Edge Cloud Continuum for Energy Communities

Dear Colleagues,

The integration of edge computing and cloud technologies has revolutionized energy systems, fostering innovative solutions for efficiency, reliability, and sustainability in energy communities. The edge cloud continuum integrates edge computing and cloud infrastructure, creating a distributed environment for low-latency data processing and optimized resource utilization. Critical to diverse energy resource management, the edge cloud continuum empowers communities by enabling real-time monitoring and control through local processing using edge devices like smart meters and IoT devices. This decentralized approach enhances grid stability and resilience.

The continuum also facilitates advanced demand response strategies, optimizing energy distribution and reducing overall consumption, thus enhancing grid reliability. Essential components include localized edge devices with machine learning capabilities, while centralized cloud infrastructure is the backbone, offering scalable storage and advanced analytics. Efficient communication protocols such as MQTT ensure a seamless exchange of data between edge devices and the cloud, which is vital for real-time energy systems decision-making. Successful case studies demonstrate the practical benefits of the edge cloud continuum, and future research directions, such as integrating artificial intelligence and blockchain, offer promising insights into the evolution of energy communities.

This Special Issue aims to explore the transformative technologies shaping the landscape of smart energy communities. Focusing on innovative solutions contributing to sustainability, efficiency, and resilience, it aims to collect cutting-edge research, industry insights, and practical applications.

Recommended topics include (but are not limited to):

Edge Cloud Computing in Smart Energy Systems

• Distributed computing for real-time data processing.
• Decentralized control and monitoring of energy assets.
• Resilience enhancement in smart energy grids.
• Edge-based demand response strategies.

Machine Learning for Intelligent Energy Management

• Predictive analytics for energy consumption patterns.
• Adaptive load management using machine learning algorithms.
• Predictive maintenance for energy assets.
• Dynamic energy pricing models and demand forecasting.

Artificial Intelligence for Sustainable Energy

• AI-driven threat detection for cybersecurity in energy systems.
• AI applications in optimizing energy distribution and grid stability.

Interdisciplinary Research and Case Studies

• Real-world implementations of integrated solutions.
• Success stories and lessons learned from smart energy communities.
• Interdisciplinary research approaches and methodologies.
• Challenges and solutions in deploying integrated systems.

Emerging Trends and Future Directions

• Synergies between edge cloud computing, ML, and AI.
• Collaborative research opportunities for academia and industry.
• Anticipated advancements and the role of technology in shaping the future of energy.
• Integration of blockchain and other emerging technologies.
• Quantum algorithms for energy optimization.
• Quantum machine learning for energy prediction.

Dr. Giandomenico Spezzano
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• edge cloud continuum
• energy communities
• distributed energy resources (DERs)
• decentralized energy management
• real-time analytics
• demand response
• IoT integration
• scalable energy systems