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Wind power forecast based on improved Long Short Term Memory network

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  • Han, Li
  • Jing, Huitian
  • Zhang, Rongchang
  • Gao, Zhiyu

Abstract

In order to improve the forecast accuracy of wind power, an Improved Long Short Term Memory (ILSTM) network structure is proposed. Firstly, Variational Mode Decomposition (VMD) method is adopted to decompose wind power signal to the long-term component, the fluctuation component and the random component, which are used as the input of forecast model. Then a parameter was defined and added to the memory cell to suppress the random component to long term memory of neural network. To provide a pass for the current random component, the output gate was modified accordingly. Compared with the traditional Long Short Term Memory (LSTM), the improved LSTM can reduce the impact of random component on the patterns in long term memory cells, while maintain the current random component in the short term memory of network. As a result, the learning for the real patterns of wind power is strengthened, avoiding over-fitting and achieve a better generalized forecast model. Finally, the performance of the forecast method proposed in this paper is tested by using the wind power data from the Belgian ELIA website.

Suggested Citation

  • Han, Li & Jing, Huitian & Zhang, Rongchang & Gao, Zhiyu, 2019. "Wind power forecast based on improved Long Short Term Memory network," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319954
    DOI: 10.1016/j.energy.2019.116300
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    References listed on IDEAS

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