Summary of On Autoregressive Deep Learning Models For Day-ahead Wind Power Forecasting with Irregular Shutdowns Due to Redispatching, by Stefan Meisenbacher et al.
On autoregressive deep learning models for day-ahead wind power forecasting with irregular shutdowns due to redispatching
by Stefan Meisenbacher, Silas Aaron Selzer, Mehdi Dado, Maximilian Beichter, Tim Martin, Markus Zdrallek, Peter Bretschneider, Veit Hagenmeyer, Ralf Mikut
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP); Machine Learning (stat.ML)
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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 This paper investigates the challenges of forecasting wind power generation for electrical power grid stability, particularly in the context of renewable energy integration. The authors highlight the need for scalable and automated forecasting models to predict wind power availability for redispatch planning, considering irregular shutdowns that can impact forecast accuracy. They compare three autoregressive Deep Learning methods with WP curve modeling approaches on datasets with regular and irregular shutdowns. The results show that WP curve modeling methods achieve lower forecasting errors, require less data cleaning, and are computationally more efficient. This study contributes to the development of practical wind power forecasting solutions for grid stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wind energy is important for keeping our power grids stable. To predict how much wind power we’ll have, we need good forecasts. But sometimes, wind turbines shut down unexpectedly, which makes it harder to forecast. The authors looked at different ways to make these predictions and found that a simpler approach works better than more complicated ones. This approach is faster and easier to use, making it useful for real-world applications. |
Keywords
» Artificial intelligence » Autoregressive » Deep learning
