大语言模型在储能研究中的应用

The application of large language models in energy storage research

在碳达峰、碳中和的大背景下,储能科学作为一门信息密集、多学科交叉的研究领域,迫切需要新研究方法以应对其日益复杂的难题与挑战。随着人工智能技术的快速发展,大语言模型在文本处理、信息收集与整合、图片与视频生成等领域取得了巨大的成功,其应用也在逐渐延伸至自然科学研究领域,并在提升科研效率等方面展现出了巨大的潜力,有望助力储能科学应对未来挑战。本文首先以ChatGPT为例,回顾了人工智能和大语言模型领域的重大进展,从社会生活和科学研究两方面分析了这些进展所产生的影响,整理了国内重点的大语言模型;然后结合储能领域的具体案例介绍大语言模型的基本概念及原理,并从信息处理、信息生成和系统集成三个方面详细探讨大语言模型在储能研究中的应用,凸显这一全新研究方法的实际效果与发展前景;最后结合具体时代背景,指出大语言模型与储能交叉研究的挑战与未来发展方向,并对这一新领域做出总结和展望。

In the pursuit of carbon neutrality,energy storage technology plays an increasingly crucial role in modern society.Addressing future challenges requires innovative methods in energy storage research,given its interdisciplinary and information-intensive nature.With the rapid development of artificial intelligence technology,large language models(LLMs)have achieved significant success in various domains,including text processing,information collection and integration,and picture and video generation.Moreover,the application of LLMs has extended to natural science research,demonstrating promising potential for improving research efficiency.Thus,LLMs are expected to assist in addressing future challenges in energy storage science and technology.This paper first focuses on ChatGPT and reviews AI advancements and LLMs,analyzing their impact on civil use and scientific research,particularly focusing on domestic LLMs.Subsequently,it discusses the basic concepts and fundamentals of LLMs and their application in energy storage,covering information processing,information generation,and system integration.Detailed examples are provided to illustrate the effectiveness of these new methods.Finally,this study outlines the remaining challenges and future development directions of the interdisciplinary nature of LLMs and energy storage.