Summary of Enhancing Wind Speed and Wind Power Forecasting Using Shape-wise Feature Engineering: a Novel Approach For Improved Accuracy and Robustness, by Mulomba Mukendi Christian et al.
Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness
by Mulomba Mukendi Christian, Yun Seon Kim, Hyebong Choi, Jaeyoung Lee, SongHee You
First submitted to arxiv on: 16 Jan 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 paper explores a novel approach to enhance the efficiency of wind energy systems by improving wind speed and power prediction using deep learning methods. The proposed method, called shape-wise feature engineering, alters the data input shape in both CNN-LSTM and Autoregressive models for various forecasting horizons. This technique demonstrates substantial enhancements in model resilience against noise, achieving an impressive 83% accuracy in predicting unseen data up to the 24th step. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study shows that by using this approach, wind energy systems can be improved. The method helps to reduce the noise present in the data and make more accurate predictions. This is important because accurate predictions are necessary for enhancing the efficiency of wind energy systems. |
Keywords
* Artificial intelligence * Autoregressive * Cnn * Deep learning * Feature engineering * Lstm
