Extreme weather photovoltaic power prediction method, system, equipment and medium

Abstract

The invention discloses an extreme weather photovoltaic power prediction method, system and device, and a medium. The method comprises the steps of obtaining related data of a power plant and performing first processing; constructing a first neural network to extract spatio-temporal features of the cloud picture, and realizing adaptive classification of extreme weather and non-extreme weather through a double-branch discriminator; guiding a conditional diffusion model to generate a non-extreme weather accurate cloud picture by taking the cloud picture spatial-temporal characteristics as constraint conditions; generating an extreme weather accurate cloud picture based on a cloud picture spatio-temporal feature guidance condition generative adversarial network; constructing a second neural network to capture global and local dynamic change characteristics of photovoltaic power and related meteorological data of the power plant; constructing a cross attention module to fuse the weather accurate cloud picture and the dynamic change features; and inputting the fusion features into a third neural network, and dynamically learning mapping from the fusion features to power. According to the invention, through a cloud picture generation framework and a multi-modal fusion mechanism, the problem of failure of a traditional prediction model in extreme weather is effectively solved.

Images