Rule-engine-based intelligent configuration method for power transaction settlement strategy

By combining machine learning and expert knowledge bases, and using a rule engine to optimize power trading settlement strategies, the problems of inefficiency and insufficient adaptability in existing technologies have been solved. This has enabled intelligent configuration and dynamic adjustment of power trading strategies, improving market adaptability and scientific rigor.

WO2026108526A1PCT designated stage Publication Date: 2026-05-28HUBEI CENT CHINA TECH DEV OF ELECTRIC POWER
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
HUBEI CENT CHINA TECH DEV OF ELECTRIC POWER
Filing Date
2025-10-27
Publication Date
2026-05-28

AI Technical Summary

Technical Problem

Existing power trading settlement strategies rely on rules of thumb, which are inefficient, difficult to adapt to rapidly changing and complex trading environments, and lack full utilization of expert knowledge and real-time feedback, resulting in insufficient strategy flexibility and accuracy.

Method used

By employing a rule-based engine approach, combined with machine learning models and expert knowledge bases, and through data preprocessing, LightGBM model training, fuzzy inference system, and weight adjustment, power trading settlement strategies are generated and optimized to achieve dynamic adjustment and self-optimization.

Benefits of technology

It improves the efficiency and accuracy of power trading settlement strategy configuration, enhances the flexibility and adaptability of the strategy, and enables it to learn and optimize itself to adapt to market changes.

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Abstract

Provided in the present invention is a rule-engine-based intelligent configuration method for a power transaction settlement strategy. The method comprises: acquiring transaction data and external factors from a power transaction market, and after pre-processing operations, such as data cleaning, standardization and normalization, are performed, inputting pre-processed data into a machine learning model for training, in order to generate a machine-learning-based strategy; performing weighted summation on the machine-learning-based strategy and an expert strategy, which is formed by service rules and experience in an expert knowledge base, in order to generate a final power transaction settlement strategy; and writing the generated power transaction settlement strategy into a constructed rule engine library, in order to form a dynamically adjustable settlement strategy rule library.
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Citation Information

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