Forecasting of wind speed by means of window-shifted autoregressive time series

Duran, Necati; Catak, Muammer

doi:10.1109/siu.2016.7496198

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…An ideal data generation model should not only learn feature distributions at each time point but also capture complex relationships between variables at different time points. Using the Autoregressive Model (AR) to capture the autocorrelation of time-series has a good effect on addressing the prediction problem [37][38][39]. However, the AR model is naturally deterministic, requiring meaningful and real data as input.…”

Section: Introductionmentioning

confidence: 99%

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

Shi,

Xu,

Ding

et al. 2023

Sustainability

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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.

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Section: Introductionmentioning

confidence: 99%