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Short-term power load forecasting method based on Bagging-stochastic configuration networks.

Xinfu Pang1, Wei Sun1, Haibo Li1

  • 1Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang, Liaoning, China.

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Summary
This summary is machine-generated.

This study introduces a novel Bagging-stochastic configuration networks (SCNs) method for accurate short-term load forecasting. The approach enhances power grid efficiency and reliability by improving prediction accuracy over traditional methods.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Traditional short-term load forecasting methods struggle with nonlinear data and time series information loss.
  • Improved forecasting accuracy is crucial for power grid efficiency, reliability, and resource management.

Purpose of the Study:

  • To develop an advanced short-term power load forecasting method.
  • To enhance the accuracy and generalization capabilities of load forecasting models.

Main Methods:

  • Data preprocessing involved filling missing values and coding influencing factors like weather and week type.
  • A Bagging-stochastic configuration networks (SCNs) integration algorithm was employed for short-term load prediction.
  • The proposed method was implemented in Python and compared against Long Short-Term Memory (LSTM) and single-SCNs algorithms.

Main Results:

  • The Bagging-SCNs method demonstrated high forecasting accuracy for medium- and short-term power load.
  • The proposed approach significantly improved load forecasting accuracy compared to benchmark algorithms.
  • The study validated the method using daily load data from Quanzhou City, Zhejiang Province.

Conclusions:

  • The Bagging-SCNs method offers a superior alternative for short-term load forecasting.
  • This advancement contributes to more efficient power grid operations and resource optimization.
  • The method shows strong potential for practical application in power system management.