基于Bagging混合策略的多风电场稀疏向量自回归概率预测

Bagging ensemble method of probabilistic forecasting for multiple wind farms by sparse vector autoregression

风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成若干自举时间序列。对于每个时间序列,采用向量自回归(vector autoregression,VAR)模型进行预测。针对传统模型在风场数量较多时容易出现的过拟合问题,采用稀疏向量自回归模型,筛选最有效的回归系数,得到稀疏系数矩阵。每个时间序列训练的预测模型分别产生点预测结果,对于多重点预测结果,使用KDE方法产生概率密度的预测结果。在真实风电集群数据上,验证所提多场站概率预测方法的有效性,采用分位数得分评估概率预测精度。相关实验结果表明,该方法可以有效提高概率预测精度。

Wind power prediction is of great importance for the safe and stable operation of power systems.A sparse vector autoregressive prediction method based on Bagging hybrid strategy and kernel density estimation(KDE)is proposed for the very-short-term probability prediction problem of multiple wind farms.First,a number of bootstrap time series are generated by time series decomposition and residual term bootstrap.For each time series,a vector autoregressive(VAR)model is used for forecasting.To address the overfitting problem that tends to occur in traditional models when the number of wind farms is large,a sparse vector autoregressive model is used to filter the most effective regression coefficients and obtain a sparse coefficient matrix.The prediction model trained by each time series produces point prediction results separately.For multiple prediction results,the KDE method is used to produce the prediction results of probability density.The effectiveness of the proposed method for multiple wind farms is verified on real wind power cluster data,and the quantile score is used to evaluate the probability prediction accuracy.The results show that the method can effectively improve probability prediction accuracy.