基于藤copula分位数回归的光伏功率日前概率预测

Day-ahead Probabilistic Forecasting of Photovoltaic Power Based on Vine Copula Quantile Regression

现有分位点回归方法在进行多分位点预测时往往需要为每个分位点单独建立模型,不仅训练成本高还会导致"分位点交叉"。对此,提出了一种基于藤copula分位数回归的光伏功率日前概率预测模型。利用藤copula对光伏功率及其条件变量间的相依结构进行解析化表达,基于优化算法对藤copula结构及参数进行优化,在此基础上建立起光伏功率条件分位数回归模型;在条件变量中引入光伏功率点预测量,并借助最小化连续秩概率分数(continuousrank probability score,CRPS)权衡可靠性与锐度,筛选出最佳条件组合。算例仿真结果表明,该方法克服了现有分位数回归方法的缺点,进一步提升了光伏功率概率预测性能。

To achieve multiple quantile prediction, most of the existing quantile regression methods often need to build models for every quantile, which not only increases the training costs but also leads to "quantile crossing". In this paper, a vine copula based quantile regression model for day-ahead probabilistic forecasting of photovoltaic(PV) power is proposed. The vine copula is used to express the dependent structure between the PV power and its conditions analytically,and the vine copula structure and the parameters are optimized with the optimization algorithm. On this basis, the PV power conditional quantile regression model is established. The point prediction value of the PV power is added into the conditions,and the minimum continuous rank probability score(CRPS) is used to select the optimal combination of conditions, which can weigh the reliability and sharpness. Simulation results show that the proposed method overcomes the shortcomings of the existing quantile regression methods and further improves the performance of the PV power probabilistic forecasting.