/uniroma1.it/wcci-ijcnn-cisem2026/home-page

A second Special Session, «AICS: Computational Intelligence for Complex Systems», was organized by Alessio Martino, from the Department of AI, Data and Decision Sciences at LUISS University, together with Enrico De Santis and Antonello Rizzi, from the Department of Information Engineering, Electronics and Telecommunications at Sapienza University of Rome. The session gathered contributions devoted to the use of Computational Intelligence for modeling, analyzing, and interpreting complex systems.

Session page
https://sites.google.com/uniroma1.it/wcci-ijcnn-aics2026

Overall, the participation of CIPARLABS at IEEE WCCI 2026 highlights the group’s interdisciplinary research activity, ranging from graph learning for biological systems to predictive maintenance in power infrastructures and reinforcement learning for intelligent energy management. These contributions document the group’s commitment to developing advanced Computational Intelligence methods for scientific, technological, and industrial problems with significant societal and environmental impact.

Conference Proceedings can be downloade here.

See you at IJCNN 2027 in South Africa!