Summary of Fine-tuning Pre-trained Large Time Series Models For Prediction Of Wind Turbine Scada Data, by Yuwei Fan et al.
Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data
by Yuwei Fan, Tao Song, Chenlong Feng, Keyu Song, Chao Liu, Dongxiang Jiang
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of a pre-trained large time series model, Timer, to predict Supervisory Control and Data Acquisition (SCADA) data collected from wind turbines. The model was fine-tuned on SCADA datasets sourced from two wind farms with differing characteristics and its accuracy evaluated. Additionally, the impact of data volume on the few-shot ability of the Timer was studied. The results show that the pre-trained large model does not consistently outperform other baseline models in terms of prediction accuracy, but demonstrates superior performance in a specific application study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a big AI model to predict things that happen over time, like how much energy a wind turbine will make. The model was trained on lots of different kinds of data and then tested on some real SCADA data from two wind farms. They wanted to see if the model could be used in different situations and if it would get better at making predictions if they gave it more information. What they found out is that this big AI model can actually be really good at making predictions, but only in certain situations. |
Keywords
» Artificial intelligence » Few shot » Time series
