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Related Experiment Video

Updated: Jun 28, 2025

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Improving the Method of Short-term Forecasting of Electric Load in Distribution Networks using Wavelet transform

Yaoying Wang1, Shudong Sun1, Gholamreza Fathi2

  • 1School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.

Heliyon
|April 18, 2024
PubMed
Summary

This study introduces a novel Ridgelet Neural Network (RNN) method for short-term electric load forecasting. Optimized by a Self-Adapted Kho-Kho algorithm, it significantly enhances prediction accuracy for electrical grids.

Keywords:
Electric load forecastingOptimizationRidgelet neural networkSelf-adapted kho-kho algorithmWavelet transform

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Time Series Analysis

Background:

  • Accurate short-term electric load forecasting is crucial for efficient power grid operation and planning.
  • Existing forecasting methods often struggle with complex temporal dependencies and require precise parameter tuning.

Purpose of the Study:

  • To develop an advanced forecasting model that improves the accuracy and reliability of short-term electric load predictions.
  • To introduce a novel hybrid approach combining Wavelet Transform, Ridgelet Neural Network, and a Self-Adapted Kho-Kho optimization algorithm.

Main Methods:

  • Decomposition of electric load data using Wavelet Transform (WT).
  • Application of Ridgelet Neural Network (RNN) to individual frequency components.
  • Optimization of the RNN using a dynamically adaptive Self-Adapted Kho-Kho (SAKhoKho) algorithm.

Main Results:

  • The proposed RNN/SAKhoKho/WT method achieved the lowest Mean Absolute Error (MAE) of 7.7704 and Root Mean Square Error (RMSE) of 17.4132.
  • Demonstrated superior performance compared to six state-of-the-art methods including SVM/SA, ARIMA, MLP/PSO, and CNN.
  • Successfully captured temporal dependencies and optimized RNN weights for minimized prediction error.

Conclusions:

  • The proposed method offers a significant advancement in short-term electric load forecasting accuracy and reliability.
  • It provides a promising technique for real-time grid management by delivering precise hourly predictions.
  • The hybrid approach effectively leverages signal decomposition and adaptive optimization for enhanced forecasting capabilities.