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A Deep Learning-Driven Self-Conscious Distributed Cyber-Physical System for Renewable Energy Communities
Open accessAbstract
The Internet of Things (IoT) is transforming various domains, including smart energy management, by enabling the integration of complex digital and physical components in distributed cyber-physical systems (DCPSs). The design of DCPSs has so far been focused on performance-related, non-functional requirements. However, with the growing power consumption and computation expenses, sustainability is becoming an important aspect to consider. This has led to the concept of energy-aware DCPSs, which integrate conventional non-functional requirements with additional attributes for sustainability, such as energy consumption. This research activity aimed to investigate and develop energy-aware architectural models and edge/cloud computing technologies to design next-generation, AI-enabled (and, specifically, deep-learning-enhanced), self-conscious IoT-extended DCPSs. Our key contributions include energy-aware edge-to-cloud architectural models and technologies, the orchestration of a (possibly federated) edge-to-cloud infrastructure, abstractions and unified models for distributed heterogeneous virtualized resources, innovative machine learning algorithms for the dynamic reallocation and reconfiguration of energy resources, and the management of energy communities. The proposed solution was validated through case studies on optimizing renewable energy communities (RECs), or energy-aware DCPSs, which are particularly challenging due to their unique requirements and constraints; in more detail, in this work, we aim to define the optimal implementation of an energy-aware DCPS. Moreover, smart grids play a crucial role in developing energy-aware DCPSs, providing a flexible and efficient power system integrating renewable energy sources, microgrids, and other distributed energy resources. The proposed energy-aware DCPSs contribute to the development of smart grids by providing a sustainable, self-consistent, and efficient way to manage energy distribution and consumption. The performance demonstrates our approach's effectiveness for consumption and production (based on RMSE and MAE metrics). Our research supports the transition towards a more sustainable future, where communities adopting REC principles become key players in the energy landscape.
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![Figure 3 Example of a software-defined CPS acting as a self-conscious environment [50].](/paper-figures/2718eea123ac42d9ae0e5cd5a8ea9c8a/a-deep-learning-driven-self-conscious-distributed-cyber-physical-system-for-figure-3.jpg)
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“Carbon-intensity reduction per unit of energy consumed offers a normalized measure, comparable across different facility sizes and fuel types [25].”
From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [25]
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Metadata
- Authors
- Cicceri, G., Tricomi, G., D’Agati, L., Longo, F., Merlino, G., & Puliafito, A.
- Affiliations
- University of Messina (IT)
- Corresponding author
- Tricomi, Giuseppe
- Puliafito, Antonio
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-05-07
- Volume
- 23
- Issue
- 9
- Pages
- 4549
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10181772
- OpenAlex
- W4376630019
- Cited by
- 58
- References
- 58
- Citations by year
- 2026: 17 · 2025: 26 · 2024: 14 · 2023: 1
- FWCI
- 6.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- IRIS UniPA (University of Palermo) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- HORIZON EUROPE Framework Programme (101079774)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- distributed cyber-physical systems, energy-aware systems, renewable energy communities, smart grid, microgrids, Internet of Things, sustainability, energy optimization, resource reconfiguration
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