为提高短期风电功率预测的准确性,提出一种基于时间卷积网络残差校正模型的短期风电功率预测方法。首先,采取自适应噪声完备集合经验模态分解算法分离出风电功率的局部特征信息,以网格搜索与交叉验证算法优化的支持向量回归模型对各分量进行预测。然后,构建时间卷积网络残差预测模型,并使用灰色关联度分析方法选择输入特征,对支持向量回归预测结果进行校正。最后,基于提出的模型对某风电场实际运行功率进行预测并与其他方法的预测精度进行比较,结果表明,该文所提方法提高了短期风电功率预测的精度。
Abstract
A short-term wind power prediction method based on temporal convolutional network residual correction model is proposed to improve the accuracy of short-term wind power prediction. Firstly, using the complete ensemble empirical mode decomposition with adaptive noise algorithm to separate the local characteristic information of original wind power data, each component is predicted by the support vector regression model which is optimized by grid search and cross-validation algorithm. Secondly, a temporal convolutional network residual prediction model is constructed, and the gray correlation analysis method is used to select the input features of the residual prediction model to correct the support vector regression prediction results. Finally, based on the proposed model, the actual operating power of a wind farm is predicted and compared with the prediction accuracy of other methods. The results verify that the proposed method improves the accuracy of short-term wind power prediction.
关键词
风电功率预测 /
自适应噪声完备集合经验模态分解 /
时间卷积网络 /
灰色关联度分析 /
残差校正
Key words
wind power prediction /
complete ensemble empirical mode decomposition with adaptive noise /
temporal convolutional network /
grey relational analysis /
residual correction
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基金
国防基础研究计划（JCKY2019407C002）
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