Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer
Reliable and accurate wind power forecasting serves as one of the effective measures to enhance grid peak regulation capacity while improving the safety and stability of power systems. However, wind power generation exhibits strong randomness and volatility, which pose significant challenges to achi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11127017/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849223091221168128 |
|---|---|
| author | Jun He Zijian Cheng Zijie Zhong Lizhuo Liang Jianhui Ye |
| author_facet | Jun He Zijian Cheng Zijie Zhong Lizhuo Liang Jianhui Ye |
| author_sort | Jun He |
| collection | DOAJ |
| description | Reliable and accurate wind power forecasting serves as one of the effective measures to enhance grid peak regulation capacity while improving the safety and stability of power systems. However, wind power generation exhibits strong randomness and volatility, which pose significant challenges to achieving precise predictions. This paper proposes a hybrid forecasting model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with ModernTCN-Informer. At first, the original wind power data undergoes ICEEMDAN decomposition to obtain several relatively stable subsequences, thereby mitigating data fluctuations. This process facilitates feature extraction from the sequences and enables the construction of a highly adaptive forecasting model. Secondly, the ModernTCN model is used to sequentially extract correlations among univariate patch sequences across multiple time steps in the dataset, long-term dependencies within univariate patch sequences, and latent correlations across variables. This effectively captures potential interrelationships in wind power data from both temporal and spatial dimensions, followed by accurate and efficient predictions using the Informer model. Finally, validation is conducted on real wind farm data, and the results show that: compared with Informer, the MAE of 12-step prediction is reduced by 3.1%, and compared with LSTM, it is reduced by 29.5%; after incorporating ICEEMDAN, the MAE is further reduced by 64.1%, and the R2 reaches 0.969. The multi-step prediction accuracy is superior to that of the comparison models, verifying the effectiveness of the proposed model. |
| format | Article |
| id | doaj-art-4e24909a5cc04912aaa02e08fb760892 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4e24909a5cc04912aaa02e08fb7608922025-08-25T23:18:42ZengIEEEIEEE Access2169-35362025-01-011314525614527010.1109/ACCESS.2025.359960911127017Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-InformerJun He0Zijian Cheng1https://orcid.org/0009-0008-2384-2557Zijie Zhong2Lizhuo Liang3Jianhui Ye4School of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaReliable and accurate wind power forecasting serves as one of the effective measures to enhance grid peak regulation capacity while improving the safety and stability of power systems. However, wind power generation exhibits strong randomness and volatility, which pose significant challenges to achieving precise predictions. This paper proposes a hybrid forecasting model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with ModernTCN-Informer. At first, the original wind power data undergoes ICEEMDAN decomposition to obtain several relatively stable subsequences, thereby mitigating data fluctuations. This process facilitates feature extraction from the sequences and enables the construction of a highly adaptive forecasting model. Secondly, the ModernTCN model is used to sequentially extract correlations among univariate patch sequences across multiple time steps in the dataset, long-term dependencies within univariate patch sequences, and latent correlations across variables. This effectively captures potential interrelationships in wind power data from both temporal and spatial dimensions, followed by accurate and efficient predictions using the Informer model. Finally, validation is conducted on real wind farm data, and the results show that: compared with Informer, the MAE of 12-step prediction is reduced by 3.1%, and compared with LSTM, it is reduced by 29.5%; after incorporating ICEEMDAN, the MAE is further reduced by 64.1%, and the R2 reaches 0.969. The multi-step prediction accuracy is superior to that of the comparison models, verifying the effectiveness of the proposed model.https://ieeexplore.ieee.org/document/11127017/ICEEMDANModernTCNinformercrossformerwind power forecasting |
| spellingShingle | Jun He Zijian Cheng Zijie Zhong Lizhuo Liang Jianhui Ye Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer IEEE Access ICEEMDAN ModernTCN informer crossformer wind power forecasting |
| title | Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer |
| title_full | Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer |
| title_fullStr | Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer |
| title_full_unstemmed | Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer |
| title_short | Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer |
| title_sort | wind power prediction based on a hybrid model of iceemdan and moderntcn informer |
| topic | ICEEMDAN ModernTCN informer crossformer wind power forecasting |
| url | https://ieeexplore.ieee.org/document/11127017/ |
| work_keys_str_mv | AT junhe windpowerpredictionbasedonahybridmodeloficeemdanandmoderntcninformer AT zijiancheng windpowerpredictionbasedonahybridmodeloficeemdanandmoderntcninformer AT zijiezhong windpowerpredictionbasedonahybridmodeloficeemdanandmoderntcninformer AT lizhuoliang windpowerpredictionbasedonahybridmodeloficeemdanandmoderntcninformer AT jianhuiye windpowerpredictionbasedonahybridmodeloficeemdanandmoderntcninformer |