Summary of Probabilistic Energy Forecasting Through Quantile Regression in Reproducing Kernel Hilbert Spaces, by Luca Pernigo and Rohan Sen and Davide Baroli
Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces
by Luca Pernigo, Rohan Sen, Davide Baroli
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 presents a non-parametric method called kernel quantile regression based on reproducing kernel Hilbert spaces (RKHS) for energy demand forecasting, which is crucial for achieving sustainable and resilient energy development. This approach aims to quantify uncertainty in forecasts, enabling informed decisions. The study benchmarks its reliability and sharpness against state-of-the-art methods in load and price forecasting for the DACH region. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses a new method called kernel quantile regression based on RKHS to forecast energy demand. This helps us understand what will happen with our energy use better, which is important for making smart decisions about how we produce and store energy. The study shows that this approach works well and is more accurate than some other methods. |
Keywords
» Artificial intelligence » Regression
