An Attention-Based Cycle-Consistent Generative Adversarial Network for IoT Data Generation and Its Application in Smart Energy Systems

Abstract

The availability of Internet of Things (IoT) data is essential for the operation of intelligent systems, such as smart energy systems. Unfortunately, information sensitivity and the lack of observations tend to impact the availability of IoT data. To solve this problem, this article proposes an attention-based cycle-consistent generative adversarial network (ABC-GAN) to generate IoT data. By efficiently learning the distribution among different data patterns and sufficiently capturing temporal features, ABC-GAN can effectively reproduce the IoT data collected from different devices and regions. Various experimental results in smart energy systems demonstrate that ABC-GAN excels at capturing the temporal features, distribution, and latent manifolds of the original data when compared to the baselines and that the prediction models trained with the data generated by ABC-GAN can achieve performances similar to models trained with the real data.