Prediction System of Cloud Distribution Image Using Fully Convolutional Networks

DOI IR Web Site 2 References
  • Akiyama Koki
    Graduate School of Sciences and Technology for Innovation, Tokushima University
  • Suzuki Hiroshi
    Graduate School of Sciences and Technology for Innovation, Tokushima University
  • Kitajima Takahiro
    Graduate School of Sciences and Technology for Innovation, Tokushima University
  • Yasuno Takashi
    Graduate School of Sciences and Technology for Innovation, Tokushima University

Abstract

<p>In this paper, we propose a cloud distribution prediction model in which fully convolutional networks are used to improve the prediction accuracy for photovoltaic power generation systems. The model learns the cloud distribution from meteorological satellite images and predicts the cloud image 60 min later. We examined the applicability of Day Microphysics RGB as input to the cloud image prediction model. Day Microphysics RGB is a type of RGB composite image based on the observation image of Himawari-8. It is used for daytime cloud analysis and can perform detailed cloud analysis, for example, the discrimination of cloud areas such as upper and lower clouds. The performance of the proposed method is evaluated on the basis of the root mean square error of the prediction and ground truth images.</p>

Journal

References(2)*help

See more

Details 詳細情報について

Report a problem

Back to top