Computer Science ›› 2020, Vol. 47 ›› Issue (9): 105-109.doi: 10.11896/jsjkx.190800030

• Database & Big Data & Data Science • Previous Articles     Next Articles

Short Term Load Forecasting via Zoneout-based Multi-time Scale Recurrent Neural Network

ZHUANG Shi-jie, YU Zhi-yong, GUO Wen-zhong, HUANG Fang-wan   

  1. College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China
    Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou University,Fuzhou 350116,China
  • Received:2019-08-07 Published:2020-09-10
  • About author:ZHUANG Shi-jie,born in 1995,undergraduate.His main research interests include computational intelligence,machine learning and deep learning.
    HUANG Fang-wan,born in 1980,senior lecturer,is a member of China Computer Federation.Her main research interests include computational intelligence,machine learning and big data analysis.
  • Supported by:
    National Natural Science Foundation of China (61772136,61672159) and Research Project for Young and Middle-aged Teachers of Fujian Province (JT180045).

Abstract: Because accurate power load forecasting,smart grids can provide more efficient,reliable and environmentally friendly power services than traditional grids.In real life,power load data often has a high temporal correlation with historical data,while traditional neural networks pay little attention to it.In recent years,the recurrent neural network (RNN) has received more and more attention in power load forecasting,because it can well capture the correlation between data with large cross-time scale.However,due to the unique self-connections structure of RNN,when the back-propagation through time(BPTT) is adopted for network training,the problems such as vanishing gradient are prone to occur with the number of network layers increases,resulting in a decrease in prediction accuracy.There are varieties of RNN architectures that can solve the vanishing gradient problem,such as long short-term memory (LSTM) and gated recurrent unit (GRU),but their complex internal structure will increase the training time.In order to solve the above problems,this paper first analyzes and studies RNN and itsvariants,and then combines the Zoneout function to design a multi-time scale modularized RNN architecture,focuses on the update strategy of hidden layer modules.It not only effectively solves the vanishing gradient problem,but also greatly reduces the number of network parameters that need to be trained.Experimental results based on the benchmark dataset and the real-worldload dataset show that this architecture can achieve better performance than the current popular RNN architecture.

Key words: Short term load forecast, Recurrent neural networks, Multi-time scale, Zoneout

CLC Number: 

  • TP183
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