Wind Power Forecasting Methods Based on Deep Learning: A Survey
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2020/01/01
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Indian UGC (journal)
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Citations22
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Xing Deng
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Haijian Shao
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Chunlong Hu
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Dengbiao Jiang
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Yingtao Jiang
Cite
Deng, Xing, et al. “Wind Power Forecasting Methods Based on Deep Learning: A Survey”. Computer Modeling in Engineering &Amp; Sciences, vol. 122, no. 1, 2020, pp. 273-01, https://doi.org/10.32604/cmes.2020.08768.
Deng, X., Shao, H., Hu, C., Jiang, D., & Jiang, Y. (2020). Wind Power Forecasting Methods Based on Deep Learning: A Survey. Computer Modeling in Engineering &Amp; Sciences, 122(1), 273-301. https://doi.org/10.32604/cmes.2020.08768
Deng, Xing, Haijian Shao, Chunlong Hu, Dengbiao Jiang, and Yingtao Jiang. “Wind Power Forecasting Methods Based on Deep Learning: A Survey”. Computer Modeling in Engineering &Amp; Sciences 122, no. 1 (2020): 273-301. https://doi.org/10.32604/cmes.2020.08768.
Deng X, Shao H, Hu C, Jiang D, Jiang Y. Wind Power Forecasting Methods Based on Deep Learning: A Survey. Computer Modeling in Engineering & Sciences. 2020;122(1):273-301.
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting | Energy |
| 1 | 2024 |
An enhancement of transformer-based architecture with randomized regularization for wind speed prediction | Journal of Intelligent & Fuzzy Systems |
| 2023 | |
| Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities | Computer Modeling in Engineering & Sciences | 2023 | ||
| A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas | Energy Engineering | 1 | 2023 | |
Deep learning model-transformer based wind power forecasting approach | Frontiers in Energy Research |
| 3 | 2023 |
Citations Analysis
The category
Technology: Engineering (General). Civil engineering (General) 12
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine and was published in 2020. The most recent citation comes from a 2024 study titled A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting. This article reached its peak citation in 2022, with 11 citations. It has been cited in 18 different journals, 22% of which are open access. Among related journals, the Computer Modeling in Engineering & Sciences cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.