基于多特征提取的Attention-BiGRU短期负荷预测方法  

Short Term Load Forecasting Method Based on Multi-feature Extracted Attention-BiGRU

作　　者：蔡同尧 曾献辉[1] CAI Tongyao;ZENG Xianhui(School of Information Science and Technology,Donghua University,Shanghai 201620,China)

机构地区：[1]东华大学信息科学与技术学院,上海201620

出　　处：《河北电力技术》2023年第1期1-7,共7页Hebei Electric Power

摘　　要：电力负荷预测在电力系统发展中起着重要的作用,为供电提供了重要的指导。短期电力负荷预测(STLF)可以在短时间内保证电网的安全和稳定。为解决预测精度不足且数据集单一缺乏参考因素的问题,提出一种基于多特征提取并结合注意力机制的双向门控循环单元(Attention-BiGRU)网络短期电力负荷预测方法。预测模型采用门控循环单元(GRU)的基本结构,在已有数据特征的基础上进行时间特征与数据分布特征提取,将所有特征作为负荷预测的影响因素,然后使用注意力机制对输入序列进行权重分配,使用双向门控循环单元(BiGRU)网络对特征进行学习并输出负荷预测值。仿真结果表明,基于多特征提取的Attention-BiGRU网络表现优于传统高斯回归预测方法、GRU网络、多特征提取的BiGRU网络和BiGRU网络。Power load forecasting plays an important role in the development of power system and provides important guidance for power supply.Short-term load forecasting(STLF)can ensure the security and stability of a power system in a short time.In order to solve the problems of insufficient prediction accuracy and lack of reference factors in the dataset,the paper proposes a short-term power load forecasting method based on a multi-feature extracted Attentionr BiGRU(Bidirectional Gated Recurrent Unit)network.The forecasting model is the basic structure of the Gated Recurrent Unit(GRU)network.According to the data set,the method extracts time characteristics and data distribution characteristics in advance.All characteristics are used as the influencing factors for load forecasting.'Then,the paper used attention mechanism to allocate the weight of the input sequence.Finally,the BiGRU network is used to learn the characteristics and output the forecasted load values.The simulation results show that the Attention BiGRU network based on multi-feature extraction outperforms the traditional Gaussian regression method,GRU network,BiGRU network with multi-feature extraction and BiGRU network.

关 键 词：短期负荷预测 多特征提取 注意力机制 BiGRU 数据挖掘 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]