The CIPARLABS research group from the Department of Information Engineering, Electronics and Telecommunications (DIET) at Sapienza University of Rome participated in the 18th International Joint Conference on Computational Intelligence (IJCCI 2026), held from October 28 to 30 in Angers, France.
The group presented six scientific contributions, spanning the Neural Computation Theory and Applications (NCTA) and Explainable Artificial Intelligence (EXPLAINS) conferences. The papers reflect ongoing research on intelligent energy management, Renewable Energy Communities, reinforcement learning, time-series analysis and cybersecurity.
Although addressing different application domains, these studies share a common research perspective, combining Computational Intelligence methodologies with the challenges of developing efficient, robust and interpretable intelligent systems. Particular attention is devoted to how learning algorithms and optimization techniques can be evaluated in relation to the physical, operational and informational constraints of real-world applications.
Intelligent Energy Management and Renewable Energy Communities
A significant part of the research presented at IJCCI 2026 concerns the intelligent management of distributed energy systems, with particular emphasis on equitable resource allocation, battery degradation and explainable decision-making.
Game-Theoretic Fair Allocation in MPC-Based Management of RECs
Danial Zendehdel, Enrico De Santis, Antonello Rizzi
How can energy be allocated fairly among members of a Renewable Energy Community whose consumption patterns, generation capacities and storage resources differ?
This paper addresses the problem through a decentralized framework combining Model Predictive Control (MPC) with a cooperative allocation mechanism inspired by Nash bargaining theory. The proposed approach coordinates peer-to-peer energy exchanges according to the participants' energy needs and the costs they would incur without cooperation.
The study introduces a distinction between equality and equity in energy distribution, emphasizing the importance of allocating shared resources according to actual demand. Experiments using real household data demonstrate a 50% improvement in the need-based equity metric compared with a centralized Fair-MPC benchmark, while achieving comparable operational efficiency with approximately 75% less peer-to-peer energy trading.
These findings suggest that fairness-aware coordination can improve the distribution of renewable resources without requiring centralized control of individual energy systems.
A Reinforcement Learning Model for Microgrid Energy Management under Uncertainty and Battery Degradation
Gianluca Ferro, Alessio Orlandi, Mohammad Ali Sherafat, Enrico De Santis, Antonello Rizzi
This contribution investigates how reinforcement learning can support energy management decisions while accounting for the progressive degradation of battery storage systems.
The proposed controller employs Proximal Policy Optimization (PPO) to learn battery charging and discharging strategies that balance electricity costs against the economic consequences of battery wear. Forecast information on photovoltaic generation and electricity demand is incorporated into the decision-making process.
Through a multi-objective Pareto analysis, the learned policies are compared with rule-based control and Model Predictive Control, including an oracle MPC benchmark with perfect future information.
The experiments show that longer forecasting horizons can improve the trade-off between operating costs and battery preservation. Under selected experimental configurations, the reinforcement learning controller also achieves lower total costs than the corresponding oracle MPC strategies.
The study contributes to the development of degradation-aware intelligent control, where the long-term condition of energy storage becomes an explicit component of operational optimization.
Explainable EMS Benchmarking for Renewable Energy Communities through Reproducible Multi-Microgrid Simulation
Enrico De Santis, Gianluca Ferro, Antonello Rizzi
Presented at EXPLAINS 2026, this position paper addresses a methodological question in intelligent energy management research. How can heterogeneous Energy Management Systems be compared meaningfully when their performance depends on the operating scenario, available information and evaluation criteria?
The work introduces an explainable and reproducible benchmarking framework based on multi-microgrid simulation, enabling systematic comparisons between interpretable control strategies and supervised learning-based approaches.
The experimental study comprises 1,800 simulations, covering nine controllers, ten operating scenarios, alternative tariff and forecasting configurations, and multiple evaluation criteria.
The results reveal that controller rankings depend substantially on the selected metrics. A strategy that performs well in reducing grid imports may exhibit different behavior in terms of battery depletion or resilience, making aggregate performance scores insufficient to characterize the underlying operational decisions.
The developed platform also provides an educational environment in which students and researchers can investigate energy flows, controller behavior and performance trade-offs through transparent and reproducible experiments.
Time-Series Analysis, Forecasting and Knowledge Discovery
Two further contributions investigate complementary aspects of time-series intelligence, ranging from the discovery of recurring temporal structures to the use of anomaly information for improving predictive models.
Motif Discovery in Time Series – A Survey
Enrico De Santis, Alessio Martino, Vanessa Pennazzi, Antonello Rizzi
Identifying recurring patterns in temporal data is a fundamental problem in time-series analysis, with applications extending from industrial monitoring and energy systems to biological signals and human activity recognition.
This survey provides a systematic overview of time-series motif discovery, tracing its methodological evolution from early symbolic and approximate techniques to scalable exact algorithms and modern representation-learning approaches.
Particular attention is devoted to Matrix Profile-based methods, variable-length and multivariate motif discovery, and emerging techniques based on neural representations.
By examining algorithmic principles, computational trade-offs and available software implementations, the paper provides a reference for researchers and practitioners interested in identifying meaningful temporal regularities across heterogeneous datasets.
Enhancing Prediction Performance in Time Series Forecasting with Anomaly Signals Detected by Hyperdimensional Computing
Sabereh Taghdisi Rastkar, Saeid Jamili, Enrico De Santis, Antonello Rizzi
Irregular events and anomalous observations can substantially affect the reliability of electricity-demand forecasting, especially during periods of high consumption.
This work proposes a forecasting architecture combining Hyperdimensional Computing (HDC) with Long Short-Term Memory (LSTM) neural networks. An HDC-based anomaly detector analyzes recent consumption patterns and provides an additional contextual signal to the forecasting model, allowing it to account for potentially anomalous observations.
Experiments conducted on five years of hourly Brazilian electricity-consumption data, augmented with controlled anomaly injections, demonstrate a 14.9% reduction in forecasting RMSE over the complete test horizon and an 18.5% reduction during peak-demand intervals, compared with an equivalent LSTM model without anomaly information.
The findings indicate that lightweight anomaly representations can enhance the robustness of neural forecasting models, offering a promising direction for intelligent monitoring and predictive management in smart grids.
Neural Time-Series Classification for Cybersecurity
Severity-Aware Sliding Window Classification with Overlap for DDoS Detection
Eugenio Borrini, Enrico De Santis, Antonello Rizzi
The final contribution applies neural time-series classification to the detection of Distributed Denial-of-Service (DDoS) attacks, which threaten network availability by overwhelming communication or computational resources.
The proposed framework employs LSTM neural networks to analyze overlapping windows of network packets, introducing a severity-aware classification mechanism that considers the proportion of malicious traffic within each window.
A central methodological contribution is the adoption of soft labeling, which represents the composition of network traffic more gradually than conventional hard classification labels, improving the learning process when benign and malicious packets are interleaved.
Experiments on the CICDDoS2019 dataset demonstrate substantial improvements in detection performance. In the binary evaluation combining different attack types, classification accuracy increases from 84.8% to 99.1% when soft labeling replaces the conventional hard-labeling strategy.
The work highlights the benefits of severity-aware temporal representations for developing more reliable network intrusion detection systems.
Looking Ahead
The six contributions presented at IJCCI 2026 illustrate the breadth of CIPARLABS' research activities and the methodological connections between apparently distant application domains.
From cooperative energy management and degradation-aware reinforcement learning to temporal pattern discovery and neural cybersecurity, these studies address the relationship between intelligent decision-making, computational modeling and the interpretation of experimental evidence.
Across these research directions, reproducibility, explainability and context-aware performance evaluation emerge as recurring methodological priorities, guiding the development of Computational Intelligence techniques toward increasingly demanding scientific and technological applications.
Beyond the scientific presentations, IJCCI 2026 provided an opportunity to exchange ideas, discuss emerging research directions and strengthen connections within the international Computational Intelligence community.
We thank our co-authors, collaborators and fellow researchers for the stimulating scientific discussions and shared experiences during the conference.
See you at the next IJCCI!
Conference information
IJCCI 2026 – Official Technical Program
CIPARLABS – Sapienza University of Rome
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