电子测量与仪器学报

张登攀,兰征,杜怡衡.基于PCHIP-VMD数据分析与SSA-LSTM模型的短期风电功率预测[J].电子测量与仪器学报,2025,39(5):251-261

基于PCHIP-VMD数据分析与SSA-LSTM模型的短期风电功率预测

Short-term wind power forecasting based on PCHIP-VMD dataanalysis and SSA-LSTM model

DOI：

中文关键词: 功率预测分段三次Hermite插值变分模态分解麻雀搜索算法长短期神经网络

英文关键词:power prediction piecewise cubic Hermite interpolating polynomial variational mode decomposition sparrow search algorithm long and short term neural networks

基金项目:湖南省自然科学基金面上项目（2025JJ50232）、湖南省教育厅科学研究重点项目（24A0404）资助

作者	单位
张登攀	湖南工业大学电气与信息工程学院株洲412007
兰征	湖南工业大学电气与信息工程学院株洲412007
杜怡衡	郑州轻工业大学电气与信息工程学院郑州450002

Author	Institution
Zhang Dengpan	School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China
Lan Zheng	School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China
Du Yiheng	School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002,China

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中文摘要:

短期风电功率预测对电力系统调度与安全运行具有重要的支撑作用,然而风电数据具有强随机性和非平稳性,现有预测方法存在数据预处理保形性不足、模态混叠、预测模型参数优化效率低等问题,严重影响短期风电功率预测的准确性。为此,提出分段三次Hermite插值法(PCHIP)与变分模态分解(VMD)相结合的数据预处理方法,以及麻雀搜索算法(SSA)与长短期记忆网络(LSTM)相结合的风电功率预测模型。首先,对风电原始数据异常值进行检测,针对异常值导致的时序数据保形性缺失问题，采用PCHIP法进行数据修复;其次,结合VMD法将预处理后的风电功率数据分解为4个内在模态分量,得到不同时间尺度上变化的数据信号;然后,将分解后的稳定项序列输入SSA-LSTM风电功率预测模型,得到风电功率预测结果。以某风电场21 d的实测功率数据为样本数据进行分析验证,所提模型的预测结果与真实值拟合程度可达到0.989 1,较当前LSTM模型预测精度提升5.558%,证明了所提方法的有效性和优越性。

英文摘要:

Short-term wind power prediction is crucial for power system scheduling and operational security. However, the accuracy of such predictions is severely compromised by the inherent strong randomness and non-stationarity of wind power data, as well as limitations in existing methods, including insufficient shape-preserving capability in data preprocessing, modal aliasing, and inefficient parameter optimization in prediction models. To address these issues, this paper proposes a novel hybrid framework combining a piecewise cubic hermite interpolating polynomial (PCHIP) with variational mode decomposition (VMD) for data preprocessing and a sparrow search algorithm (SSA)-optimized long short-term memory (LSTM) network for prediction. First, abnormal values in raw wind power data are identified and repaired using PCHIP, which preserves the local monotonicity and curvature of the original sequence through Hermite interpolation. Second, the preprocessed data are decomposed into four intrinsic mode components (IMFs) via VMD to capture multi-scale temporal features. Finally, the stabilized IMF sequences are input into the SSA-LSTM wind power forecasting model to yield prediction outcomes. Experimental validation using 21-day measured power data from a wind farm demonstrates that the proposed model achieves a fitting degree of 0.989 1 with actual values, improving prediction accuracy by 5.558% compared to conventional LSTM, thereby verifying the effectiveness and superiority of the method.

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