Summary of Transformer Meets Wcdtw to Improve Real-time Battery Bids: a New Approach to Scenario Selection, by Sujal Bhavsar et al.
Transformer meets wcDTW to improve real-time battery bids: A new approach to scenario selection
by Sujal Bhavsar, Vera Zaychik Moffitt, Justin Appleby
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 A pioneering methodology is introduced that combines Transformer-based forecasting with weighted constrained Dynamic Time Warping (wcDTW) to refine scenario selection for stochastic battery bidding in real-time energy markets. The approach leverages Transformers’ predictive capabilities to forecast energy prices, while wcDTW ensures the selection of pertinent historical scenarios by maintaining coherence between multiple uncertain products. This method exhibits a 10% increase in revenue compared to conventional methods in the PJM market, highlighting its potential to revolutionize battery bidding strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way to predict energy prices and choose the best scenarios for selling stored energy back to the grid. They use special computer models called Transformers that are good at predicting things that will happen in the future. These predictions help them select the right scenarios from historical data that match what’s happening now. This method is tested in a real-world scenario and shows that it can make more money than other methods, which could change how energy is sold on the grid. |
Keywords
» Artificial intelligence » Transformer
