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}
}