基于量子加权多层级GRU神经网络的综合能源系统多元负荷短期预测  

Short-term multivariate load forecasting of an integrated energy system based on a quantum weighted multi-hierarchy gated recurrent unit neural network

作　　者：王凇瑶 张智晟 WANG Songyao;ZHANG Zhisheng(College of Electrical Engineering,Qingdao University,Qingdao 266071,China)

机构地区：[1]青岛大学电气工程学院,山东青岛266071

出　　处：《电力系统保护与控制》2022年第23期85-93,共9页Power System Protection and Control

基　　金：国家自然科学基金项目资助(52077108)。

摘　　要：针对综合能源系统多元负荷短期预测问题,提出一种基于量子加权多层级GRU(quantum weighted multi hierarchy gated recurrent unit,QWMHGRU)神经网络的多元负荷短期预测模型。采用最大信息系数对多元负荷间和负荷与天气因素间的相关性进行分析,选取模型输入量。然后改进GRU的门控结构,形成多层级门控循环单元(multi hierarchy gated recurrent unit,MHGRU),并将量子加权神经元引入MHGRU,构成QWMHGRU多变量负荷预测模型。仿真算例结果表明,QWMHGRU多元负荷预测模型在夏季和冬季的权重平均精度均可达97%以上,相比MHGRU、QWGRU和GRU模型具有更高的预测精度。To solve the problem of short-term multivariate load forecasting for integrated energy systems,a model based on a quantum weighted multi-hierarchy gated recurrent unit(QWMHGRU)neural network is proposed.The maximum information coefficient is adopted to analyze the correlation between multivariate loads and the relevance between loads and weather factors to form the multiple input sequence.Then the gate structure of the GRU is improved to form a multi-hierarchy gate recurrent unit(MHGRU)and the quantum weighted neurons are introduced into the MHGRU to form the QWMHGRU multivariate load forecasting model.The simulation results show that the weighted average accuracy of the multivariate load forecasting model of QWMHGRU model in summer and winter can reach over 97%.This is higher than MHGRU,QWGRU and GRU models.

关 键 词：综合能源系统 最大信息系数 多元负荷短期预测 量子加权多层级GRU 

分 类 号：TM715[电气工程—电力系统及自动化] TK01[动力工程及工程热物理] TP183[自动化与计算机技术—控制理论与控制工程]