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

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.