储能科学与技术2025，Vol.14Issue(2)：467-478,12.DOI:10.19799/j.cnki.2095-4239.2025.0189
DeepSeek在储能研究中的应用前景展望
A perspective on DeepSeek application in energy storage research
摘要
Abstract
During the global energy system's transition to renewable energy,energy storage technology has emerged as the core regulatory unit of new power systems,yet it faces multifaceted challenges,including inefficient material development,complex system optimization,lagging safety management,and imperfect market mechanisms.The DeepSeek large language model,with its low energy consumption,high efficiency,and exceptional reasoning capabilities,proffers an innovative pathway to address critical bottlenecks in energy storage.Through core technologies such as multi-head latent attention,DeepSeek mixture-of-experts models,and multi-token prediction,DeepSeek significantly reduces energy costs in both model training and inference.Its broad application prospects in energy storage research are expected to drive a paradigm shift from"trial-and-error"to"intelligent design"in materials development,establish multi-scale coupled digital twin frameworks for system optimization,transform safety management from passive response to proactive early warning,and create data-driven dynamic market evaluation systems for policy analysis.The"system symbiosis and energy-efficiency co-evolution"development paradigm provides a technological foundation for the deep integration of artificial intelligence with clean energy technologies,potentially accelerating the construction of carbon-neutral computing infrastructure and ushering energy storage technology into an intelligent new era.关键词
深度求索大模型/大语言模型/人工智能/储能技术Key words
DeepSeek/large language model/artificial intelligence/energy storage technology分类
信息技术与安全科学引用本文复制引用
高宇辰,李蔚林,陈翔,袁誉杭,牛艺琳,张强..DeepSeek在储能研究中的应用前景展望[J].储能科学与技术,2025,14(2):467-478,12.基金项目
国家自然科学基金项目(T2322015),国家重点研发计划项目(2021YFB2500300),科学探索奖. （T2322015）
