Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data

TL;DR

This paper investigates the use of a pre-trained large time series model, Timer, for wind turbine SCADA data prediction, highlighting its few-shot learning capabilities and rapid deployment advantages in industrial settings.

Contribution

It demonstrates the application and evaluation of a pre-trained large time series model in wind energy forecasting, emphasizing its few-shot learning and generalization abilities.

Findings

Pre-trained Timer model shows superior performance in application studies.

Model's accuracy is not always better than baselines with abundant data.

Pre-trained model enables swift deployment in industrial contexts.

Abstract

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.