Summary of Stacking For Probabilistic Short-term Load Forecasting, by Grzegorz Dudek
Stacking for Probabilistic Short-term Load Forecasting
by Grzegorz Dudek
First submitted to arxiv on: 15 Jun 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 This study combines point-based forecasts for probabilistic short-term electricity demand forecasting using meta-learning. It utilizes quantile linear regression, quantile regression forest, and post-processing techniques to generate quantile forecasts. The approach also introduces global and local variants of meta-learning, with the local variant trained on patterns similar to the query pattern. Experimental studies across 35 forecasting scenarios and 16 base forecasting models demonstrate the superiority of quantile regression forest over its competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses a special kind of learning called meta-learning to help predict how much electricity people will use in the short-term. They combine different forecasting methods, like linear regression and forests, to get more accurate results. The study also tries out different ways of using this meta-learning approach, including one that focuses on patterns similar to the ones it’s trying to predict. By testing it across many different scenarios and forecasting models, they show that one method, called quantile regression forest, is better than others at making these predictions. |
Keywords
* Artificial intelligence * Linear regression * Meta learning * Regression
