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.

翻译：大型模型在自然语言处理（NLP）与计算机视觉（CV）领域的显著成就，激发了将其应用于工业场景时间序列预测的研究兴趣。本文探讨了一种预训练大型时间序列模型Timer在风力涡轮机监控与数据采集（SCADA）数据预测中的应用。该模型最初在跨多个领域的广泛时间序列数据上进行训练，本研究使用来自两个具有不同特征的风场的SCADA数据集对其进行微调，并评估其预测精度。此外，通过研究数据量的影响，评估了Timer的小样本学习能力。最后，实施了一项面向全场预测的单机微调应用研究，该场景同时要求模型具备小样本学习与跨机组泛化能力。结果表明，无论数据是否充足，该预训练大型模型在预测精度方面并未始终优于其他基线模型，但在应用研究中展现出更优性能。这一发现凸显了预训练大型时间序列模型在促进快速部署方面的独特优势。