基于多窗宽核密度估计的风电功率超短期自适应概率预测

Ultrashort-term Adaptive Probabilistic Forecasting of Wind Power Based on Multi-band Width Kernel Density Estimation

精准的风电功率预测是保证新型电力系统安稳运行、促进风电消纳的重要手段。针对核密度估计所求分位数在不同置信度下鲁棒性差的问题,提出多窗宽核密度估计方法,根据不同置信度生成不同窗宽的核密度估计值,实现了风电功率的超短期自适应概率预测。首先,结合风电功率曲线和数据驱动模型,建立基于改进双向长短期记忆网络的风电功率超短期确定性预测模型。其次,推导了最优窗宽核密度估计方法,并基于此构建多窗宽核密度估计误差拟合模型,在不同置信度下自适应生成最优窗宽并构建预测区间。最后,基于实际运行数据验证模型的可行性与有效性。结果表明,所提模型可有效提高确定性预测的精度和概率预测的鲁棒性。

Ultrashort-term forecasting of wind power(WP)plays an important role in ensuring the safe and stable opera-tion of power systems with high renewable energy ratio and promote WP consumption.Accurate forecasting results can promote WP consumption.In this paper,an ultrashort-term adaptive probabilistic forecasting of WP based on kernel den-sity estimation(KDE)is proposed.The value of KDE with different band widths(BW)are generated according to different confidence levels,and the problem of poor robustness of the quantile obtained from the KDEs under different confidence levels is addressed.Ultrashort-term deterministic forecasting of WP based on improved bi-directional long short-term memory(BiLSTM)combines WP curves and data-driven in forecasting model.Thereby,the optimal BW KDEs are derived and an error-fitting model is constructed.This model can adaptively generate the optimal BW and con-struct forecasting intervals under different confidence levels.Finally,the proposed model is validated by the actual data,and the results show the superiority and effectiveness of the proposed model.