RelayGAN: Sequential knowledge propagation for sustainable multi-generation

With the development of artificial intelligence technology, the need for a large amount of high-quality learning data is increasing to be used in various fields. This paper proposes RelayGAN, a new generative model that integrates knowledge inherent in multiple energy data based on the Generative Adversarial Network(GAN) sequentially, similar to relay running. To evaluate the effectiveness of RelayGAN, we conducted extensive experiments using quantitative methods. We employ three statistical metrics, including the Pearson correlation coefficient, the Mann–Whitney U test, and the Kolmogorov–Smirnov test, to validate the quality of the generated data. This shows that RelayGAN improves the performance of conventional multitasking learning-based GAN under the same conditions. Through this, we demonstrate that RelayGAN consistently outperforms state-of-the-art generative models in terms of data quality and pattern preservation. Furthermore, we verify that RelayGAN leverages sequential knowledge transfer to reduce redundant learning processes in accordance with the principles of sustainable AI development, increasing computational efficiency and contributing to eco-friendly AI. Beyond energy data, RelayGAN is a promising approach for multi-source data generation in various edge intelligence applications, ultimately contributing to data-driven innovation.