Computational Intelligence in Electrical Systems: 2nd Edition
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: 15 May 2025 | Viewed by 186
Special Issue Editors
Interests: deep learning; computational intelligence; smart sensor networks; quantum computing
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning techniques for time series analysis and forecasting; distributed learning algorithms; neural and fuzzy neural models for ICT and industrial applications
Special Issues, Collections and Topics in MDPI journals
Interests: electromagnetic compatibility; energy harvesting; graphene electrodynamics; numerical and analytical techniques for modeling high-speed printed circuit boards; shielding; transmission lines; periodic structures; devices based on graphene
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Electrical systems are central in the energy transition from fossil fuels to renewables. Toward this end, it is essential that final prosumers can collectively cooperate in the management of distributed energy resources (DERs) to share energy and assets. Distributed resources in an energy community can be geographically near, sharing a smart microgrid conceived as a set of renewable energy sources (RESs), loads, energy storage systems (ESSs), and electric vehicles (EVs).
In this scenario, data-driven modeling techniques play a crucial role based on the machine learning paradigm and, more generally, computational intelligence in synergy with ICT technologies that help share information across complex infrastructures. Many control, decision, and optimization problems for electrical systems should be handled with real-time constraints while involving a large amount of data in complex operation frameworks. Consequently, such tasks should be solved using distributed learning techniques, as they cannot be handled by a centralized authority (i.e., for privacy concerns, networking reliability, etc.), nor can they be carried out efficiently by human operators.
This Special Issue is intended to bring forth advances in the use of computational intelligence tools (shallow and deep neural networks, fuzzy systems, evolutionary computation, etc.) in connection with statistical machine learning and signal processing techniques to solve real-world problems related to electrical systems. Special attention should be paid to the distributed contexts of smart grid, RES, ESS, and EV infrastructures, as well as to the energy/power aspects in ICT technologies and the related applications as, for instance, hungry data centers, green computing and green networking, EMC/EMI, energy harvesting, low-power micro/nano/optoelectronic systems, and so forth. Strategic tasks are pattern analysis, data regression and classification, optimization and control, decision-making, and time series forecasting.
Prof. Dr. Massimo Panella
Dr. Antonello Rosato
Prof. Dr. Rodolfo Araneo
Guest Editors
Manuscript Submission Information
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Keywords
- smart grids, microgrids, and virtual power plants
- distributed energy resources
- renewable energy sources
- energy storage systems
- electric vehicles
- green computing and green networking
- energy harvesting
- low-power ICT systems
- neural networks
- fuzzy systems
- evolutionary computation
- deep learning
- classification and clustering
- data regression optimization and control
- time series
- forecasting
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Related Special Issue
- Computational Intelligence in Electrical Systems in Energies (8 articles)
