A method, system, device and storage medium for reconstructing photovoltaic power data
By combining the adaptive kernel function support vector regression model (AK-SVR) with the real-time physical conditions of the photovoltaic power plant, the problem of data loss or distortion caused by power curtailment and shutdown during photovoltaic power generation is solved, generating a high-precision, physically accurate photovoltaic power data sequence, which is suitable for automated data reconstruction of photovoltaic power plants.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-20
- Publication Date
- 2026-07-21
AI Technical Summary
Existing technologies fail to effectively integrate the nonlinear physical principles in the photovoltaic power generation process and cannot build adaptive models for data reconstruction, resulting in missing or distorted photovoltaic measured power data under conditions such as power curtailment and shutdown. Traditional methods cannot guarantee physical authenticity and accuracy.
A support vector regression model based on adaptive kernel function (AK-SVR) is adopted. By acquiring historical data of photovoltaic power plants, data preprocessing is performed using theoretical clear-sky irradiance curves and real-time physical conditions. The model is trained in different regions and combined with tilted surface irradiance and wind speed and temperature corrections to generate high-precision reconstructed power data.
It achieves continuous and complete power data reconstruction, and the reconstruction results are highly consistent with the actual meteorological conditions, improving the integrity and usability of the data. It is highly adaptable and accurate, and is suitable for automated data reconstruction of various photovoltaic power plants.
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Figure CN122432504A_ABST
Abstract
Citation Information
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