Summary of Fusionsf: Fuse Heterogeneous Modalities in a Vector Quantized Framework For Robust Solar Power Forecasting, by Ziqing Ma et al.
FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting
by Ziqing Ma, Wenwei Wang, Tian Zhou, Chao Chen, Bingqing Peng, Liang Sun, Rong Jin
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 proposed multi-modality fusion framework combines historical power data, numerical weather prediction, and satellite images to improve solar power forecasting, particularly for newly installed plants. The vector quantized framework aligns modalities with varying information densities, achieving a balance between incorporating sufficient information and preventing model overfitting. This approach demonstrates strong zero-shot forecasting capabilities and outperforms leading models in both zero-shot and trained scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper proposes a new way to predict how much solar power will be generated by newly installed solar plants. These plants don’t have enough data yet, so the authors combine different types of information (historical power data, weather forecasts, and satellite images) to make more accurate predictions. The new approach is really good at making predictions without needing a lot of training data, which is helpful for these new solar plants. The authors also release a special dataset and a tool called eForecaster that can be used by other researchers. |
Keywords
* Artificial intelligence * Overfitting * Zero shot
