Meteorological-physical mapping new energy multi-time scale output prediction method based on Transform

By constructing a deeply coupled meteorological-physical mapping model using Transformer, the problem of poor adaptability of new energy output forecasting under extreme weather conditions was solved, achieving high-precision multi-timescale forecasting and grid dispatch support.

CN122051930APending Publication Date: 2026-05-15SICHUAN UNIV
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Patent Information

Application Number
CN202610075219.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-20
Publication Date
2026-05-15

AI Technical Summary

Technical Problem

Existing methods for predicting renewable energy output have poor adaptability to extreme weather conditions, unclear mechanisms, and difficulty in meeting the multi-stage dispatching needs of the power grid. In particular, they fail to effectively constrain and utilize spatiotemporal uncertainties in complex environments.

Method used

We employ a Transformer-based data-driven dynamic meteorological forecasting model that deeply couples data with a rigorous photovoltaic physics mapping model. We construct a hybrid dynamic meteorological forecasting model combining CausalTransformer, CVAE-GAN, and GRU, and combine it with the photovoltaic physics model to predict power output at multiple time scales.

Benefits of technology

It achieves high-precision multi-timescale forecasting under extreme weather conditions, improves forecast accuracy and robustness, supports multi-stage grid dispatching needs, and provides reliable technical support for new energy power forecasting in complex environments.

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Abstract

The invention relates to the technical field of new energy output prediction, and discloses a meteorological-physical mapping new energy multi-time scale output prediction method based on Transform. The method comprises the following steps: collecting a multi-source time sequence meteorological data set of a target new energy base and a surrounding area, preprocessing the multi-source time sequence meteorological data set, carrying out feature analysis, and screening meteorological elements which have key influences on meteorological dynamic prediction and photovoltaic output prediction in the multi-source time sequence meteorological data set; building a meteorological dynamic prediction model based on the generative model and training by using a data set; building a physical model for new energy output prediction; and inputting the input data into the meteorological dynamic prediction model to obtain meteorological prediction results under different time scales, and inputting the meteorological prediction results into the physical model to obtain a new energy output prediction result. The core problems that an existing method is poor in adaptability, unclear in mechanism and difficult to meet the multi-stage dispatching requirement of a power grid in extreme weather are solved, and reliable technical support is provided for new energy power prediction in a complex environment.
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