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A Comparative Study on Data Augmentation Using Generative Models for Robust Solar Irradiance Prediction

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(11), pp.29-42
  • DOI : 10.9708/jksci.2023.28.11.029
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 18, 2023
  • Accepted : November 13, 2023
  • Published : November 30, 2023

Jinyeong Oh 1 Jimin Lee 1 Daesungjin Kim 2 Bo-Young Kim 2 Jihoon Moon 1

1순천향대학교
2아산중학교

Accredited

ABSTRACT

In this paper, we propose a method to enhance the prediction accuracy of solar irradiance for three major South Korean cities: Seoul, Busan, and Incheon. Our method entails the development of five generative models—vanilla GAN, CTGAN, Copula GAN, WGANGP, and TVAE—to generate independent variables that mimic the patterns of existing training data. To mitigate the bias in model training, we derive values for the dependent variables using random forests and deep neural networks, enriching the training datasets. These datasets are integrated with existing data to form comprehensive solar irradiance prediction models. The experimentation revealed that the augmented datasets led to significantly improved model performance compared to those trained solely on the original data. Specifically, CTGAN showed outstanding results due to its sophisticated mechanism for handling the intricacies of multivariate data relationships, ensuring that the generated data are diverse and closely aligned with the real-world variability of solar irradiance. The proposed method is expected to address the issue of data scarcity by augmenting the training data with high-quality synthetic data, thereby contributing to the operation of solar power systems for sustainable development.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.