Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine
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Cited by:
- Seon Hyeog Kim & Gyul Lee & Gu-Young Kwon & Do-In Kim & Yong-June Shin, 2018. "Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting," Energies, MDPI, vol. 11(12), pages 1-17, December.
- Mengyue Hu & Zhijian Hu & Jingpeng Yue & Menglin Zhang & Meiyu Hu, 2017. "A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction," Energies, MDPI, vol. 10(4), pages 1-15, March.
- Lilin Cheng & Haixiang Zang & Tao Ding & Rong Sun & Miaomiao Wang & Zhinong Wei & Guoqiang Sun, 2018. "Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach," Energies, MDPI, vol. 11(8), pages 1-23, July.
- Jinxin Liu & Guan Wang & Tong Zhao & Li Zhang, 2017. "Fault Diagnosis of On-Load Tap-Changer Based on Variational Mode Decomposition and Relevance Vector Machine," Energies, MDPI, vol. 10(7), pages 1-14, July.
- Jianzhong Zhou & Na Sun & Benjun Jia & Tian Peng, 2018. "A Novel Decomposition-Optimization Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 11(7), pages 1-27, July.
- Han, Li & Jing, Huitian & Zhang, Rongchang & Gao, Zhiyu, 2019. "Wind power forecast based on improved Long Short Term Memory network," Energy, Elsevier, vol. 189(C).
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Keywords
wind speed forecasting; variational mode decomposition; partial autocorrelation function; weighted regular extreme learning machine;All these keywords.
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