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Forecasting methodology with structural auto-adaptive intelligent grey models.

Flavian Emmanuel Sapnken1,2, Jean Gaston Tamba1,2

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

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Summary

A new Structural Auto-Adaptive Intelligent Grey Model (SAIGM) improves petroleum products consumption forecasting. This adaptable model enhances prediction accuracy for energy planning and reserve management.

Keywords:
Grey forecasting modelModelling knowledgeParameterizationSAIGM modelStructural auto-adaptive intelligent grey model (SAIGM)Structural flexibility

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Area of Science:

  • Energy Economics
  • Forecasting Science
  • Mathematical Modeling

Background:

  • Accurate mid- and long-term petroleum products (PP) consumption forecasting is crucial for strategic reserve management and energy planning.
  • Traditional grey models have limitations in adaptability and accuracy for diverse forecasting scenarios.

Purpose of the Study:

  • To develop a novel Structural Auto-Adaptive Intelligent Grey Model (SAIGM) for enhanced energy forecasting.
  • To improve the accuracy and flexibility of petroleum products consumption predictions.

Main Methods:

  • A novel time response function was developed to address weaknesses in traditional grey models.
  • Optimal parameter values were calculated using SAIGM for increased adaptability.
  • The SAIGM model was validated using both synthetic algebraic series and real-world petroleum consumption data from Cameroon.

Main Results:

  • SAIGM achieved high accuracy with an RMSE of 3.10 and 1.54% MAPE.
  • The model demonstrated superior performance compared to existing intelligent grey systems.
  • SAIGM effectively extracted underlying patterns from data without requiring input attribute determination or data preprocessing.

Conclusions:

  • SAIGM is a robust and flexible forecasting tool for petroleum products consumption.
  • The model offers enhanced forecasting power for intelligent grey models, adaptable to various data specifications.
  • SAIGM provides a valid approach for tracking and predicting energy demand growth, as demonstrated with Cameroon's PP consumption data.