基于时间模式注意力机制的CNN-BiGRU短期负荷预测  

Short-term Power Load Forecasting Based on Temporal Pattern Attention Mechanism of CNN-BiGRU

作　　者：黄宇[1] 顾智勇 李永玲 史博韬 黄怡然[3] HUANG Yu;GU Zhiyong;LI Yongling;SHI Botao;HUANG Yiran(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China;Management Training Center of State Grid Jibei Electric Power Co.,Ltd,Beijing 102401,China;School of Electronic&Communication Engineering,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学控制与计算机工程学院,河北保定071003 [2]国网冀北电力有限公司管理培训中心,北京102401 [3]华北电力大学电子与通信工程系,河北保定071003

出　　处：《华北电力大学学报（自然科学版）》2023年第6期11-20,共10页Journal of North China Electric Power University：Natural Science Edition

基　　金：中央高校基本科研业务费专项资金资助项目(2018MS093).

摘　　要：准确的短期负荷预测可以缓解电力供需矛盾,协调负荷管理,保障电力系统安全。短期电力负荷具有很强的非线性和时间依赖性,并与气候变化和实时电价等诸多外部因素有关,给精准预测短期负荷带来了困难。为此,提出一种基于时间模式注意力(temporal pattern attention, TPA)机制的卷积神经网络(convolutional neural network, CNN)-双向门控循环单元(bidirectional gated recurrent unit, BiGRU)短期负荷预测方法。其中,卷积神经网络用于挖掘不同输入变量与当前负荷间的非线性空间联系,双向门控循环单元用于从时间序列中捕获长期依赖关系,时间模式注意力机制引入以自适应加权赋予特征权重,突出重要信息,实现短期负荷预测。以澳大利亚公开负荷数据集作为实例验证所提方法的效果,预测精度达到了97.651%,与CNN-LSTM、RNN预测模型进行对比,精度分别提高了0.531%和5.992%,实验结果表明:所提的TPA-CNN-BiGRU预测方法具有更高的预测精度和普适性。Accurate short-term load forecasting can alleviate the contradiction between power supply and demand,coordinate load management and ensure power system security.Short-term power load had strong nonlinearity and time dependence,and was related to many external factors such as climate change and real-time electricity price,which made it difficult to accurately predict short-term power load.Therefore,we proposed a short-term power load forecasting method based on TPA(temporal pattern attention)mechanism of CNN(convolutional neural network)-BiGRU(bidirectional gated recurrent unit).Among them,the convolutional neural network was used to mine the nonlinear spatial relation between different input variables and the current load,bidirectional gated recurrent unit was used to capture long-term dependencies from time series,and temporal pattern attention mechanism was introduced to assign feature weights,thus highlighting important information and achieving short-term load prediction.Taking the Australian public load data set as an example to verify the effect of the proposed method,the prediction accuracy reached 97.651%,and comparing with the CNN-LSTM and RNN prediction models,the accuracy was increased by 0.531%and 5.992%respectively.The experimental results show that the proposed TPA-CNN-BiGRU prediction method has higher prediction accuracy and universality.

关 键 词：短期负荷预测 时间模式注意力 卷积神经网络 双向门控循环单元 

分 类 号：TM715[电气工程—电力系统及自动化]