为充分挖掘风速的时空特征,提出一种基于图卷积网络（GCN）、逐次变分模态分解（SVMD）、双向长短时记忆网络（BiLSTM）及柯尔莫哥洛夫-阿诺德网络（KAN）的短期风速预测模型。首先,利用GCN提取不同水平距离风速之间的空间特征;然后,采用SVMD对目标风速序列进行分解,以提取不同时间尺度的模态特征;最后,将提取的空间信息和分解后的多尺度时间序列进行融合,并将融合后的时空多维信息输入到BiLSTM-KAN模型中进行预测。实验结果表明,在超前1步、3步、6步、9步和12步预测中,所提模型的平均绝对误差相比于BiLSTM模型分别降低38.36%、55.45%、58.21%、60.53%和59.76%。
Abstract
To comprehensively capture the spatiotemporal characteristics of wind speed, this paper proposes a short-term wind speed forecasting model based on graph convolutional network （GCN）, successive variational mode decomposition （SVMD）, bidirectional long short-term memory （BiLSTM） network, and the Kolmogorov-Arnold Network （KAN）. First, GCN is employed to extract spatial features among wind speeds at different horizontal distances. Then, SVMD is used to decompose the target wind speed series, thereby extracting modal features across various temporal scales. Finally, the extracted spatial features and the multi-scale temporal components are fused and input into a BiLSTM-KAN model to perform forecasting. Experimental results show that for 1-, 3-, 6-, 9-, and 12-step ahead predictions, the proposed model reduces the mean absolute error by 38.36%, 55.45%, 58.21%, 60.53%, and 59.76%, respectively, compared with the BiLSTM model.
关键词
风力发电 /
风速 /
预测 /
图卷积网络 /
双向长短时记忆网络 /
柯尔莫哥洛夫-阿诺德网络（KAN）
Key words
wind power /
wind speed /
forecasting /
graph convolutional network /
bidirectional long short-term memory network /
Kolmogorov-Arnold Network
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基金
中广核新能源控股有限公司研究项目（003-CAK-F120-2023-238）
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