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

Serge Guefano¹, Jean Gaston Tamba¹, Tchitile Emmanuel Wilfried Azong¹

¹University of Douala, University Institute of Technology of Douala, Cameroon.

|August 26, 2021

Summary

Accurate electricity demand forecasting is crucial. A new hybrid GM(1,1)-VAR(1) model, incorporating economic and demographic factors, offers superior prediction accuracy compared to existing methods.

Keywords:

Electricity consumption Forecast Grey model VAR model hybrid model GM(1,1)-VAR(1)

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Area of Science:

Energy economics
Econometrics
Forecasting methodologies

Background:

Accurate energy demand forecasting is essential for effective energy management and resource allocation.
Existing forecasting models often struggle to capture complex economic and demographic influences on energy consumption.
The need for reliable tools to monitor evolving consumer energy needs is paramount.

Purpose of the Study:

To develop a novel hybrid forecasting model for electricity demand.
To improve the accuracy and reliability of energy demand predictions.
To integrate key economic and demographic determinants into an exponential growth trend model.

Main Methods:

Coupling the Grey (GM(1,1)) model with the Vector Autoregressive (VAR(1)) model to create a hybrid GM(1,1)-VAR(1) approach.
Incorporating five economic and demographic parameters to account for influencing factors.
Evaluating model performance using accuracy indicators such as Absolute Percentage Error (APE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE).

Main Results:

The hybrid GM(1,1)-VAR(1) model achieved an APE of 1.5, a MAPE of 1.628%, and an RMSE of 15.42.
Demonstrated superior accuracy compared to other hybrid models of a similar nature.
Exhibited accuracy comparable to recent hybrid artificial intelligence models.

Conclusions:

The developed hybrid GM(1,1)-VAR(1) model is a reliable tool for forecasting energy demand.
The model effectively integrates economic and demographic factors for more accurate predictions.
This approach enhances the monitoring of evolving energy consumption patterns.