A photovoltaic power prediction method and device

By preprocessing real-time data from photovoltaic power plants and constructing multi-dimensional features, and combining the prediction step size for power increment calculation and constraint processing, the problems of discontinuous photovoltaic power prediction results and exceeding the physical range are solved, achieving high-accuracy prediction in actual scheduling scenarios.

CN122178832APending Publication Date: 2026-06-09LINKYOYO
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
LINKYOYO
Filing Date
2026-02-02
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing photovoltaic power prediction methods are prone to producing discontinuous or physically feasible predictions when irradiance changes rapidly or when the installed capacity is close to its limit, which limits the accuracy of the predictions in actual dispatch scenarios.

Method used

By preprocessing real-time photovoltaic power data, meteorological observation data, and weather forecast data from photovoltaic power plants, power increment data is generated. A real-time feature set is generated based on multi-dimensional features, and power increment is calculated in combination with the prediction step size. Power constraints and smoothing are then performed to output the photovoltaic power prediction results.

Benefits of technology

Maintaining the continuity and physical rationality of power changes under multiple prediction steps enhances the accuracy of prediction results in actual scheduling scenarios.

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Abstract

This application provides a photovoltaic power prediction method and apparatus. The method includes: preprocessing real-time photovoltaic power data, meteorological observation data, and weather forecast data at the current moment to obtain power increment data corresponding to the current moment; constructing multi-dimensional features based on the power increment data to generate a real-time feature set; determining the predicted power increment corresponding to each prediction step according to the real-time feature set, and superimposing it with the real-time photovoltaic power data at the current moment to generate an initial predicted power result corresponding to each prediction step; and outputting the photovoltaic power prediction result by performing power constraint and smoothing processing on the initial predicted power result. This application superimposes power increments at multiple prediction steps and applies physical constraints and temporal verification to the prediction results, which can maintain the continuity and physical rationality of power changes during multi-timescale prediction, and enhance the accuracy of the prediction results in actual scheduling scenarios.
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