基于VMD-LSTMQR的滚动母线负荷区间预测  

Rolling bus load interval prediction based on VMD-LSTMQR

作　　者：董新伟 卜智龙 陈鸣慧 鹿文蓬 年珩[2] DONG Xinwei;BU Zhilong;CHEN Minghui;LU Wenpeng;NIAN Heng(School of Electrical and Power Engineering,China University of Mining and Technology,Xuzhou 221116,China;College of Electrical Engineering,Zhejiang University,Hangzhou 310058,China)

机构地区：[1]中国矿业大学电气与动力工程学院,江苏徐州221116 [2]浙江大学电气工程学院,浙江杭州310058

出　　处：《电力工程技术》2021年第6期9-17,共9页Electric Power Engineering Technology

基　　金：国家自然科学基金资助项目(51977194)。

摘　　要：负荷区间预测能够对负荷出力变化进行概率化分析,准确量化不确定性因素对负荷的影响。相较于传统的点预测,区间预测更能直观反映负荷变化趋势,有助于保障电力系统的安全稳定运行。基于此,文中提出一种基于变分模态分解-长短期记忆神经网络分位数回归(VMD-LSTMQR)的滚动母线负荷区间预测方法。首先,文中采用VMD将原始母线负荷分解成一系列不同频率特征的子序列;接着,确定不同子序列的最优滚动步长并采用LSTMQR分别对不同子序列进行区间预测;最后,将不同子序列的区间预测进行重构,得到原始母线负荷预测结果。文中利用220 kV和10 kV母线负荷数据验证了所采用的区间预测模型相较于传统区间预测模型在预测精度、区间宽度方面得到明显改善。Load interval prediction conducting probabilistic analysis for load power quantifies the impact of uncertain factors accurately.Compared with traditional point prediction,interval prediction is beneficial to the safety and stability of the power system,and it reflects the trend of load changes more intuitively.It is proposed a rolling bus load interval prediction method based on variational mode decomposition(VMD)and long short-term memory neural network quantile regression(LSTMQR)in this paper.First of all,the bus load is decomposed into a series of subsequences with different frequency characteristics by VMD.After that,the optimal rolling steps of different subsequences are determined and LSTMQR is used to predict power intervals of different subsequences.Finally,the interval predictions of different subsequences are reconstructed to obtain the original load prediction results.It is verified by 220 kV and 10 kV bus load data to obtain that the proposed method above has a significant improvement in prediction accuracy and interval width by comparing with traditional interval prediction models.

关 键 词：母线负荷 区间预测 变分模态分解(VMD) 长短期记忆神经网络分位数回归(LSTMQR) 滚动模式 分解重构 

分 类 号：TM715[电气工程—电力系统及自动化]