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Methodology for forecasting electricity consumption by Grey and Vector autoregressive models.

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Econometric methodology for selecting explanatory factors for power consumption.

Serge Guefano1

  • 1University of Douala, University Institute of Technology, Laboratory of Technologies and applied science, P.O. BOX 8698, Douala, Cameroon.

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Summary

This study introduces a novel method for selecting energy demand model inputs. It reveals that GDP per capita, CO2 emissions, urbanization, and subscriber numbers significantly influence residential electricity consumption in Cameroon.

Keywords:
CameroonCausalityElectricity consumptionExplanatory factors

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Area of Science:

  • Energy Economics
  • Econometrics
  • Environmental Science

Background:

  • Accurate energy demand modeling relies heavily on appropriate input variable selection.
  • Current methods often use simple correlations or descriptive analyses, potentially missing true causal relationships.

Purpose of the Study:

  • To propose a new methodology for selecting input variables for electricity demand modeling.
  • To identify key determinants of residential electricity consumption through a robust selection process.

Main Methods:

  • Integration of stationarity tests, the Johansen cointegration test, Autoregressive Distributed Lag (ARDL) modeling, and Vector Error Correction (VECM) modeling.
  • Application of the methodology to analyze electricity demand in Cameroon.

Main Results:

  • A unidirectional causal relationship was established from GDP per capita, CO2 emissions, urbanization, and the number of subscribers to residential electricity consumption.
  • The proposed methodology effectively highlights real causal links between input variables and electricity demand.

Conclusions:

  • The identified key factors (GDP per capita, CO2 emissions, urbanization, subscriber numbers) are crucial for explaining residential electricity consumption in Cameroon.
  • The developed input selection methodology optimizes the choice of variables for improved energy demand modeling and forecasting.