Summary of Deep Learning-based Electricity Price Forecast For Virtual Bidding in Wholesale Electricity Market, by Xuesong Wang et al.
Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market
by Xuesong Wang, Sharaf K. Magableh, Oraib Dawaghreh, Caisheng Wang, Jiaxuan Gong, Zhongyang Zhao, Michael H. Liao
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Finance (q-fin.CP)
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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 study presents a Transformer-based deep learning model to forecast the price spread between real-time and day-ahead electricity prices in the ERCOT market. The proposed model leverages various time-series features, including load forecasts, solar and wind generation forecasts, and temporal attributes. The model is trained under realistic constraints and validated using a walk-forward approach by updating the model every week. The results show that an accurate electricity price forecasting model is crucial for virtual bidders, reducing uncertainty and maximizing profits. The study identifies a trading strategy of trading only at the peak hour with a precision score of over 50% produces nearly consistent profit over the test period. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to predict changes in electricity prices. It uses special computer models called Transformers to make these predictions. The model looks at different kinds of data, like how much energy people are using and what’s happening with wind and solar power. The researchers tested their model and found that it can be very accurate. They also came up with a strategy for making money by buying and selling electricity at the right times. |
Keywords
» Artificial intelligence » Deep learning » Precision » Time series » Transformer
