Short-term prediction of wind power and its ramp events based on semi-supervised generative adversarial network

Bin Zhou*, Haoran Duan, Qiuwei Wu, Huaizhi Wang, Siu Wing Or, Ka Wing Chan, Yunfan Meng

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Short-term predictions of wind power and its ramp events play a critical role in economic operation and risk management of smart grid. This paper proposes a hybrid forecasting model based on semi-supervised generative adversarial network (GAN) to solve the short-term wind power outputs and ramp event forecasting problems. In the proposed model, the original time series of wind energy data can be decomposed into several sub-series characterized by intrinsic mode functions (IMFs) with different frequencies, and the semi-supervised regression with label learning is employed for data augmentation to extract non-linear and dynamic behaviors from each IMF. Then, the GAN generative model is used to obtain unlabeled virtual samples for capturing data distribution characteristics of wind power outputs, while the discriminative model is redesigned with a semi-supervised regression layer to perform the point prediction of wind power. These two GAN models form a min-max game so as to improve the sample generation quality and reduce forecasting errors. Moreover, a self-tuning forecasting strategy with multi-label classifier is proposed to facilitate the forecasting of wind power ramp events. Finally, the real data of a wind farm from Belgium is collected in the case study to demonstrate the superior performance of the proposed approach compared with other forecasting algorithms.

Original languageEnglish
Article number106411
JournalInternational Journal of Electrical Power and Energy Systems
Volume125
Number of pages14
ISSN0142-0615
DOIs
Publication statusPublished - Feb 2021

Keywords

  • Generative adversarial network
  • Renewable energy
  • Semi-supervised regression
  • Wind power forecasting
  • Wind power ramp event

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