Long-Term Solar Power Time-Series Data Generation Method Based on Generative Adversarial Networks and Sunrise–Sunset Time Correction

Abstract

Constructing long-term solar power time-series data is a challenging task for power system planners. This paper proposes a novel approach to generate long-term solar power time-series data through leveraging Time-series Generative Adversarial Networks (TimeGANs) in conjunction with adjustments based on sunrise–sunset times. A TimeGAN model including three key components, an autoencoder network, an adversarial network, and a supervised network, is proposed for data generation. In order to effectively capture autocorrelation and enhance the fidelity of the generated data, a Recurrent Neural Network (RNN) is proposed to construct each component of TimeGAN. The sunrise and sunset time calculated based on astronomical theory is proposed for adjusting the start and end time of solar power time-series, which are generated by the TimeGAN model. This case study, using real datasets of solar power stations at two different geographic locations, indicates that the proposed method is superior to previous methods in terms of four aspects: annual power generation, probability distribution, fluctuation, and periodicity features. A comparison of production cost simulation results between using the solar power data generated via the proposed method and using the actual data affirms the feasibility of the proposed method.

Overview of Data Augmentation Techniques in Time Series Analysis

“…[39], [40], [41], [42], [43] A. Generative Models 1) TimeGAN: TimeGAN, a generative model designed for time series data, leverages a Generative Adversarial Network (GAN) framework to generate synthetic time series data that closely resembles the original data's statistical properties and dependencies [44], [45]. It comprises two main components: the generator and the discriminator.…”