Summary of Weather-informed Probabilistic Forecasting and Scenario Generation in Power Systems, by Hanyu Zhang et al.
Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems
by Hanyu Zhang, Reza Zandehshahvar, Mathieu Tanneau, Pascal Van Hentenryck
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Applications (stat.AP)
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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 In this paper, researchers develop a method for predicting day-ahead load, wind, and solar power in power grids. The approach combines probabilistic forecasting and Gaussian copula to account for the stochasticity and uncertainty of renewable energy sources (RES). By incorporating weather covariates and preserving spatio-temporal correlations, the proposed method enhances the reliability of probabilistic forecasts. The authors evaluate the effectiveness of different time series models using comprehensive metrics on a real-world dataset from Midcontinent Independent System Operator (MISO). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict how much renewable energy we’ll get each day to keep our power grids reliable. It uses special math called Gaussian copula and weather information to make better predictions. The researchers tested different ways of doing this and found that one method, Temporal Fusion Transformer (TFT), works really well. |
Keywords
» Artificial intelligence » Time series » Transformer
