AI-Enhanced Sensing Technologies for Smart Energy Informatics and Sustainable Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".
Deadline for manuscript submissions: 25 February 2026 | Viewed by 11
Special Issue Editors
Interests: AI; machine learning; energy informatics
Interests: compiler optimizations; high performance computing; optimizing deep neural networks and embedded systems
Special Issues, Collections and Topics in MDPI journals
Interests: applications of signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The global transition toward sustainable and intelligent energy systems requires the integration of advanced sensing technologies with artificial intelligence (AI). Edge AI, namely AI processing performed locally on edge devices, could enhance energy applications by providing real-time decision-making, reduced latency, enhanced privacy, and lower energy consumption. This Special Issue aims to highlight innovative research at the intersection of AI, sensor technologies, and energy informatics, thus promoting the development of smarter, more efficient, and resilient energy infrastructures.
We welcome the submission of original research articles, reviews, and case studies that explore innovative sensor systems empowered by AI techniques for the monitoring, analysis, and optimization of energy consumption, generation, and distribution. The scope of this Special Issue includes, but is not limited to, the following topics:
- AI-driven sensor networks for smart grid monitoring and fault detection.
- Machine learning models for predictive maintenance in energy systems.
- Intelligent sensors in renewable energy management (solar, wind, hydro).
- Edge AI and IoT-enabled devices for real-time energy analytics.
- Sensor fusion and deep learning in building energy optimization.
- Anomaly detection and cybersecurity in sensor-rich energy infrastructures.
- Digital twins for energy systems using sensor and AI integration.
- AI-based demand forecasting using multi-modal sensor data.
We invite researchers, engineers, and practitioners to contribute original research articles or reviews that demonstrate how edge AI is shaping the future of energy informatics.
Dr. Pascal Schirmer
Dr. Vasilios Kelefouras
Prof. Dr. Iosif Mporas
Guest Editors
Manuscript Submission Information
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Keywords
- smart grid monitoring
- machine learning
- sensor fusion
- sensor systems
- artificial intelligence
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