Summary of Conformal Prediction For Stochastic Decision-making Of Pv Power in Electricity Markets, by Yvet Renkema et al.
Conformal Prediction for Stochastic Decision-Making of PV Power in Electricity Markets
by Yvet Renkema, Nico Brinkel, Tarek Alskaif
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Machine Learning (stat.ML)
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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 explores the application of conformal prediction (CP), a probabilistic forecasting method, for day-ahead photovoltaic power predictions. By combining machine learning models with CP, the authors aim to enhance participation in electricity markets. The study implements several variants of CP, including CP intervals and cumulative distribution functions, to quantify uncertainty. Various bidding strategies are tested under uncertainty, such as trust-the-forecast, worst-case, Newsvendor, and expected utility maximization (EUM). Results show that combining CP with k-nearest neighbors and/or Mondrian binning outperforms linear quantile regressors. The use of CP with certain bidding strategies can yield high profits with minimal energy imbalance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses a new way to predict the power produced by solar panels a day in advance, which helps electricity companies make better decisions. By combining this prediction method with different strategies for making bids, the authors show that it’s possible to achieve high profits while minimizing the difference between actual and predicted energy production. |
Keywords
» Artificial intelligence » Machine learning
