Summary of Forecasting Day-ahead Electricity Prices in the Integrated Single Electricity Market: Addressing Volatility with Comparative Machine Learning Methods, by Ben Harkin et al.
Forecasting Day-Ahead Electricity Prices in the Integrated Single Electricity Market: Addressing Volatility with Comparative Machine Learning Methods
by Ben Harkin, Xueqin Liu
First submitted to arxiv on: 10 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 comprehensive investigation into electricity price forecasting methods in the Irish Integrated Single Electricity Market, focusing on recent periods of high volatility. The study evaluates and compares various machine learning models, including traditional approaches and neural networks, considering different training period lengths. Performance metrics such as mean absolute error, root mean square error, and relative mean absolute error are used to assess model accuracy. A set of input features is investigated from data recorded between October 2018 and September 2022. The study finds that the daily EU Natural Gas price is a more useful feature for electricity price forecasting in Ireland than the daily Henry Hub Natural Gas price. Additionally, the correlation of features to day-ahead market prices has changed recently, with natural gas prices and wind energy levels being significantly correlated with the day-ahead market price. Furthermore, the study shows that input fuel for electricity is now a more important driver of its price than total generation or demand. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well different computer models can predict electricity prices in Ireland. They test lots of different models and see which one does the best job. They also look at what makes those predictions good or bad, like the price of natural gas and how much wind power is being used. They found that some things are more important than others for predicting electricity prices, and that this has changed over time. |
Keywords
* Artificial intelligence * Machine learning
