Open Access
Issue |
E3S Web Conf.
Volume 358, 2022
5th International Conference on Green Energy and Sustainable Development (GESD 2022)
|
|
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Article Number | 01040 | |
Number of page(s) | 8 | |
Section | Invited Contributions | |
DOI | https://doi.org/10.1051/e3sconf/202235801040 | |
Published online | 27 October 2022 |
- Liu Shuai. Research on Short-Term Power Forecasting Methods for Wind Farms [D]. Beijing: North China Electric Power University, 2019. [Google Scholar]
- Liang Zhifeng, Wang Yan, Feng Shuanglei, et al. Ultra-short term forecasting method of wind power based on fluctuation law mining [J]. Power System Technology, 2020, 25 (10): 1–10. [Google Scholar]
- Peng Xiaosheng, Xiong Lei, Wen Jinyu, et al. Summary of improved short-term and ultra-shortterm power prediction accuracy methods for wind power clusters [J]. Chinese Journal of Electrical Engineering, 2016, 36 (23): 6315–6326 + 6596. [Google Scholar]
- Lu Peng, Ye Lin, Tang Yong, et al. Multi-time scale active power optimization scheduling strategy for wind power cluster based on model predictive control [J]. Proceedings of the CSEE, 2019, 39 (22): 6572–6583. [Google Scholar]
- Wang Yining, Xie Da, Wang Xitian, et al. Prediction of wind turbine network interaction based on PCA-LSTM model [J]. Chinese Journal of Electrical Engineering, 2019, 39(14): 4070–4081. [Google Scholar]
- Song Jiakang, Peng Yonggang, Cai Hongda, et al. Study on short-term wind power prediction considering multi-site NWP and atypical characteristics[J]. Power System Technology, 2018, 42 (10): 3234–3242. [Google Scholar]
- J. Yan, H. Zhang, Y. Liu, et al. Forecasting the High Penetration of Wind Power on Multiple Scales Using Multi-to-Multi Mapping [J]. Transactions on Power Systems, 2018, 3276–3284. [CrossRef] [Google Scholar]
- Wang Youjia, Lu Zongxiang, Qiao Ying, et al. Statistical upscaling prediction of regional wind power based on feature clustering [J]. Power System Technology, 2017, 41 (05): 1383–1389. [Google Scholar]
- Wu Xiaomei, Lin Xiang, Xie Xuquan, et al. Shortterm wind power prediction based on VMD-PE and optimized correlation vector machine [J]. Journal of Solar Energy, 2018, 39 (11): 3277–3285. [Google Scholar]
- Zhu Qiaomu, Li Hongyi, Wang Ziqi, et al. Ultrashort-term prediction of wind farm power generation based on long-short-term memory network [J]. Power System Technology, 2017, 41 (12): 3797–3802. [Google Scholar]
- Mu Gang, Yang Xiuyu, Yan Qian, et al. Prediction method of field group continuous power characteristics based on the evolution trend of wind farm group convergence [J]. Chinese Journal of Electrical Engineering, 2018, 38 (S1): 32–38. [Google Scholar]
- S. Tian, Y. Fu, P. Ling, et al. Wind Power Forecasting Based on ARIMA-LGARCH Model[J]. 2018 International Conference on Power System Technology, Guangzhou, 2018, pp. 1285–1289. [CrossRef] [Google Scholar]
- E. Yatiyana, S. Rajakaruna, A. Ghosh. Wind speed and direction forecasting for wind power generation using ARIMA model[J]. 2017 Australasian Universities Power Engineering Conference, 2017, pp. 1–6. [Google Scholar]
- Yin Hao, Ou Zuhong, Chen De, et al. Ultra-shortterm wind power prediction based on quadratic mode decomposition and cascading deep learning[J]. Power System Technology, 2020 (02): 445–453. [Google Scholar]
- Hao Chen, Fangxing Li, Yurong Wang. Wind power forecasting based on outlier smooth transition autoregressive GARCH model[J]. Journal of Modern Power Systems and Clean Energy, 2018, 6(3). [Google Scholar]
- Yu Hua, Lu Jiping, Zeng Yanting, et al. Nonlinear combined prediction of wind power based on different optimization criteria and generalized regression neural network [J]. High Voltage Technology, 2019, 45 (03): 1002–1008. [Google Scholar]
- Y. Zhang, H. Sun, Y. Guo, Wind Power Prediction Based on PSO-SVR and Grey Combination Model[J], IEEE Access, 2019, pp. 136254–136267. [CrossRef] [Google Scholar]
- Li Yanqing, Yuan Yanwu, Guo Tong. Ultra-shortterm wind power combination prediction based on AMD-ICSA-SVM [J]. Power System Protection and Control, 2017, 45 (14): 113–120. [Google Scholar]
- H. J. Lu and G. W. Chang. Wind Power Forecast by Using Improved Radial Basis Function Neural Network, 2018 IEEE Power & Energy Society General Meeting, Portland, O.R., 2018, pp. 1–5. [Google Scholar]
- Ding Ming, Zhang Chao, Wang Bo. Short-term prediction and error correction of wind power based on the power rate fluctuation process [J]. Automation of Electric Power Systems, 2019, 43 (03): 2–12. [Google Scholar]
- J. Varanasi, M. M. Tripathi. Artificial neural network based wind power forecasting in belgium, 2016 IEEE 7th Power India International Conference, 2016, pp. 1–6. [Google Scholar]
- Jinhua Zhang, Jie Yan, David Infield, et al. Shortterm forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model[J]. Applied Energy, 2019, pp. 229–244. [CrossRef] [Google Scholar]
- X. Peng, D. Deng, J. Wen, et al. A very short term wind power forecasting approach based on numerical weather prediction and error correction method[C]. 2016 China International Conference on Electricity Distribution, 2016, pp. 1–4. [Google Scholar]
- Xiong Yindi, Liu Kaipei, Qin Liang, et al. Short-term wind power prediction method based on dynamic weather classification of time series data [J]. Power System Technology, 2019, 43 (09): 3353–3359. [Google Scholar]
- B. Khorramdel, C. Y. Chung, N. Safari, G. C. D. Price, A Fuzzy Adaptive Probabilistic Wind Power Prediction Framework Using Diffusion Kernel Density Estimators, IEEE Transactions on Power Systems, 2018, pp. 7109–7121. [CrossRef] [Google Scholar]
- T. Ouyang, H. Huang, Y. He, Ramp events forecasting based on long-term wind power prediction and correction, IET Renewable Power Generation, 2019, pp. 2793–2801. [CrossRef] [Google Scholar]
- Peng Chenyu, Chen Ning, Gao. Ultra-short-term wind power prediction method combining multiple clustering and hierarchical clustering [J]. Automation of Electric Power Systems: 1–8 [2019-11-10]. [Google Scholar]
- Shuang Han, Yan-hui Qiao, Jie Yan, et al. Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long shortterm memory network[J]. Applied Energy, 2019, pp, 181–191. [Google Scholar]
- Ying-Yi Hong, Christian Lian Paulo P. Rioflorido. A hybrid deep learning-based neural network for 24-h ahead wind power forecasting[J]. Applied Energy, 2019, pp. 530–539. [CrossRef] [Google Scholar]
- N. Safari, C. Y. Chung, G.C.D. Price, Novel MultiStep Short-Term Wind Power Prediction Framework Based on Chaotic Time Series Analysis and Singular Spectrum Analysis, IEEE Transactions on Power Systems, 2018, pp. 590–601. [CrossRef] [Google Scholar]
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