考虑预测误差时空相依性的短期风电功率概率预测

Probabilistic Prediction of Short-Term Wind Power Considering the Temporal and Spatial Dependence of Prediction Errors

由于风电功率点预测误差无法避免,概率预测可以充分描述风电功率的不确定性,进而对调度部门的决策提供进一步指导。当前的风电功率概率预测方法对其物理变化过程挖掘仍不完整。通过挖掘历史风电功率数据和数值天气预报(numerical weather prediction,NWP)的时空特性,构建了一种考虑误差时空相依性的短期风电概率预测新框架。首先通过门控循环单元(gated recurrent unit,GRU)得到点预测结果;进而,引入多位置NWP,提出了一种考虑时空相依特性的多层次误差场景划分方法;最后,利用Bootstrap抽样法重构形成新的适应建模的误差样本集,进行了不同置信水平下的短期风电功率概率预测。由此得出,在考虑时空相依性的概率预测下,整体框架的效果在中国东北某风电场被验证,对比相同置信水平下,预测精度得到有效明显提高,且评价指标预测区间覆盖率提高0.53%、0.44%、0.32%,预测区间平均带宽缩小2.40%、2.14%,0.06%,证实了所提方法的有效性和可行性。

Since wind power point forecast errors are unavoidable,probabilistic forecasts can fully describe the uncertainty of wind power,and then provide further guidance for the dispatching department’s decision-making.The current wind power probabilistic prediction methods are still incomplete in mining its physical change process.Therefore,this paper constructs a new short-term wind power probabilistic prediction framework that considers the spatiotemporal dependence of errors by mining the spatiotemporal characteristics of historical wind power data and numerical weather prediction(NWP).Firstly,the point prediction results are obtained through the gated recurrent unit(GRU);then,a multi-position NWP is introduced,and a multi-level error scene division method considering the characteristics of space-time dependence is proposed;finally,the Bootstrap sampling method is used to reconstruct the error to form a new adaptive modeling a sample set of short-term wind power probabilistic prediction at different confidence levels is carried out.The experimental results show that the effect of the overall framework has been verified in a wind farm in Northeast China under the probabilistic prediction considering the spatial and temporal dependencies.Compared with the same confidence level,the prediction accuracy is effectively and significantly improved,and the evaluation index PICP is improved by 0.53%and 0.44%,0.32%,PINAW shrinks by 2.40%,2.14%,0.06%,which proves the feasibility and effectiveness of the proposed method.