Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (1): 231-236.DOI: 10.11772/j.issn.1001-9081.2020060929

Special Issue: 第八届中国数据挖掘会议(CCDM 2020)

• China Conference on Data Mining 2020 (CCDM 2020) • Previous Articles     Next Articles

Multi-scale skip deep long short-term memory network for short-term multivariate load forecasting

XIAO Yong1, ZHENG Kaihong1, ZHENG Zhenjing2, QIAN Bin1, LI Sen2, MA Qianli2   

  1. 1. Electric Power Research Institute, China Southern Power Grid International Company Limited, Guangzhou Guangdong 510080, China;
    2. School of Computer Science and Engineering, South China University of Technology, Guangzhou Guangdong 510006, China
  • Received:2020-05-31 Revised:2020-07-20 Online:2021-01-10 Published:2020-11-12
  • Supported by:
    This work is partially supported by the Key Project of National Natural Science Foundation of China (61751205), the National Natural Science Foundation of China (61872148).


肖勇1, 郑楷洪1, 郑镇境2, 钱斌1, 李森2, 马千里2   

  1. 1. 南方电网科学研究院有限责任公司, 广州 510080;
    2. 华南理工大学 计算机科学与工程学院, 广州 510006
  • 通讯作者: 马千里
  • 作者简介:肖勇(1978-),男,湖南怀化人,高级工程师,博士,主要研究方向:电能计量管理、电能计量自动化系统、用电技术;郑楷洪(1991-),男,广东汕头人,工程师,硕士,主要研究方向:电能计量、电能计量自动化系统、用电技术;郑镇境(1996-),男,广东揭阳人,硕士研究生,主要研究方向:数据挖掘、机器学习、神经网络;钱斌(1989-),男,湖北十堰人,工程师,硕士,主要研究方向:电能计量;李森(1994-),男,广东茂名人,硕士研究生,主要研究方向:数据挖掘、机器学习、神经网络;马千里(1980-),男,甘肃宕昌人,教授,博士,主要研究方向:数据挖掘、机器学习、神经网络。
  • 基金资助:

Abstract: In recent years, the short-term power load prediction model built with Recurrent Neural Network (RNN) as main part has achieved excellent performance in short-term power load forecasting. However, RNN cannot effectively capture the multi-scale temporal features in short-term power load data, making it difficult to further improve the load forecasting accuracy. To capture the multi-scale temporal features in short-term power load data, a short-term power load prediction model based on Multi-scale Skip Deep Long Short-Term Memory (MSD-LSTM) was proposed. Specifically, a forecasting model was built with LSTM (Long Short-Term Memory) as main part, which was able to better capture long short-term temporal dependencies, thereby alleviating the problem that important information is easily lost when encountering the long time series. Furthermore, a multi-layer LSTM architecture was adopted and different skip connection numbers were set for the layers, enabling different layers of MSD-LSTM can capture the features with different time scales. Finally, a fully connected layer was introduced to fuse the multi-scale temporal features extracted by different layers, and the obtained fusion feature was used to perform the short-term power load prediction. Experimental results show that compared with LSTM, MSD-LSTM achieves lower Mean Square Error (MSE) with the reduction of 10% in general. It can be seen that MSD-LSTM can better capture multi-scale temporal features in short-term power load data, thereby improving the accuracy of short-term power load forecasting.

Key words: short-term power load forecasting, time series forecasting, multi-scale temporal feature, Long Short-Term Memory (LSTM) network, skip connection

摘要: 近年来,以循环神经网络(RNN)为主体构建的预测模型在短期电力负荷预测中取得了优越的性能。然而,由于RNN不能有效捕捉存在于短期电力负荷数据的多尺度时序特征,因而难以进一步提升负荷预测精度。为了捕获短期电力负荷数据中的多尺度时序特征,提出了一种基于多尺度跳跃深度长短期记忆(MSD-LSTM)网络的短期电力负荷预测模型。具体来说,以长短期记忆(LSTM)网络为主体构建预测模型能够较好地捕获长短期时序依赖,从而缓解时序过长时重要信息容易丢失的问题。进一步地,采用多层LSTM架构并且对各层设置不同的跳跃连接数,使得MSD-LSTM的每一层能够捕获不同时间尺度的特征。最后,引入全连接层把各层提取到的多尺度时序特征进行融合,再利用该融合特征进行短期电力负荷预测。实验结果表明,与单层LSTM和多层LSTM相比,MSD-LSTM的均方误差总体下降了10%。可见MSD-LSTM能够更好地提取短期负荷数据中的多尺度时序特征,从而提高短期电力负荷预测的精度。

关键词: 短期电力负荷预测, 时间序列预测, 多尺度时序特征, 长短期记忆网络, 跳跃连接

CLC Number: