Multiple wavelet convolutional neural network for short-term load forecasting

Zhifang Liao, Haihui Pan, Xiaoping Fan*, Yan Zhang, Li Kuang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
191 Downloads (Pure)


Although the accuracy of load forecasting has been studied by many works, the actual deployability of a model is rarely considered. In this work, we consider the actual deployability of a model from four aspects: 1) the prediction performance of the model; 2) the robustness of the model; 3) the dependence of the model on external data; and 4) the storage size of the model. From these four aspects, we propose a multiple wavelet convolutional neural network (MWCNN) for load forecasting. On two public data sets, we verified the performance and robustness of the MWCNN. The MWCNN only uses load data, and the storage size of the model is only 497 kB, which shows that MWCNN has good deployability. In addition, our MWCNN prediction results are interpretable. The experimental results show that the MWCNN can effectively capture the periodic characteristics of load data.

Original languageEnglish
Pages (from-to)9730-9739
Number of pages10
JournalIEEE Internet of Things Journal
Issue number12
Early online date25 Sep 2020
Publication statusPublished - 15 Jun 2021


  • load modeling
  • predictive models
  • load forecasting
  • data models
  • wavelet transforms
  • internet of things
  • robustness
  • wavelet reconstruction
  • deployability
  • interpretability
  • convolutional neural network (CNN)
  • short-term load forecasting

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications


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