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!

Degradation-Aware Reinforcement Learning for Smarter Energy Management at IJCCI 2025, Marbella (Spain)

 

Towards Sustainable and Intelligent Microgrids

At the International Joint Conference on Computational Intelligence (IJCCI 2025), held in Marbella (Spain) from 22 to 24 October, CIPARLABS presented new research on degradation-aware energy management for residential microgrids. The paper, authored by Danial Zendehdel, Gianluca Ferro, Enrico De Santis, and Antonello Rizzi, introduces a reinforcement learning framework that intelligently manages battery usage to balance economic efficiency and battery longevity.

Residential microgrids and Renewable Energy Communities are redefining the energy landscape by promoting decentralized and cooperative energy production. Within these systems, lithium-ion batteries play a crucial role in storing excess solar power and ensuring supply continuity. Yet, their performance is constrained by degradation processes that reduce both capacity and lifespan.

Traditional control strategies often neglect this degradation, focusing only on short-term cost optimization. The work presented by the CIPARLABS team moves beyond that limitation by embedding battery health awareness directly into the control policy.

Reinforcement Learning Meets Battery Physics

The study proposes a Reinforcement Learning (RL) framework based on the Soft Actor-Critic (SAC) algorithm, implemented with Stable Baselines3, to learn optimal energy dispatch policies in real time.

What sets this approach apart is the explicit integration of battery State of Health (SoH) feedback into the agent’s learning loop. The RL agent learns to maximize long-term economic rewards while implicitly minimizing degradation, guided by a simplified but empirically calibrated degradation model derived from NASA’s Li-ion cell datasets.

The microgrid environment modeled in the study includes photovoltaic generation, household consumption profiles, and time-of-use electricity tariffs. The RL agent decides, at every timestep, whether to charge or discharge the battery or exchange energy with the grid. Its performance was benchmarked against a Model Predictive Control (MPC) strategy based on Mixed-Integer Linear Programming.

Learning to Preserve Energy and Battery Life

Simulations were conducted using high-resolution load and solar data from the Pecan Street Inc. Dataport. Over both one-year and ten-year scenarios, the RL-based controller demonstrated remarkable improvements over the MPC baseline.

• For a typical household, the RL agent extended battery life by up to 6.3 % compared to the MPC benchmark.

• It reduced energy purchased from the grid by 45–60 %, while maintaining or improving economic performance.

• Over long-term simulations, the degradation-aware SAC agent lowered total battery wear cost by 6.4 %, reflecting more efficient use of the storage system without compromising availability.

These outcomes reveal that the RL framework not only optimizes daily dispatch decisions but also learns non-linear, context-dependent policies that capture the intricate balance between short-term gain and long-term sustainability.

Implications and Future Work

The results suggest a promising pathway for deploying AI-driven, degradation-aware control systems in residential and community microgrids. Such systems could operate autonomously, adapting to changing conditions and maximizing both user savings and battery lifespan.

The research team plans to extend this work through real-world validation and multi-agent reinforcement learning experiments, where multiple prosumers within a Renewable Energy Community coordinate energy exchanges. The framework is being developed within the MOST – Sustainable Mobility Center and supported by European Union Next-GenerationEU funding under the Italian PNRR program.

Danial Zendehdel at IJCCI 2025

This work continues CIPARLABS’ mission to merge computational intelligence and sustainable energy research, paving the way for smarter, more resilient energy ecosystems.

Cite as:

@inproceedings{Zendehdel2026IJCCI,
  author    = {Danial Zendehdel and Gianluca Ferro and Enrico De Santis and Antonello Rizzi},
  title     = {Degradation-Aware Energy Management in Residential Microgrids: A Reinforcement Learning Framework},
  booktitle = {Proceedings of the 17th International Joint Conference on Computational Intelligence (IJCCI 2025)},
  year      = {2026},
  address   = {Marbella, Spain},
  month     = {October 22--24},
  publisher = {SCITEPRESS -- Science and Technology Publications},
  keywords  = {Reinforcement Learning, Battery Management System, Energy Management, Lithium-ion Batteries, Degradation Modeling, Microgrids},
  note      = {(Presented at IJCCI 2025, Marbella, Spain)},
}

Solar and Wind Forecasting: Rethinking the Future with a Multi-Site Mindset

 

A review of solar and wind energy forecasting: From single-site to multi-site paradigm

The global energy transition is no longer a distant vision — it’s unfolding now, rapidly, and with it comes a crucial question: how do we predict the unpredictable? When it comes to solar and wind energy, forecasting isn’t just a technical detail; it’s a keystone of modern energy systems. In a new paper just published in Applied Energy, Alessio Verdone, Massimo Panella, Enrico De Santis, and Antonello Rizzi from Sapienza University of Rome offer a timely and in-depth exploration of how forecasting methods have evolved to meet this challenge.

Their work is more than just a literature review. It’s a methodological study around the transformation of forecasting paradigms, tracing the field’s progress from early single-site statistical models to the latest deep learning architectures that analyze spatio-temporal data from entire networks of plants. The authors shed light on how our understanding — and our tools — have shifted alongside the growing complexity of renewable energy infrastructures.

At the heart of the paper is a simple but powerful idea: renewable energy production is no longer a local matter. Today’s systems consist of distributed solar panels and wind farms spread across vast areas. By treating each site in isolation, we miss the chance to capture valuable correlations between them. This is where multi-site forecasting comes into play, allowing models to learn not just from the past of a single plant, but from the coordinated behavior of many. And thanks to innovations in machine learning—particularly Graph Neural Networks, Transformers, and hybrid architectures — we now have the tools to make this possible.

The paper is rich with insights. It offers a structured classification of forecasting methods and benchmarks, highlights the most commonly used datasets (and the difficulty in accessing reliable public data), and discusses the metrics used to evaluate performance. But what makes this work stand out is the authors’ critical perspective. They don’t just describe methods — they ask what works, what doesn’t, and why. Their analysis of how spatial and temporal data can be integrated to boost performance speaks directly to current needs in grid management and renewable energy communities.

For researchers, engineers, and energy planners, this review is a valuable resource. It connects the dots between methodological innovation and practical application, offering a clear picture of where the field stands and where it’s heading. More importantly, it invites readers to think systemically: to see renewable energy forecasting not as a single algorithmic task, but as a complex, multi-layered problem with implications for sustainability, policy, and technology.

If you’re interested in the intersection of AI and energy, or if you’re working on Smart Grids or Renewable Energy Communities, forecasting tools, or the design of future energy systems, this is a paper worth diving into.

Read the full paper here!

2025 - Studies in Computational Intelligence (SCI, volume 1196, Springer) finally published

 

Finally the collection "Studies in Computational Intelligence (SCI, volume 1196)" has been published, concerning the book chapter extensions of our works selected by IJCCI: International Joint Conference on Computational Intelligence (2022).

Our contributions concern the context of energy sustainability, Smart Grids and Renewable Energy Communities, in particular in modeling and control techniques and energy forecasting.

Specifically the two study are the following:

1) Antonino Capillo, Enrico De Santis , Fabio Massimo Frattale Mascioli , and Antonello Rizzi, On the Performance of Multi-Objective Evolutionary Algorithms for Energy Management in Microgrids

Abstract. In the context of Energy Communities (ECs), where energy flows among PV generators, batteries and loads have to be optimally managed not to waste a single drop of energy, relying on robust optimization algorithms is mandatory. The purpose of this work is to reasonably investigate the performance of the Fuzzy Inference System-Multi-Objective-Genetic Algorithm model (MO-FIS-GA), synthesized for achieving the optimal Energy Management strat-egy for a docked e-boat. The MO-FIS-GA performance is compared to a model composed of the same FIS implementation related to the former work but opti-mized by a Differential Evolution (DE) algorithm – instead of the GA – on the same optimization problem. Since the aim is not evaluating the best-performing optimization algorithm, it is not necessary to push their capabilities to the max. Rather, a good meta-parameter combination is found for the GA and the DE such that their performance is acceptable according to the technical literature. Results show that the MO-FIS-GA performance is similar to the equivalent MO-FIS-DE model, suggesting that the former could be worth developing. Further works will focus on proposing the aforementioned comparison on different optimiza-tion problems for a wider performance evaluation, aiming at implementing the MO-FIS-GA on a wide range of real applications, not only in the nautical field.

2) Sabereh Taghdisi Rastkar , Danial Zendehdel , Antonino Capillo , Enrico De Santis, and Antonello Rizzi, Seasonality Effect Exploration for Energy Demand Forecasting in Smart Grids

Abstract. Effective energy forecasting is essential for the efficient and sustain-able management of energy resources, especially as energy demand fluctuates significantly with seasonal changes. This paper explores the impact of seasonal-ity on forecasting algorithms in the context of energy consumption within Smart Grids. Using three years of data from four different countries, the study evaluates and compares both seasonal models – such as Seasonal Autoregressive Integrated Moving Average (SARIMA), Seasonal Long Short-Term Memory (Seasonal-LSTM), and Seasonal eXtreme Gradient Boosting (Seasonal-XGBoost) – and their non-seasonal counterparts. The results demonstrate that seasonal models outperform non-seasonal ones in capturing complex consumption patterns, offer-ing improved accuracy in energy demand prediction. These findings provide valu-able insights for energy companies or in the design of intelligent Energy Manage-ment Systems, suggesting optimized strategies for resource allocation and under-scoring the importance of advanced forecasting methods in supporting sustain-able energy practices in urban environments. 

BibTex book citation:

@book{back2025computational,
  editor    = {Thomas B{\"a}ck and Niki van Stein and Christian Wagner and Jonathan M. Garibaldi and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Faiyaz Doctor and Joaquim Filipe and Kevin Warwick and Janusz Kacprzyk},
  title     = {Computational Intelligence: 14th and 15th International Joint Conference on Computational Intelligence (IJCCI 2022 and IJCCI 2023) Revised Selected Papers},
  year      = {2025},
  publisher = {Springer},
  series    = {Studies in Computational Intelligence},
  volume    = {1196},
  doi       = {10.1007/978-3-031-85252-7}
}
 

Single chapter BibTex references:

@incollection{chapter1IJCCI2025,
  author    = {Author Name},
  title     = {Computational Intelligence: 14th and 15th International Joint Conference on Computational Intelligence (IJCCI 2022 and IJCCI 2023) Revised Selected Papers},
  booktitle = {Computational Intelligence},
  editor    = {Thomas B{\"a}ck and Niki van Stein and Christian Wagner and Jonathan M. Garibaldi and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Faiyaz Doctor and Joaquim Filipe and Kevin Warwick and Janusz Kacprzyk},
  publisher = {Springer},
  year      = {2025},
  chapter   = {1},
  pages     = {1--10},
  doi       = {10.1007/978-3-031-85252-7_1}
}
 

@incollection{rastkar2025seasonality,
  author    = {Sabereh Taghdisi Rastkar and Danial Zendehdel and Antonino Capillo and Enrico De Santis and Antonello Rizzi},
  title     = {Seasonality Effect Exploration for Energy Demand Forecasting in Smart Grids},
  booktitle = {Computational Intelligence: 14th and 15th International Joint Conference on Computational Intelligence (IJCCI 2022 and IJCCI 2023) Revised Selected Papers},
  editor    = {Thomas B{\"a}ck and Niki van Stein and Christian Wagner and Jonathan M. Garibaldi and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Faiyaz Doctor and Joaquim Filipe and Kevin Warwick and Janusz Kacprzyk},
  publisher = {Springer},
  year      = {2025},
  series    = {Studies in Computational Intelligence},
  volume    = {1196},
  pages     = {211--223},
  doi       = {10.1007/978-3-031-85252-7_12},
  url       = {https://link.springer.com/chapter/10.1007/978-3-031-85252-7_12}
}
 

Nuove Normative e Trend per le Rinnovabili e i Sistemi di Accumulo in Italia

 

La transizione energetica in Italia sta vivendo una fase fondamentale, con nuove normative e trend che stanno ridefinendo il panorama delle rinnovabili e dei sistemi di accumulo. Due sviluppi recenti meritano particolare attenzione: l'approvazione del Testo Unico sulle Rinnovabili e i dati aggiornati sul mercato dei sistemi di accumulo. Entrambi offrono una panoramica delle sfide e delle opportunità per il settore energetico nazionale.

Testo Unico sulle Rinnovabili: semplificazione e nuove regole

Approvato dal Consiglio dei Ministri, il Testo Unico sulle Rinnovabili (o Testo Unico FER) entrerà in vigore il 30 dicembre 2024. L'obiettivo principale è semplificare i complessi iter burocratici per la costruzione e la gestione di impianti rinnovabili, attraverso tre regimi amministrativi:

1. Attività libera:

   • Esenzione da permessi e autorizzazioni per interventi che non interferiscono con beni tutelati o opere pubbliche.

   • Applicabile a impianti fotovoltaici fino a 12 MW (integrati) o 1 MW (a terra), turbine eoliche singole, impianti agrivoltaici fino a 5 MW e altre configurazioni specifiche.

   • Richiesta una cauzione per interventi su suoli non antropizzati.

2. Procedura abilitativa semplificata (PAS):

   • Richiede una dichiarazione di disponibilità delle superfici, minimizzazione dell’impatto paesaggistico e polizza fideiussoria per i costi di ripristino.

   • Prevede oneri e compensazioni territoriali per impianti con potenza superiore a 1 MW.

   • Decadenza del titolo abilitativo in caso di mancato avvio o conclusione dei lavori entro i termini stabiliti.

3. Autorizzazione unica (AU):

   • Competenza regionale per impianti fino a 300 MW; ministeriale per impianti offshore o >300 MW.

   • Include obblighi di ripristino e validità minima di 4 anni.

Zone di accelerazione: Entro maggio 2025 il GSE pubblicherà una mappatura delle aree disponibili per impianti rinnovabili. Regioni e Province Autonome adotteranno entro febbraio 2026 piani per semplificare ulteriormente gli iter autorizzativi.

Sistemi di Accumulo: flessione e opportunità

Il mercato italiano dei sistemi di accumulo sta vivendo dinamiche contrastanti. Dopo il boom legato al Superbonus, il segmento residenziale ha registrato un netto rallentamento, mentre il settore utility ha mostrato una crescita significativa.

Dati principali 2024

• Segmento residenziale:

  • Calo del 25% nelle installazioni, -31% in potenza e -29% in capacità rispetto al 2023.

• Settore commerciale e industriale (C&I):

  • Riduzione del 18% nelle installazioni, -29% in potenza e -11% in capacità rispetto all’anno precedente.

• Scala utility:

  • Crescita esponenziale con +133% nelle installazioni, +532% in potenza e +2877% in capacità, trainata da progetti del capacity market e iniziative merchant non incentivati.

Criticità normative

• La fine del Superbonus e le modifiche nelle detrazioni fiscali hanno inciso negativamente sul segmento residenziale.

• Il Testo Unico Rinnovabili presenta incertezze sugli iter autorizzativi per i sistemi di accumulo, con possibili conflitti di competenza tra amministrazioni.

• Anie Rinnovabili propone che le nuove norme si applichino solo ai progetti futuri e richiede armonizzazione normativa entro sei mesi.

Dati cumulati a settembre 2024

• Sistemi di accumulo installati: 692.386 unità.

• Potenza complessiva: 5.034 MW.

• Capacità massima: 11.388 MWh.

Si può concludere che le rinnovabili e i sistemi di accumulo sono al centro della transizione energetica italiana. Mentre il Testo Unico sulle Rinnovabili promette di semplificare le procedure, il mercato dei sistemi di accumulo riflette le sfide legate alla normativa e alla fine di incentivi chiave. Tuttavia, i dati su scala utility dimostrano il potenziale di crescita del settore, segnalando opportunità per il futuro.

 

Fonte 1

Fonte 2