Summary of Hiformer: Hybrid Frequency Feature Enhancement Inverted Transformer For Long-term Wind Power Prediction, by Chongyang Wan et al.
Hiformer: Hybrid Frequency Feature Enhancement Inverted Transformer for Long-Term Wind Power Prediction
by Chongyang Wan, Shunbo Lei, Yuan Luo
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 increasing severity of climate change necessitates a transition to renewable energy sources, making large-scale wind energy adoption crucial for mitigating environmental impact. However, the uncertainty of wind power poses challenges for grid stability, requiring accurate wind energy prediction models for effective power system planning and operation. Long-term predictions are essential for power grid dispatch and market transactions, but existing short-term forecasting methods may lead to inaccurate results and high computational costs in long-term settings. To address these limitations, we propose Hiformer, a novel approach that integrates signal decomposition technology with weather feature extraction technique to enhance the modeling of correlations between meteorological conditions and wind power generation. Compared to state-of-the-art methods, Hiformer improves prediction accuracy by up to 52.5% and reduces computational time by up to 68.5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Climate change is getting worse, so we need to switch to renewable energy sources like wind power. But predicting how much wind power will be available is tricky because weather conditions can affect the output. Right now, most predictions are only good for a short period, but we need accurate long-term forecasts to make sure our power grid works smoothly and efficiently. To solve this problem, researchers have developed a new approach called Hiformer that combines different techniques to improve wind power prediction accuracy and speed. This new method can be up to 52.5% more accurate than existing methods and take up to 68.5% less time to do the calculations. |
Keywords
» Artificial intelligence » Feature extraction
