Open Access
Issue |
E3S Web Conf.
Volume 182, 2020
2020 10th International Conference on Power, Energy and Electrical Engineering (CPEEE 2020)
|
|
---|---|---|
Article Number | 01003 | |
Number of page(s) | 6 | |
Section | Advanced Power Generation Technology and Application | |
DOI | https://doi.org/10.1051/e3sconf/202018201003 | |
Published online | 31 July 2020 |
- State Grid Corporation. Gansu Power Grid 2018 Annual Operation Mode [R]. 2018. [Google Scholar]
- Huang Honglin, Song Lili, Zhou Rongwei, et al. Characteristics Analysis of Wind Power Fluctuations for Large-scale Wind Farms [J]. Proceedings of the CSEE, 2017(06):26-37. [Google Scholar]
- Quan H, Srinivasan D, Khosravi A. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals[J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 26(9):2123-2135. [Google Scholar]
- Cui M, Ke D, Sun Y, et al. Wind Power Ramp Event Forecasting Using a Stochastic Scenario Generation Method[J]. IEEE Transactions on Sustainable Energy, 2015, 6(2):422-433. [Google Scholar]
- Ma, Xi-Yuan, Yuan-Zhang Sun, and Hua-Liang Fang. “Scenario generation of wind power based on statistical uncertainty and variability.” IEEE Transactions on Sustainable Energy 4.4 (2013): 894-904. [Google Scholar]
- Morales J.M, Mínguez R, Conejo A.J. A methodology to generate statistically dependent wind speed scenarios[J]. Applied Energy, 2010, 87(3):843-855. [Google Scholar]
- Sideratos G, Hatziargyriou N.D. Probabilistic Wind Power Forecasting Using Radial Basis Function Neural Networks[J]. IEEE Transactions on Power Systems, 2012, 27(4):1788-1796. [Google Scholar]
- Goodfellow I.J, Pouget-Abadie J, Mirza M, et al. Generative Adversarial Networks[J]. Advances in Neural Information Processing Systems, 2014, 3:2672-2680. [Google Scholar]
- Chen Y, Wang Y, Kirschen D.S, et al. Model-Free Renewable Scenario Generation Using Generative Adversarial Networks[J]. IEEE Transactions on Power Systems, 2018:11-1. [Google Scholar]
- Zhang, Yufan, et al. “Typical wind power scenario generation for multiple wind farms using conditional improved Wasserstein generative adversarial network.” International Journal of Electrical Power & Energy Systems 114 (2020): 105388. [Google Scholar]
- Arjovsky M, Chintala S, Bottou L. Wasserstein GAN[J]. 2017. [Google Scholar]
- Gulrajani I, Ahmed F, Arjovsky M, et al. Improved Training of Wasserstein GANs[J]. 2017. [Google Scholar]
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