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Ultra-short-term Wind Power Prediction Based on IEWT-FE-BO-LSTM Model
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Qiu-xian LU1, Gang MA1, Meng-fu TU2
Water Resources and Power | 2023, 41(1) : 217 - 220
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Water Resources and Power | 2023, 41(1): 217-220
• ENERGY •
Ultra-short-term Wind Power Prediction Based on IEWT-FE-BO-LSTM Model
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Qiu-xian LU1, Gang MA1, Meng-fu TU2
Affiliations
  • 1.School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
  • 2.NARI Group Co. Ltd., Nanjing 211106, China
Published: 2023-01-25 doi: 10.20040/j.cnki.1000-7709.2023.20220494
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In order to improve the prediction accuracy of ultra short-term wind power, a combined wind power prediction model based on IEWT-FE-BO-LSTM was proposed. Firstly, an improved empirical wavelet decomposition (IEWT) was used to decompose the historical wind power data. The Fuzzy Entropy (FE) algorithm was introduced to calculate the complexity of each decomposed submodel and reconstruct the submodel. For each recostructed component, a prediction model based on long short term neural network (LSTM) was established. Bayesian optimization algorithm (BO) was used for hyper parameters to solve the problem of poor training results caused by manual parameter adjustment. The example analysis based on historical wind farm data shows that the IEWT-FE-BO-LSTM model has high prediction accuracy and efficiency for ultra short-term wind power.

ultra short-term wind power prediction  /  improved empirical wavelet transform  /  fuzzy entropy  /  Bayesian optimization algorithm
Qiu-xian LU, Gang MA, Meng-fu TU. Ultra-short-term Wind Power Prediction Based on IEWT-FE-BO-LSTM Model[J]. Water Resources and Power, 2023 , 41 (1) : 217 -220 . DOI: 10.20040/j.cnki.1000-7709.2023.20220494
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Year 2023 volume 41 Issue 1
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20220494
  • Receive Date：2022-03-16
  • Online Date：2026-01-28
  • Published：2023-01-25
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History
  • Received：2022-03-16
  • Revised：2022-04-22
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Affiliations
    1.School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
    2.NARI Group Co. Ltd., Nanjing 211106, China
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