会议信息

IRSES 2027: Intelligent, Resilient, and Sustainable Energy Systems

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IRSES
截稿日期:
2026-08-15 还有 32 天
通知日期:
2026-11-01
会议日期:
2027-02-19
会议地点:
Gandhinagar, Gujarat, India
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征稿

IRSES 2027 (Intelligent, Resilient, and Sustainable Energy Systems) is an academic conference held in Gandhinagar, Gujarat, India on 2027-02-19. The paper submission deadline is 2026-08-15. Acceptance notifications are sent on 2026-11-01.

Technical Tracks Submit original research across intelligent, resilient, and sustainable energy systems. 01 AI and Data-Driven Energy Systems Machine learning for energy forecasting, optimization, and control Foundation models and multimodal learning for energy data Explainable and trustworthy AI in energy systems AI for energy systems Causal inference and uncertainty quantification 02 Cyber-Physical and IoT-Enabled Energy Infrastructure Smart grids and cyber-physical energy systems IoT architectures for sensing, monitoring, and control Edge computing and real-time energy analytics Digital twins for energy systems Security and privacy in energy infrastructure 03 Resilient, Decentralized, and Market-Driven Energy Systems Grid resilience under climate and extreme events Distributed energy resources (DERs) and microgrids Demand response and transactive energy systems Energy markets, pricing, and game-theoretic models Policy-aware and regulation-driven system design 04 Sustainable Energy Technologies and Circular Systems Renewable energy systems including solar, wind, hydrogen, and hybrid systems Energy storage technologies and optimization Waste-to-energy and circular energy systems Lifecycle analysis and carbon-aware design Integrated energy systems across electric, thermal, and hydrogen domains 2D materials for photovoltaic applications AI for climate control RF energy harvesting Low-power rectennas 05 Energy Efficient and Secure Hardware Architecture for Edge AI Systems Quantum-inspired and next-generation hardware In-memory computing for AI Real-time edge intelligence systems Secure hardware design for AI systems GPU/NPU optimization for edge inference Low-power VLSI architectures for AI acceleration Energy-aware processor design for edge devices Neuromorphic and bio-inspired architectures FPGA-based AI accelerators for edge computing
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