基于奇异谱分析去噪和加权系数动态修正的风电功率实时组合预测模型

Real Time Combination Forecasting Model of Wind Power Based on Singular Spectrum Denoising Analysis and Dynamic Correction of Weighting Coefficients

摘要: 准确地把握风电功率的变化规律是风电功率预测的本质，然而目前大多数预测方法忽略了噪声对风电功率变化规律的影响。基于此，本文提出一种基于奇异谱分析去噪和加权系数动态修正的风电功率实时组合预测模型。在该模型中，首先利用奇异谱分析对风电功率时间序列进行分解，得到有限个子序列；然后将风电功率时间序列的功率谱密度波峰的个数作为重构子序列个数的分配依据，得到消噪序列，剩余分量作为噪声滤除；最后利用自回归滑动平均、持续法和最小二乘支持向量机的加权系数动态修正组合模型对消噪序列进行预测。算例分析表明，该方法能够有效提高预测准确性，并显示出良好的普适性。

Abstract: It is the essence of wind power forecasting to accurately grasp the variation law of wind power. However, most of the forecasting methods ignore the influence of noise on the wind power change law. Based on this, this paper proposes a real-time combination forecasting model of wind power based on singular spectrum denoising analysis and dynamic correction of weighting coefficients. In this model, the wind power time subsequence is decomposed by singular spectrum analysis, and the finite subsequence is obtained. Then, the number of the power spectral density wave peak of the wind power time subsequence is used as the distribution basis of the number of the recon-structed subsequence, and the noise elimination sequence is obtained and the residual amount is filtered as the noise. Finally, the self return is used. The weighted average dynamic correction combination model of sliding average, continuation method and least squares support vector ma-chine is used to predict the noise elimination sequence. The example analysis shows that this method can effectively improve the prediction accuracy and show good universality.