Method, device and electronic equipment for predicting wind power of wind power station

By integrating multiple sub-prediction models and dynamically updating the model weights, the problem of insufficient adaptability of wind power prediction models for wind farms is solved, and accurate and stable predictions over long periods of time are achieved.

CN122246695APending Publication Date: 2026-06-19中电建新能源集团股份有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
中电建新能源集团股份有限公司
Filing Date
2026-05-20
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing wind power prediction models for wind farms are ill-suited to the complex and ever-changing weather conditions, leading to decreased prediction accuracy and stability, and making it impossible to consistently provide accurate wind power predictions.

Method used

A wind power prediction method integrating multiple sub-prediction models is adopted. By dynamically updating model weights and detecting model performance degradation, dynamic update rules and incremental learning mechanisms are constructed to ensure that the model maintains accuracy and stability over a long period of time.

Benefits of technology

It has achieved accurate and stable prediction of wind power at wind farms, reduced prediction errors, and can adapt to various meteorological and weather conditions.

✦ Generated by Eureka AI based on patent content.

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Abstract

This specification provides a method, apparatus, and electronic equipment for predicting wind power at wind farms, applicable to the field of artificial intelligence. Based on this method, before implementation, a wind power prediction model integrating multiple different types of sub-prediction models can be constructed and trained, effectively adapting to various meteorological and weather conditions. During implementation, based on the aforementioned wind power prediction model, by introducing dynamic updates to the model weights of the sub-prediction models and degradation detection of the overall model performance, it can be effectively ensured that the wind power prediction model can operate continuously, accurately, and stably over a longer period, thereby accurately predicting the wind power of the target wind farm and reducing prediction errors.
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