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A Deep Learning-Driven Self-Conscious Distributed Cyber-Physical System for Renewable Energy Communities.

Giovanni Cicceri1,2, Giuseppe Tricomi1, Luca D'Agati1,3

  • 1Department of Engineering (DI), University of Messina, 98122 Messina, Italy.

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Summary
This summary is machine-generated.

This research introduces energy-aware distributed cyber-physical systems (DCPSs) for sustainable smart energy management. It develops AI-enhanced IoT models for optimizing renewable energy communities and smart grids.

Keywords:
Internet of Thingsdeep learningedge-to-cloud infrastructureenergy managementenergy-aware DCPSrenewable energy communities (RECs)smart grids

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Area of Science:

  • Cyber-Physical Systems
  • Smart Energy Management
  • Artificial Intelligence

Background:

  • Traditional distributed cyber-physical systems (DCPSs) prioritize performance over sustainability.
  • Increasing power consumption and computational costs necessitate energy-aware designs.
  • The Internet of Things (IoT) enables complex integrations, highlighting the need for sustainable solutions.

Purpose of the Study:

  • To develop energy-aware architectural models and edge/cloud computing technologies for next-generation AI-enabled DCPSs.
  • To design self-conscious, IoT-extended DCPSs that integrate sustainability attributes like energy consumption.
  • To optimize renewable energy communities (RECs) and contribute to smart grid development.

Main Methods:

  • Developed energy-aware edge-to-cloud architectural models and technologies.
  • Orchestrated federated edge-to-cloud infrastructure with unified resource models.
  • Implemented innovative machine learning algorithms for dynamic energy resource reallocation and reconfiguration.
  • Managed energy communities and validated through case studies on RECs.

Main Results:

  • Demonstrated the effectiveness of proposed energy-aware DCPSs for managing energy consumption and production.
  • Achieved optimized performance in renewable energy communities using RMSE and MAE metrics.
  • Validated the contribution of energy-aware DCPSs to sustainable smart grid development.

Conclusions:

  • The research provides a sustainable, self-consistent, and efficient approach to energy management in smart grids.
  • The developed models and technologies support the transition to a sustainable future with active community participation in energy landscapes.
  • Energy-aware DCPSs are crucial for optimizing renewable energy integration and smart grid efficiency.