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Wind Farm NWP Data Preprocessing Method Based on t-SNE

Author

Listed:
  • Jiu Gu

    (School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Minhang District, Shanghai 200240, China)

  • Yining Wang

    (School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Minhang District, Shanghai 200240, China)

  • Da Xie

    (School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Minhang District, Shanghai 200240, China)

  • Yu Zhang

    (State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China)

Abstract

The operation prediction of wind farms will be accompanied by the need for massive data processing, especially the preprocessing of wind farm meteorological data or numerical weather prediction (NWP). Because NWP data are strongly correlated with wind farm operation, proper processing of NWP data could not only reduce data volume but also improve the correlations of wind farm operation predictions. For this purpose, this paper proposes a data preprocessing algorithm based on t-distributed stochastic neighbor embedding (t-SNE). Firstly, the data collected were normalized to eliminate the influence caused by different dimensions. The t-SNE algorithm is then used to reduce the dimensionality of the NWP data related to wind farm operation. Finally, the wind farm data visualization platform is established. In this paper, 22 index variables in NWP data were taken as objects. The t-SNE method was used to preprocess the NWP historical data of a wind farm, and the results were compared with the results of the principal component analysis (PCA) algorithm. It outperformed PCA in error precision; in addition, t-SNE dimension reduction preprocessing also had a visual effect, which could be applied to big data visualization platforms. A long short-term memory network (LSTM) was used to predict the operation of the wind farm by combining the preprocessed NWP data and the operation data. The simulation results proved that the effect of the preprocessed NWP data based on t-SNE on the wind power prediction was significantly improved.

Suggested Citation

  • Jiu Gu & Yining Wang & Da Xie & Yu Zhang, 2019. "Wind Farm NWP Data Preprocessing Method Based on t-SNE," Energies, MDPI, vol. 12(19), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3622-:d:269834
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    References listed on IDEAS

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    4. Xia, Fang & Song, Feng, 2017. "Evaluating the economic impact of wind power development on local economies in China," Energy Policy, Elsevier, vol. 110(C), pages 263-270.
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    Cited by:

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    2. Zhenyu He & Xiaochen Zhang & Chao Liu & Te Han, 2020. "Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model," Energies, MDPI, vol. 13(18), pages 1-20, September.

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