面向电力任务的预训练LLM适配方法:多源异构数据表征学习与有监督微调OA

Large language model(LLM)has demonstrated substantial application potential in power systems.However,conventional text-based data input struggles to accurately capture the inherent structural characteristics and numerical precision of power system data,thereby limiting their effectiveness in power-related tasks.To address this issue,this paper proposes an adaptation method for pre-trained LLMs tailored to power system tasks,integrating multi-source heterogeneous data representation learning with supervised fine-tuning.First,power system data are categorized into four typical forms,snapshot measurements,time series,graph structures,and textual descriptions,and the characteristic attributes of each type are analyzed.Then,a unified representation framework for multi-source heterogeneous data is constructed,in which feature extraction and semantic alignment techniques are employed to achieve efficient and lossless mapping of heterogeneous data into the semantic space of the LLM.Finally,experimental results from load forecasting and distribution network state estimation demonstrate that effectively leveraging pre-trained LLMs significantly improves task accuracy.In the load forecasting task,the proposed method reduces prediction error by 8.0％on average compared to traditional methods.In the distribution network state estimation task,the proposed method with only 8％measurement ratio achieves the same estimation accuracy as baseline methods with 16％measurement ratio,while the training process can be completed on consumer-grade graphics processing units.