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Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation

Author

Listed:
  • Huiting Zheng

    (College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    These authors contributed equally to this work.)

  • Jiabin Yuan

    (College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    These authors contributed equally to this work.)

  • Long Chen

    (College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    These authors contributed equally to this work.)

Abstract

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.

Suggested Citation

  • Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1168-:d:107502
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    References listed on IDEAS

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