| 自适应提升及预测误差修正的风电功率超短期预测 |
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| 引用本文： | 高阳,谢丽蓉,叶家豪,乔倜傥,代兵.自适应提升及预测误差修正的风电功率超短期预测[J].陕西电力,2022,0(8):14-21. |
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| 作者姓名： | 高阳 谢丽蓉 叶家豪 乔倜傥 代兵 |
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| 作者单位： | （新疆大学电气工程学院，新疆乌鲁木齐 830047） |
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| 摘 要： | 为了提高超短期风电功率预测精度，提出了一种自适应提升及预测误差修正的风电功率超短期预测方法。首先，使用CEEMDAN将原始风电功率序列分解为多个分量，用RCMSE对其重构成新模态以降低风电功率序列复杂性及提高预测效率；其次，用EESHHO优化ELM权值和阈值提高模型的泛化性，同时引入AdaBoost提高预测模型的精确度和稳定性；最后，在学习历史训练误差的基础上提出修正预测值的策略，进一步提高预测精度。算例结果验证了所提方法的有效性。
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| 关 键 词： | 风电功率预测 CEEMDAN 精细复合多尺度熵 AdaBoost 误差修正 |
Ultra-short-term Wind Power Prediction Based on Adaptive Lifting and Prediction Error Correction |
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| GAO Yang,XIE Lirong,YE Jiahao,QIAO Titang,DAI Bing.Ultra-short-term Wind Power Prediction Based on Adaptive Lifting and Prediction Error Correction[J].Shanxi Electric Power,2022,0(8):14-21. |
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| Authors: | GAO Yang XIE Lirong YE Jiahao QIAO Titang DAI Bing |
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| Affiliation: | （School of Electrical Engineering，Xinjiang University，Urumqi 830047，China） |
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| Abstract: | In order to improve the prediction accuracy of ultra-short-term wind power， an ultra short term wind power prediction method based on adaptive lifting and prediction error correction is proposed. Firstly，the original wind power sequence is decomposed into multiple components using combining complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN）， and reconstructed into new modes with refined composite multiscale entropy （RCMSE） to reduce the complexity of wind power sequence. Secondly，EESHHO is used to optimize the ELM weights and thresholds to improve the generalization of the model，and at the same time AdaBoost is introduced to improve the accuracy and stability of prediction model；Finally，the strategy to correct the prediction is proposed based on the historical training error value strategy，further improving the prediction accuracy. The results verify the effectiveness of the proposed method. |
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| Keywords: | wind power prediction CEEMDAN refined composite multiscale entropy AdaBoost error correction |
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