Appendix_for_Short_Term_Electricity_Load_Forecasting_Based_on_Large_Language_Models_and_Weighted_External_Factor_Optimization.pdf

Accurate short-term electricity load forecasting is essential for the stable and efficient operation of modern power systems. This paper proposes a novel forecasting framework that integrates a time-decay weighted average of historical loads with external factor adjustments derived from structured data (weather and calendar) and unstructured textual sources (news). A key innovation lies in the dual role played by the Generative Pre-trained Transformer (GPT), which is used both to extract semantic features from news texts and to perform adaptive self-tuning mechanism that dynamically refines external factor scores. The model is validated through a case study using real-world data from New York State. Results show that the optimized prediction significantly improves forecasting accuracy, reducing the mean absolute error (MAE) by 90.7\% and the root mean square error (RMSE) by 88.5\% compared to the initial estimate. This work demonstrates the practical potential of large language models in energy forecasting and offers a scalable approach for integrating natural language understanding into quantitative prediction systems.精准的短期电力负荷预测，对于现代电力系统的稳定高效运行至关重要。本文提出一种新型预测框架，该框架将历史负荷的时间衰减加权平均与来自结构化数据（气象与日历数据）及非结构化文本源（新闻文本）的外部因子调整相结合。本框架的核心创新之处在于生成式预训练Transformer（Generative Pre-trained Transformer，GPT）的双重应用：其既可从新闻文本中提取语义特征，又可实现自适应自调优机制，动态优化外部因子评分。本文采用纽约州的真实世界数据开展案例研究，对所提模型进行验证。实验结果表明，经优化后的预测模型显著提升了预测精度：与初始预估结果相比，其平均绝对误差（mean absolute error，MAE）降低90.7%，均方根误差（root mean square error，RMSE）降低88.5%。本研究证实了大语言模型（Large Language Model，LLM）在能源预测领域的实用潜力，并为将自然语言理解融入定量预测系统提供了一种可扩展的解决方案。