Summary of Bias Correction Of Wind Power Forecasts with Scada Data and Continuous Learning, by Stefan Jonas et al.
Bias correction of wind power forecasts with SCADA data and continuous learning
by Stefan Jonas, Kevin Winter, Bernhard Brodbeck, Angela Meyer
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 transition to renewable energy sources is crucial, but wind power’s uncertainty and variability hinder its growth. To mitigate these challenges, machine learning-based models can be employed for applications like power management or maintenance scheduling. This paper presents and compares four machine learning models for 48-hour wind power forecasting, using datasets from a wind park with 65 turbines. The best model is a convolutional neural network that reduces the average NRMSE to 22% and mean bias, outperforming a baseline model using uncorrected numerical weather prediction forecasts. Our findings suggest that changes to neural network architectures have little impact on forecasting performance, while continuous learning strategies can achieve significant improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Renewable energy is the future, but wind power has its challenges. One problem is that wind is unpredictable, making it hard to know how much energy we’ll get from it. To solve this issue, scientists are using special computer models to predict when and where the wind will be strong or weak. This paper compares four different kinds of computer models to see which one works best for predicting wind power over a 48-hour period. The best model is like a super-smart camera that takes pictures of the wind patterns and uses them to make more accurate predictions. By improving these predictions, we can make better decisions about how to use our renewable energy resources. |
Keywords
* Artificial intelligence * Machine learning * Neural network
