Ultra-short-term wind speed prediction based on empirical wavelet transform and combined model
Author:
Funder
the Natural Science Foundation of Liaoning Province of China
the Science Research Project of Liaoning Education Department
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01185-3.pdf
Reference37 articles.
1. Cui Y, Yan S, Zhang H et al (2019) Ultra-short-term prediction of wind power based on chaos theory and ABC optimized RBF neural network[C]. In. IEEE 3rd International Electrical and Energy Conference (CIEEC) IEEE 2019:1422–1427
2. Ding J, Chen G, Huang Y et al (2021) Short-term wind speed prediction based on CEEMDAN-SE-improved PIO-GRNN model[J]. Measurement and Control 54(1–2):73–87
3. Gao L, Zhao L, Kong F et al (2022) Research method of ultra-short-term wind power prediction based on PSO-GRU prediction[C]. In: 2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC). IEEE 2022:967–972
4. Gilles J (2013) Empirical wavelet transform[J]. IEEE Trans Signal Process 61(16):3999–4010
5. Hao SQ, Kuan ATH, Rudd CD (2020) A circular economy approach to green energy: Wind turbine, waste, and material recovery. Sci Total Environ 702:135054
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