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Data-Driven Approaches for Enhancing Decision-Making in Low-Carbon Energy Systems
截止日期:2024年9月30日
关键词:数据驱动决策, 低碳能源系统, 能源系统优化,
机器学习应用, 决策支持系统
As the global energy sector transitions towards low-carbon solutions, there is an increasing need for advanced decision-making tools that can handle the complexity of modern power and energy systems. This special issue focuses on the role of data-driven approaches, including machine learning and optimization techniques, in improving decision-making processes in low-carbon energy systems. These methodologies are critical in addressing challenges such as integrating renewable energy sources, ensuring system reliability, and optimizing energy consumption.
Prescriptive Analytics for Decision-Making in Energy Systems Machine Learning for Energy System Optimization Reinforcement Learning for Energy Decision-Making End-to-End Learning for Energy System Management Distributed Machine Learning in Energy Networks Interpretable Machine Learning in Energy Systems Data Management for Decision-Making in Low-Carbon Energy Systems
Prof. F. Vallée,
University of Mons, Belgium
Prof. Yi Wang,
The University of Hong Kong, China
Prof. Nima Amjady,
Federation University Australia, Australia
Prof. George Kariniotakis,
MINES Paris, France
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投稿链接:https://mc03.manuscriptcentral.com/cjpes
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