Ultra-Short-Term Wind Power Prediction Based on eEEMD-LSTM
The intermittent and random nature of wind brings great challenges to the accurate prediction of wind power; a single model is insufficient to meet the requirements of ultra-short-term wind power prediction. Although ensemble empirical mode decomposition (EEMD) can be used to extract the time series...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/1/251 |
| _version_ | 1827384768995524608 |
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| author | Jingtao Huang Weina Zhang Jin Qin Shuzhong Song |
| author_facet | Jingtao Huang Weina Zhang Jin Qin Shuzhong Song |
| author_sort | Jingtao Huang |
| collection | DOAJ |
| description | The intermittent and random nature of wind brings great challenges to the accurate prediction of wind power; a single model is insufficient to meet the requirements of ultra-short-term wind power prediction. Although ensemble empirical mode decomposition (EEMD) can be used to extract the time series features of the original wind power data, the number of its modes will increase with the complexity of the original data. Too many modes are unnecessary, making the prediction model constructed based on the sub-models too complex. An entropy ensemble empirical mode decomposition (eEEMD) method based on information entropy is proposed in this work. Fewer components with significant feature differences are obtained using information entropy to reconstruct sub-sequences. The long short-term memory (LSTM) model is suitable for prediction after the decomposition of time series. All the modes are trained with the same deep learning framework LSTM. In view of the different features of each mode, models should be trained differentially for each mode; a rule is designed to determine the training error of each mode according to its average value. In this way, the model prediction accuracy and efficiency can make better tradeoffs. The predictions of different modes are reconstructed to obtain the final prediction results. The test results from a wind power unit show that the proposed eEEMD-LSTM has higher prediction accuracy compared with single LSTM and EEMD-LSTM, and the results based on Bayesian ridge regression (BR) and support vector regression (SVR) are the same; eEEMD-LSTM exhibits better performance. |
| first_indexed | 2024-03-08T15:07:23Z |
| format | Article |
| id | doaj.art-570a5d2349894ce8a785f310c1348a07 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T15:07:23Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-570a5d2349894ce8a785f310c1348a072024-01-10T14:56:25ZengMDPI AGEnergies1996-10732024-01-0117125110.3390/en17010251Ultra-Short-Term Wind Power Prediction Based on eEEMD-LSTMJingtao Huang0Weina Zhang1Jin Qin2Shuzhong Song3Information Engineering College, Henan University of Science and Technology, Luoyang 471023, ChinaInformation Engineering College, Henan University of Science and Technology, Luoyang 471023, ChinaInformation Engineering College, Henan University of Science and Technology, Luoyang 471023, ChinaInformation Engineering College, Henan University of Science and Technology, Luoyang 471023, ChinaThe intermittent and random nature of wind brings great challenges to the accurate prediction of wind power; a single model is insufficient to meet the requirements of ultra-short-term wind power prediction. Although ensemble empirical mode decomposition (EEMD) can be used to extract the time series features of the original wind power data, the number of its modes will increase with the complexity of the original data. Too many modes are unnecessary, making the prediction model constructed based on the sub-models too complex. An entropy ensemble empirical mode decomposition (eEEMD) method based on information entropy is proposed in this work. Fewer components with significant feature differences are obtained using information entropy to reconstruct sub-sequences. The long short-term memory (LSTM) model is suitable for prediction after the decomposition of time series. All the modes are trained with the same deep learning framework LSTM. In view of the different features of each mode, models should be trained differentially for each mode; a rule is designed to determine the training error of each mode according to its average value. In this way, the model prediction accuracy and efficiency can make better tradeoffs. The predictions of different modes are reconstructed to obtain the final prediction results. The test results from a wind power unit show that the proposed eEEMD-LSTM has higher prediction accuracy compared with single LSTM and EEMD-LSTM, and the results based on Bayesian ridge regression (BR) and support vector regression (SVR) are the same; eEEMD-LSTM exhibits better performance.https://www.mdpi.com/1996-1073/17/1/251wind power predictionentropy ensemble empirical mode decomposition (eEEMD)differentiated traininglong short-term memory (LSTM) |
| spellingShingle | Jingtao Huang Weina Zhang Jin Qin Shuzhong Song Ultra-Short-Term Wind Power Prediction Based on eEEMD-LSTM Energies wind power prediction entropy ensemble empirical mode decomposition (eEEMD) differentiated training long short-term memory (LSTM) |
| title | Ultra-Short-Term Wind Power Prediction Based on eEEMD-LSTM |
| title_full | Ultra-Short-Term Wind Power Prediction Based on eEEMD-LSTM |
| title_fullStr | Ultra-Short-Term Wind Power Prediction Based on eEEMD-LSTM |
| title_full_unstemmed | Ultra-Short-Term Wind Power Prediction Based on eEEMD-LSTM |
| title_short | Ultra-Short-Term Wind Power Prediction Based on eEEMD-LSTM |
| title_sort | ultra short term wind power prediction based on eeemd lstm |
| topic | wind power prediction entropy ensemble empirical mode decomposition (eEEMD) differentiated training long short-term memory (LSTM) |
| url | https://www.mdpi.com/1996-1073/17/1/251 |
| work_keys_str_mv | AT jingtaohuang ultrashorttermwindpowerpredictionbasedoneeemdlstm AT weinazhang ultrashorttermwindpowerpredictionbasedoneeemdlstm AT jinqin ultrashorttermwindpowerpredictionbasedoneeemdlstm AT shuzhongsong ultrashorttermwindpowerpredictionbasedoneeemdlstm |