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Distributed Optimization and Machine Learning for Energy Internet
截止日期:2024年9月15日
The energy sector is currently experiencing a profound shift, driven by the integration of renewable energy, the rise of Al technologies, and the growing focus on sustainability. As energy systems increasingly incorporate distributed resources, which are becoming more decentralized and complex, the need for robust and intelligent solutions is paramount. Distributed optimization and machine learning have emerged as critical tools that can significantly influence the development of the energy internet. These approaches allow for the creation of adaptive, resilient, and sustainable systems that can efficiently respond to changing conditions. The intricate nature of modern energy systems presents a diverse array of challenges that require thorough investigation and innovative approaches. These challenges span numerous aspects, including system architecture, strategic planning, fault detection, optimization, and control, etc. This Special Session is dedicated to exploring and sharing the latest research and advancements in the design and development of resilient energy systems, with a strong emphasis on the application of distributed optimization and machine learning techniques.
New reference architectures of energy internet
Distributed energy management for energy internet
Design of distributed optimization algorithms and applications in energy internet
Applications of machine learning in energy monitoring, diagnosis, prediction, planning, and control
Digital twins of energy internet and their applications
Design of digital platforms and testbeds for energy internet
投稿链接:https://easychair.org/conferences/?conf=ei22024(阅读原文)
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