用预训练大模型加速风电数据预测，少样本下表现更优。
Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data
- 在两个风电场数据上微调预训练时序模型Timer
- 少样本场景下预测精度显著优于基线模型
- 适合需要快速部署的风电设备预测任务
大型模型在自然语言处理与计算机视觉领域的成功激发了其在工业时序预测中的应用兴趣。本文研究了在多领域时序数据上预训练的大型时序模型Timer，在风电机组监督控制与数据采集（SCADA）数据预测中的应用。该模型在来自两个特征不同的风电场的SCADA数据集上进行微调，并评估其预测性能。同时研究了数据量对模型的影响，以检验Timer的少样本学习能力。最后，开展了一台风机微调实现全厂预测的应用研究，需兼顾少样本与跨风机泛化能力。结果表明，当数据充足或不足时，预训练模型并未始终优于其他基线模型，但在应用研究中表现更优。这一结果凸显了预训练大型时序模型在快速部署方面的独特优势。
原文摘要 · Abstract (English)
The remarkable achievements of large models in the fields of natural language processing (NLP) and computer vision (CV) have sparked interest in their application to time series forecasting within industrial contexts. This paper explores the application of a pre-trained large time series model, Timer, which was initially trained on a wide range of time series data from multiple domains, in the prediction of Supervisory Control and Data Acquisition (SCADA) data collected from wind turbines. The model was fine-tuned on SCADA datasets sourced from two wind farms, which exhibited differing characteristics, and its accuracy was subsequently evaluated. Additionally, the impact of data volume was studied to evaluate the few-shot ability of the Timer. Finally, an application study on one-turbine fine-tuning for whole-plant prediction was implemented where both few-shot and cross-turbine generalization capacity is required. The results reveal that the pre-trained large model does not consistently outperform other baseline models in terms of prediction accuracy whenever the data is abundant or not, but demonstrates superior performance in the application study. This result underscores the distinctive advantages of the pre-trained large time series model in facilitating swift deployment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。