Short-Term Wind Speed Predicting Based on Legendre Multiwavelet Transform and GRU-ENN
Wind energy has become a vital component of the power system. Due to the stochastic and intermittent characteristics of wind speed, thus enhancing accuracy and stability in short-term wind speed prediction is imperative and important for effectively harnessing wind energy. This paper proposes a nove...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10614600/ |
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| author | Xiaoyang Zheng Xiaoheng Luo Dezhi Liu |
| author_facet | Xiaoyang Zheng Xiaoheng Luo Dezhi Liu |
| author_sort | Xiaoyang Zheng |
| collection | DOAJ |
| description | Wind energy has become a vital component of the power system. Due to the stochastic and intermittent characteristics of wind speed, thus enhancing accuracy and stability in short-term wind speed prediction is imperative and important for effectively harnessing wind energy. This paper proposes a novel hybrid model combing Legendre multiwavelet transform, Gated recurrent unit and Elman neural network (LMWT-GRU-ENN) for short-term wind speed prediction. More precisely, the rich properties, especial various regularities of LMW bases are utilized to effectively match the non-linearity and larger non-stationary features of short-term wind speed corresponding to multi-resolution level and multi-wavelet bases. GRU model is used to predict the low frequency components, and ENN model is implemented to predict the high frequency components obtained by LMWT, which can effectively improve the prediction performance by thoroughly making use of their advantages. Finally, massive experiments are conducted on two short-term wind speed datasets, and the experimental results demonstrate the proposed method attains the excellent performance of in both accuracy and stability compared with other state-of-the-art methods. |
| format | Article |
| id | doaj-art-5c5cf29cd23745d7854bb713a821a0f6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5c5cf29cd23745d7854bb713a821a0f62025-01-10T00:01:29ZengIEEEIEEE Access2169-35362025-01-01134381439710.1109/ACCESS.2024.343567410614600Short-Term Wind Speed Predicting Based on Legendre Multiwavelet Transform and GRU-ENNXiaoyang Zheng0https://orcid.org/0000-0002-1719-4169Xiaoheng Luo1https://orcid.org/0009-0009-2274-9139Dezhi Liu2School of Artificial Intelligence, Chongqing University of Technology, Chongqing, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing, ChinaWind energy has become a vital component of the power system. Due to the stochastic and intermittent characteristics of wind speed, thus enhancing accuracy and stability in short-term wind speed prediction is imperative and important for effectively harnessing wind energy. This paper proposes a novel hybrid model combing Legendre multiwavelet transform, Gated recurrent unit and Elman neural network (LMWT-GRU-ENN) for short-term wind speed prediction. More precisely, the rich properties, especial various regularities of LMW bases are utilized to effectively match the non-linearity and larger non-stationary features of short-term wind speed corresponding to multi-resolution level and multi-wavelet bases. GRU model is used to predict the low frequency components, and ENN model is implemented to predict the high frequency components obtained by LMWT, which can effectively improve the prediction performance by thoroughly making use of their advantages. Finally, massive experiments are conducted on two short-term wind speed datasets, and the experimental results demonstrate the proposed method attains the excellent performance of in both accuracy and stability compared with other state-of-the-art methods.https://ieeexplore.ieee.org/document/10614600/Legendre multiwavelet transformempirical mode decompositiondiscrete wavelet transformElman neural networkgated recurrent unit |
| spellingShingle | Xiaoyang Zheng Xiaoheng Luo Dezhi Liu Short-Term Wind Speed Predicting Based on Legendre Multiwavelet Transform and GRU-ENN IEEE Access Legendre multiwavelet transform empirical mode decomposition discrete wavelet transform Elman neural network gated recurrent unit |
| title | Short-Term Wind Speed Predicting Based on Legendre Multiwavelet Transform and GRU-ENN |
| title_full | Short-Term Wind Speed Predicting Based on Legendre Multiwavelet Transform and GRU-ENN |
| title_fullStr | Short-Term Wind Speed Predicting Based on Legendre Multiwavelet Transform and GRU-ENN |
| title_full_unstemmed | Short-Term Wind Speed Predicting Based on Legendre Multiwavelet Transform and GRU-ENN |
| title_short | Short-Term Wind Speed Predicting Based on Legendre Multiwavelet Transform and GRU-ENN |
| title_sort | short term wind speed predicting based on legendre multiwavelet transform and gru enn |
| topic | Legendre multiwavelet transform empirical mode decomposition discrete wavelet transform Elman neural network gated recurrent unit |
| url | https://ieeexplore.ieee.org/document/10614600/ |
| work_keys_str_mv | AT xiaoyangzheng shorttermwindspeedpredictingbasedonlegendremultiwavelettransformandgruenn AT xiaohengluo shorttermwindspeedpredictingbasedonlegendremultiwavelettransformandgruenn AT dezhiliu shorttermwindspeedpredictingbasedonlegendremultiwavelettransformandgruenn |