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Related Experiment Video

Updated: Aug 6, 2025

On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes

On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes

Published on: August 5, 2016

A technique for improving petroleum products forecasts using grey convolution models and genetic algorithms.

Flavian Emmanuel Sapnken^1,2, Ahmat Khazali Acyl², Michel Boukar²

¹Laboratory of Technologies and Applied Science, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon.

|March 16, 2023

Summary

Accurate energy consumption forecasting is crucial. A new hybrid model, Sequential-GMC(1,n)-GA, improves upon the traditional GM(1,1) model, offering a robust and reliable tool for predicting energy demand with minimal data.

Keywords:

Arc consistency Constraint satisfaction problems Energy demand Optimization Sequential-GMC(1,n)-GA hybrid model

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Area of Science:

Energy Economics
Forecasting Science
Applied Mathematics

Background:

Accurate energy consumption forecasting is vital for policymakers and industry.
Traditional models like GM(1,1) have limitations, including large forecast errors and sensitivity to data trends.
Selecting the most appropriate forecasting tool is critical for effective energy management.

Purpose of the Study:

To address the shortcomings of the GM(1,1) model in energy consumption forecasting.
To introduce a novel hybrid model, Sequential-GMC(1,n)-GA, for enhanced forecasting accuracy.
To provide a reliable tool for tracking energy demand growth across various sectors.

Main Methods:

Modification, extension, and optimization of the grey multivariate model (GM(1,n)).
Integration of genetic algorithms with sequential selection and arc consistency for model optimization.
Application of the Sequential-GMC(1,n)-GA model using a minimum of four observations.

Main Results:

The proposed Sequential-GMC(1,n)-GA model demonstrates robustness and reliability.
Achieved a Mean Absolute Percentage Error (MAPE) of 1.44% and Root Mean Square Error (RMSE) of 0.833.
The model is generic and applicable to diverse energy sectors, including household energy demand.

Conclusions:

The Sequential-GMC(1,n)-GA is a valid and improved forecasting tool for energy consumption.
The model overcomes limitations of traditional methods, offering higher accuracy and reliability.
Its generic nature makes it suitable for a wide range of energy forecasting applications.