王辉,邹智超,李欣,吴作辉,周珂锐

• 1:三峡大学电气与新能源学院
• 2:智慧能源技术湖北省工程研究中心(三峡大学)

摘要(Abstract)：

为解决风速不确定性和波动性造成风电功率预测精度不高的问题，提出一种基于变分模态分解（VMD）、改进麻雀搜索算法（ISSA）和门控循环神经网络（GRU）的VMD-ISSA-GRU组合模型。首先，利用中心频率法确定采用VMD分解后的模态分量个数，这样有效避免了过分解或者分解不充分。其次引入混沌映射、非线性递减权重以及一个突变策略来改进麻雀搜索算法，用于优化门控循环神经网络，然后对分解得到的各个子序列建立ISSA-GRU预测模型，最后叠加每个子序列的预测值得到最终的预测值。将该模型用于实际风电功率预测，实验结果表明：VMD-ISSA-GRU组合模型的平均绝对误差、平均绝对百分比误差、均方根误差分别为1.211 8 MW、1.890 0及1.591 6 MW；相较于传统的GRU、长短时记忆（LSTM）神经网络、BiLSTM(Bi-directional LSTM)神经网络模型以及其他组合模型在预测精度上都有明显的提升，能很好地解决风电功率预测精度不高的问题

关键词(KeyWords)： 风电功率预测;变分模态分解;改进麻雀搜索算法;门控循环神经网络;超参数

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(52107107)~~

作者(Author): 王辉,邹智超,李欣,吴作辉,周珂锐

DOI: 10.19666/j.rlfd.202312189

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