期刊论文

Research on CNN-BiLSTM Short-term Power Load Forecasting Model Based on Attention Mechanism and ResNet

中文

南华大学学报(自然科学版)

1673-0062

2023

37

01

33-39+86

10.19431/j.cnki.1673-0062.2023.01.005

国家自然科学基金资助项目(U1867221)； 湖南省教育厅科学研究项目(19C1568)；

本校为第一机构

短期电力负荷预测有利于电力系统的高效运行，对电力市场实现有效调度有重要意义。短期电力负荷受多种因素影响，波动性大、随机性强，使得其预测准确率低。双向长短期记忆网络和卷积神经网络难以在短期负荷序列中提取足够多的信息，本文提出了一种结合注意力机制和残差网络的卷积神经网络-双向长短期记忆网络短期负荷预测方法。首先利用基准模型卷积神经网络-双向长短期记忆网络对输入特征进行信息提取，然后利用注意力机制突出提取到的关键信息，最后通过残差网络创建残差层以充分学习时序特征。通过某公开数据集进行实验，结果表明该方法的平均绝对百分比误差达到2.80%,均方根误差达到2.15,并...

Short term power load forecasting is beneficial to the efficient operation of power system and is of great significance to the effective dispatching of power market.Short term power load is affected by many factors,with large volatility and strong randomness,which makes...MORE Short term power load forecasting is beneficial to the efficient operation of power system and is of great significance to the effective dispatching of power market.Short term power load is affected by many factors,with large volatility and strong randomness,which makes its prediction accuracy low.It is difficult for BiL...