Summary of On-line Conformalized Neural Networks Ensembles For Probabilistic Forecasting Of Day-ahead Electricity Prices, by Alessandro Brusaferri et al.
On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices
by Alessandro Brusaferri, Andrea Ballarino, Luigi Grossi, Fabrizio Laurini
First submitted to arxiv on: 3 Apr 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 This research proposes a novel approach to probabilistic electricity price forecasting (PEPF) by extending the state-of-the-art neural networks ensembles based methods through conformal inference techniques. The proposed method improves upon existing approaches by achieving better hourly coverage and stable probabilistic scores in day-ahead forecasts. This development has significant implications for operating complex power markets with increasing shares of renewable generation, as it enables more accurate prediction uncertainty quantification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes electricity price forecasting more reliable and accurate by combining neural networks and conformal inference techniques. The goal is to better predict future prices and account for uncertainty in the energy market. Researchers have tried using different types of models before, but this new approach seems to work well and can be used in real-world applications. |
Keywords
* Artificial intelligence * Inference
