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Cross-Modal Multivariate Pattern Analysis
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A novel prediction approach using wavelet transform and grey multivariate convolution model.

Flavian Emmanuel Sapnken1,2,3, Marius Tony Kibong1,2,3, Jean Gaston Tamba1,2,3

  • 1Laboratory of Technologies and Applied Science, IUT Douala, P.O. Box 8698, Douala, Cameroon.

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Summary

This study introduces an optimized grey multivariate convolution model (ODGMC(1,N)) for more accurate electricity demand forecasting. The enhanced model improves prediction reliability and stability, outperforming existing methods.

Keywords:
Convolution integralsElectricity forecastingGrey systemsOptimal discrete grey multivariate convolution modelWavelet transform

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Area of Science:

  • Energy Economics
  • Forecasting Science
  • Applied Mathematics

Background:

  • Accurate electricity consumption forecasting is vital for energy management and planning.
  • Existing grey multivariate convolution models (GMC(1,N)) have limitations in accuracy and stability.

Purpose of the Study:

  • To propose a novel, optimized discrete grey multivariate convolution model (ODGMC(1,N)).
  • To enhance the accuracy and stability of electricity demand forecasting.
  • To validate the model's performance using Cameroon's annual electricity demand data.

Main Methods:

  • Developed ODGMC(1,N) by incorporating a linear corrective term into the GMC(1,N) structure.
  • Employed an iterative technique for parameter estimation and cumulated forecasting function calculation.
  • Utilized wavelet transform for noise reduction and feature extraction from input data.

Main Results:

  • The ODGMC(1,N) model demonstrated superior forecasting accuracy with a Mean Absolute Percentage Error (MAPE) of 1.74% and Root Mean Square Error (RMSE) of 132.16.
  • The model showed improved reliability and stability compared to competing forecasting techniques.
  • The ODGMC(1,N) effectively corrected linear impacts on forecasting performance.

Conclusions:

  • The proposed ODGMC(1,N) model offers a more precise and stable approach to electricity demand forecasting.
  • The model's ability to track annual electricity demand makes it a valuable tool for energy sector analysis.
  • Further applications of ODGMC(1,N) in energy consumption prediction are recommended.