Distributed photovoltaic power prediction method, device, equipment and medium

Through gridded geospatial modeling and spatiotemporal Transformer model, the problem of unmodeled collaborative relationships in multi-site photovoltaic power forecasting is solved, and high-precision and stable distributed photovoltaic power forecasting is achieved.

CN120675068APending Publication Date: 2025-09-19STATE GRID BEIJING ELECTRIC POWER CO +1
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Patent Information

Application Number
CN202510882406.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-27
Publication Date
2025-09-19

AI Technical Summary

Technical Problem

Existing distributed photovoltaic power prediction technology is unable to perform multi-site collaborative predictions. It lacks high-resolution spatiotemporal data in a unified format and cannot fully model the collaborative relationship between distributed sites, affecting prediction accuracy and stability.

Method used

Gridded geographic spatial modeling is used to map the geographic location information of photovoltaic power stations to spatial grid cells, integrate multi-source information within the grid, and use the spatiotemporal Transformer model to extract temporal dependency and spatial correlation information to construct a unified spatiotemporal feature representation.

Benefits of technology

By introducing the spatiotemporal Transformer model, the accuracy and stability of multi-site joint predictions are significantly improved, the model's interpretability and perception of regional spatial structure are enhanced, and its generalization and adaptability to new environments are improved.

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Abstract

The invention belongs to the technical field of photovoltaic power prediction, and particularly relates to a distributed photovoltaic power prediction method, which comprises the following steps of: acquiring multi-source information of a plurality of photovoltaic power stations; constructing a gridding geographic space model based on the collected geographic position information; mapping the geographic position information of each photovoltaic power station to a corresponding space grid unit; for each space grid unit, fusing multi-source information of all photovoltaic stations in the grid, and constructing grid-level multi-source features including meteorological features, power features and geographic position features; and the grid-level multi-source features are inputted into a space-time Transform model which is already trained, and the space-time Transform model outputs a photovoltaic power prediction value. According to the method, the photovoltaic power prediction value of the target at the future moment is obtained through joint modeling of the time sequence dependence and the spatial relationship, so that the prediction precision and stability of the future power change trend of the distributed photovoltaic power station are improved.
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