Track 13: Artificial Intelligence for Carbon Neutrality: Pioneering New Paradigms for Future Energy Systems Research 人工智能促进碳中和:为未来能源系统研究开创新范式
Organizers / 组织者
Xiaojie Lin (Associate Research Fellow)
林小杰(副研究员)
Zhejiang University / 浙江大学
Email: xiaojie.lin@zju.edu.cn
Abstract / 摘要
English: The accelerated global journey toward carbon neutrality presents profound challenges and unprecedented opportunities for the design, operation, and management of renewable and sustainable energy systems. Artificial intelligence (AI), particularly generative AI, stands at the forefront of this transformation. Its capabilities in data-driven forecasting, adaptive control, and intelligent optimization offer significant potential to mitigate carbon emissions across generation, storage, transmission, distribution, and end-use sectors. However, fully unlocking AI's potential for net-zero energy systems requires developing new methodological frameworks, fostering cross-disciplinary integration, and conducting rigorous validation in real-world scenarios.
中文: 全球迈向碳中和的加速进程,对可再生与可持续能源系统的设计、运行及管理提出了深刻挑战,同时也带来了前所未有的发展机遇。人工智能(AI),尤其是生成式人工智能,正处于这一变革的前沿。其所具备的数据驱动预测、自适应控制与智能优化能力,可显著降低发电、储能、能源输配以及终端用能等环节中的碳排放。然而,要充分释放人工智能在实现净零能源系统中的潜力,仍需发展新的方法体系,推动跨学科融合,并开展面向真实场景的应用验证。
Topics / 主题
- AI-based methods for renewable energy forecasting and uncertainty analysis
- AI-integrated intelligent scheduling and control of comprehensive energy systems
- AI methods for optimal dispatch in energy storage, hydrogen, and integrated energy systems
- AI-driven techno-economic and life-cycle analysis of low-carbon energy pathways
- AI-empowered security, privacy, and resilience issues in energy networks
- Generative AI, Large Language Models (LLMs), and their applications in energy systems
- 基于AI方法的可再生能源预测与不确定性分析
- 融合AI的综合能源系统智能调度与控制
- 面向储能、氢及综合能源系统优化调度的AI方法
- AI驱动的低碳能源路径技术经济与生命周期分析
- AI赋能的能源网络中的安全、隐私与韧性问题
- 生成式AI、大语言模型及其在能源系统中的应用
