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LLM-BIDDING

XMO-LLM Bidding (XMO Large Model Bidding) integrates industry-leading large model technology to provide in-depth analysis and strategy optimization support for power trading, specifically addressing and answering questions related to power market transactions. It not only offers professional knowledge and information on power trading to help users understand and master the operation mechanism of the power market, but also conducts detailed processing and analysis of a large amount of public data to assist in decision-making. This model combines market public data with some achievements of XMO predictive agents, and through the capabilities of large language models, assists users in deep data mining and value exploration through AI dialogue to support trading decisions.

Currently, we have integrated some data from Shandong, including prices, weather, and market information, which can be directly accessed through conversation. We will open up the included data sources in the future.

Key Features​

  • Large Model Q&A: XMO's large language model system is designed to provide intelligent decision support for power market bidding, helping market participants to quote and optimize more efficiently.

  • Automated Price Prediction: Real-time price prediction of electricity prices through intelligent agents, providing accurate market trend analysis to help companies effectively respond to price fluctuations.

  • Self-Iterative Bidding Strategy: Intelligent agents automatically generate and optimize bidding strategies, continuously improving the effectiveness and returns of strategies through self-learning and market feedback, ensuring the accuracy and competitiveness of bids.

  • Market Post-Analysis: Agents review and evaluate past market performance, identify improvement points in strategies to optimize future bidding results, ensuring continuous improvement of strategies and market adaptability.

  • Efficiency and Adaptability: This system learns and adjusts bidding strategies dynamically based on continuously changing market conditions through intelligent agents, enhancing bidding strategy optimization, improving quoting effectiveness, and market responsiveness.

Example Questions​

  • Display a line graph showing the system load and electricity price forecast for May. Analyze using the uploaded data.

  • What is the relationship between the direction of the price difference between day-ahead and real-time electricity prices in May and meteorological factors? Analyze using the available data.

  • Please write an electricity price prediction algorithm based on disclosed data (new energy output, system load) and day-ahead clearing price results as training data, and evaluate the accuracy using day-ahead disclosed data for the first week of May.