Summary of Electricity Price Forecasting in the Irish Balancing Market, by Ciaran O’connor and Joseph Collins and Steven Prestwich and Andrea Visentin
Electricity Price Forecasting in the Irish Balancing Market
by Ciaran O’Connor, Joseph Collins, Steven Prestwich, Andrea Visentin
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Pricing of Securities (q-fin.PR)
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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 applies various price prediction techniques to the Irish short-term electricity market’s balancing market, a highly volatile and understudied area. Techniques from the day-ahead market, such as statistical, machine learning, and deep learning models, are compared using a framework that investigates training size impact. The best-performing model is LEAR, a statistical approach based on LASSO, which outperforms more complex approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Irish balancing market needs accurate price forecasts due to unpredictable renewable energy sources. The paper compares different techniques from the day-ahead market to predict prices in the balancing market. It uses a framework to test how well each model works with varying training sizes. The best approach is LEAR, which is a simple statistical method that does better than more complicated models. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
