Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model
In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. With the goal of...
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MDPI AG
2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/6/1465 |
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| author | Na Fang Zhengguang Liu Shilei Fan |
| author_facet | Na Fang Zhengguang Liu Shilei Fan |
| author_sort | Na Fang |
| collection | DOAJ |
| description | In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. With the goal of extracting feature information that existed in temporal series data, CEEMDAN and VMD decomposition are used to divide the raw wind data into several intrinsic modal function components. Furthermore, to reduce computational burden and enhance convergence speed, these intrinsic mode function (IMF) components are integrated and rebuilt via the results of sample entropy and K-means. Lastly, to ensure the completeness of the prediction outcomes, the final prediction results are synthesized through the superposition of all IMF components. The simulation results indicate that the proposed model is superior to other models in accuracy and robustness. |
| format | Article |
| id | doaj-art-b4b738b9bc4f41459997332e0f2d3875 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-b4b738b9bc4f41459997332e0f2d38752025-08-20T03:43:02ZengMDPI AGEnergies1996-10732025-03-01186146510.3390/en18061465Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid ModelNa Fang0Zhengguang Liu1Shilei Fan2Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaHubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaHubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaIn order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. With the goal of extracting feature information that existed in temporal series data, CEEMDAN and VMD decomposition are used to divide the raw wind data into several intrinsic modal function components. Furthermore, to reduce computational burden and enhance convergence speed, these intrinsic mode function (IMF) components are integrated and rebuilt via the results of sample entropy and K-means. Lastly, to ensure the completeness of the prediction outcomes, the final prediction results are synthesized through the superposition of all IMF components. The simulation results indicate that the proposed model is superior to other models in accuracy and robustness.https://www.mdpi.com/1996-1073/18/6/1465time series data predictionhybrid deep learninggated recurrent unitCEEMDANVMDsecondary decomposition |
| spellingShingle | Na Fang Zhengguang Liu Shilei Fan Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model Energies time series data prediction hybrid deep learning gated recurrent unit CEEMDAN VMD secondary decomposition |
| title | Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model |
| title_full | Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model |
| title_fullStr | Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model |
| title_full_unstemmed | Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model |
| title_short | Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model |
| title_sort | short term wind power prediction method based on ceemdan vmd gru hybrid model |
| topic | time series data prediction hybrid deep learning gated recurrent unit CEEMDAN VMD secondary decomposition |
| url | https://www.mdpi.com/1996-1073/18/6/1465 |
| work_keys_str_mv | AT nafang shorttermwindpowerpredictionmethodbasedonceemdanvmdgruhybridmodel AT zhengguangliu shorttermwindpowerpredictionmethodbasedonceemdanvmdgruhybridmodel AT shileifan shorttermwindpowerpredictionmethodbasedonceemdanvmdgruhybridmodel |