Multi-source satellite offshore wind resource map analysis method, device and equipment

By using a multi-source satellite offshore wind resource map analysis method, multi-level wind farm quality control and machine learning models, the problem of difficulty in collecting wind resource assessment data before wind farm construction was solved, the integrity and accuracy of the data were achieved, the time cost was reduced, and the utilization rate of wind energy resources was improved.

CN120277334BActive Publication Date: 2025-09-23BEIJING AEROSPACE HONGTU INFORMATION TECH +1
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Patent Information

Application Number
CN202510748480.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-06-06
Publication Date
2025-09-23
Estimated Expiration
2045-06-06

AI Technical Summary

Technical Problem

Existing technologies require a large amount of long-term observation data when conducting regional wind resource assessments before wind farm construction. This is difficult and time-consuming to operate, resulting in difficulties in data collection and affecting wind resource utilization.

Method used

Through the multi-source satellite offshore wind resource map analysis method, using multi-level wind field quality control, wind speed correction and machine learning models, combined with satellite data, reanalysis data and sea surface characteristic data, a wind resource map is generated to ensure the integrity and accuracy of the data and reduce the difficulty of data collection.

Benefits of technology

It improves the data accuracy and consistency of wind resource assessment, reduces the time cost of data collection, reduces the risk of wind resource project construction, and improves the benefits of wind energy resource construction.

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Abstract

The present invention provides a multi-source satellite offshore regional wind resource map analysis method, device, and equipment, comprising: preprocessing multi-source original sea surface wind fields to determine a multi-source target sea surface wind field corresponding to a first grid; constructing an average sea surface wind field set based on wind station data, the multi-source target sea surface wind field corresponding to the first grid, and reanalysis data; training a machine learning model using the average sea surface wind field set and sea surface feature data, so that the trained machine learning model can predict the satellite average sea surface wind field of a second grid based on the reanalysis data corresponding to the second grid; and generating a wind resource map corresponding to the study sea area based on the satellite average sea surface wind fields corresponding to the first and second grids, respectively. The present invention can effectively ensure the integrity of regional data, consistency with actual observations, and data accuracy, and can also reduce the difficulty of collecting data for wind resource assessment and prediction.
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