Summary of Tackling Missing Values in Probabilistic Wind Power Forecasting: a Generative Approach, by Honglin Wen et al.
Tackling Missing Values in Probabilistic Wind Power Forecasting: A Generative Approach
by Honglin Wen, Pierre Pinson, Jie Gu, Zhijian Jin
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 A machine learning technique has been successfully used to forecast wind power probabilities, but it neglects an issue of missing values due to sensor failures. Instead of imputing missing values before forecasting, this paper proposes predicting all unknown values simultaneously based on observations using a generative model that estimates the joint distribution of features and targets. This approach avoids introducing potential errors by not requiring preprocessing. The results show better performance in terms of continuous ranked probability score compared to traditional “impute, then predict” pipelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to improve wind power forecasts. Right now, sensors can fail or go missing, which makes it hard to get accurate predictions. Instead of fixing this problem by guessing the missing values before making a forecast, researchers suggest doing all the forecasting at once. They use a special kind of model that takes into account both the things we know and the things we don’t know. This approach works better than usual methods. |
Keywords
* Artificial intelligence * Generative model * Machine learning * Probability
