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Forecasting energy consumption and wind power generation using deep echo state network

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  • Hu, Huanling
  • Wang, Lin
  • Lv, Sheng-Xiang

Abstract

Accurate energy forecasting is of great significance for the energy sector to formulate short-term plans and long-term development strategies for meeting energy needs. This study develops a stacked hierarchy of reservoirs (DeepESN) for forecasting energy consumption and wind power generation by introducing the deep learning framework into the basic echo state network. DeepESN combines the powerful nonlinear time series modeling ability of echo state network and the efficient learning ability of the deep learning framework. Two comparative examples and an extended application are analyzed to validate the accuracy and reliability of DeepESN. These comparative examples reveal that DeepESN outperforms the existing popular models, persistence model, back-propagation neural network, and echo state network. Moreover, compared with echo state network, DeepESN shows 51.56%, 51.53%, and 35.43% improvements in terms of mean absolute error, root mean square error, and mean absolute percentage error in the extended application, respectively. Therefore, DeepESN is an appropriate tool for forecasting energy consumption and wind power generation on account of its effective and stable forecasting performance.

Suggested Citation

  • Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    • Handle: RePEc:eee:renene:v:154:y:2020:i:c:p:598-613
    DOI: 10.1016/j.renene.2020.03.042
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