A Review of Short-Term Wind Power Forecasting Based on Artificial Intelligence Methods
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-69483-7_5
Reference37 articles.
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2. Global Wind Report (2023). https://gwec.net/globalwindreport2023/. Accessed 27 Mar 2024
3. Wang, Z., Wang, L., Revanesh, M., et al.: Short-term wind speed and power forecasting for smart city power grid with a hybrid machine learning framework. IEEE Internet of Things J. 10(21), 1875418765 (2023)
4. Zhou, Y., Wei, F., Kuang, K., et al.: Research on a deep ensemble learning model for the ultra-short-term probabilistic prediction of wind power. Electronics 13(3), 475 (2024)
5. Pijnenburg, P., Cao, B., Chang, L., et al.: A post‐forecast weighing algorithm to improve wind power forecasting capabilities. IET Renewable Power Generation 17(2), 296304 (2023)
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