| 引用本文: | 史加荣,赵丹梦,王琳华,等.基于RR-VMD-LSTM的短期风电功率预测[J].电力系统保护与控制,2021,49(21):63-70. |
| SHI Jiarong,ZHAO Danmeng,WANG Linhua,et al.Short-term wind power prediction based on RR-VMD-LSTM[J].Power System Protection and Control,2021,49(21):63-70 |
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| 摘要: |
| 准确的风电功率预测有利于电力系统运行、峰值调节、安全分析和节能减耗。提出了一种基于鲁棒回归(Robust Regression, RR)和变分模态分解(Variational Mode Decomposition, VMD)的长短时记忆(Long Short-Term Memory, LSTM)模型的风电功率预测方法。先使用RR处理采集数据的缺失值和异常点。再利用VMD得到风电功率序列以消除噪声并挖掘原始序列的主要特征。最后采用LSTM对每个分解序列的历史时间序列进行学习并完成预测，并通过重构所有序列的预测值获得风电功率的最终结果。使用所提出的方法对华北某一风电场风电功率进行预测，将预测结果与其他模型对比。结果表明，使用RR-VMD-LSTM方法能显著改善预测性能，降低风电功率预测误差。 |
| 关键词: 风电功率 短期预测 鲁棒回归 变分模态分解 长短时记忆 |
| DOI：DOI: 10.19783/j.cnki.pspc.210123 |
| 分类号: |
| 基金项目:“十三五”国家重点研发计划项目资助(2018YFB1502902)；陕西省自然科学基金项目资助(2021JM-378) |
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| Short-term wind power prediction based on RR-VMD-LSTM |
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SHI Jiarong1, ZHAO Danmeng1, WANG Linhua1, JIANG Tianxiang2
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(1. Xi'an University of Architecture and Technology, Xi'an 710055, China;
2. Shaanxi Regional Electric Power Group Co., Ltd., Baoji 721000, China)
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| Abstract: |
| Accurate wind power prediction is beneficial for power system operation, peak regulation, safety analysis and consumption reduction. A wind power prediction method is proposed based on Long Short-Term Memory (LSTM) combining Robust Regression (RR) and Variational Mode Decomposition (VMD). The RR method is first used to process the missing values and abnormal points of the collected data. Then VMD is proposed to decompose the wind power sequence to eliminate noise and inherit the main characteristics of the original sequence. Finally, LSTM is employed to learn the historical time series of each decomposition sequence and complete the prediction, and all prediction results are integrated to obtain the final prediction of wind power. The proposed method is applied in the wind power prediction of one farm in North China, and the prediction results are compared with other models. The results show that the RR-VMD-LSTM method can significantly improve the prediction performance and reduce the wind power prediction error.
This work is supported by the “the 13th Five-Year” National Key Research and Development Program of China (No. 2018YFB1502902) and the Natural Science Foundation of Shaanxi Province (No. 2021JM-378). |
| Key words: wind power short-term prediction robust regression variational mode decomposition long short-term memory |
