Machine learning-based concentrator error calibration method

By assigning impact weights to each decision tree in the random forest regression model, the problem in the prior art that different characteristics cannot fully consider the impact of electrical energy fluctuations is solved, and the accuracy and adaptability of electrical energy error calibration are improved.

CN119691703AActive Publication Date: 2025-03-25SHENZHEN INHEMETER
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Patent Information

Application Number
CN202510205788.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-25
Publication Date
2025-03-25
Estimated Expiration
2045-02-25

AI Technical Summary

Technical Problem

The prior art cannot fully consider the different effects of different characteristics on electrical energy fluctuations, resulting in low accuracy of the random forest regression model.

Method used

By obtaining the concentrator's multiple parameter timings, a random forest regression model is constructed, and each decision tree is assigned an impact weight. By calculating the feature correlation within the fluctuation period, the weights are dynamically adjusted to adapt to different environmental conditions.

Benefits of technology

The model's ability to capture complex nonlinear relationships of electrical energy fluctuations is improved, and the calibration accuracy and adaptability to measurement errors is enhanced, ensuring that high-precision electrical energy prediction results are provided in a variable power system environment.

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Abstract

The invention relates to the field of data processing, in particular to a concentrator error calibration method based on machine learning, which is used for calibrating errors of active electric energy and reactive electric energy by collecting and preprocessing related data of a concentrator and combining a random forest regression model. Firstly, a model is established by using historical data, an electric energy fluctuation time period is identified, and the influence of different characteristics on electric energy is determined by calculating the correlation degree of load power and electric energy. And adjusting the weight of each decision tree according to the trend correlation between each feature and the electric energy, and obtaining a weighted prediction value. And finally, calculating an electric energy error and carrying out dynamic adjustment based on a correction coefficient so as to realize real-time error calibration. According to the method, the electric energy error of the intelligent concentrator is automatically calibrated through the random forest algorithm in combination with feature correlation analysis and an error correction model.
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