Skip to content
Research Article Open access CC BY 4.0

Linear Regression and ARIMA Models for Electricity Demand Forecasting in West Africa

Kokou Prosper Semekonawo, Sié Kam

Journal of Energy Research and Reviews · pp. 26–36 · Published 31 Aug 2022

10.9734/jenrr/2022/v12i230296

Abstract

This article focuses on the predictive study of electricity demand in West African countries based on the multivariate linear regression model and the ARIMA model. The objective of the study is first to establish for each country a linear regression model and ARIMA model, then to compare the two (2) models based on the MAD, RMSE and MAPE coefficients, and finally to deduce of this comparison the best valid model to establish the electricity demand prediction of the country. We have come to the conclusion that the ARIMA model is more adequate for predicting the electricity demand of most of West African countries with the exception of Gambia, Ghana, Guinea, Liberia and Nigeria where the multivariate linear regression model performs better.

ARIMA demand electricity linear regression model prediction

Cited by 7

An Innovative Study on Stock Price Prediction for Investment Decision Through ARIMA and LSTM with Recurrent Neural Network

Yedhu Harikumar, M. Muthumeenakshi · New Mathematics and Natural Computation · 2024

Residential energy consumption dynamics: A SHAP-based interpretation, k-means clustering, and predictive modelling

Oluwatobi Adeleke, Oluwapelumi Joseph Adebowale, Idowu Ayoade · Green Energy and Resources · 2026

High-precision Prediction Method of Electric Vehicle Trading Power based on Neural Network

Wei Wei, Li Ye, Yi Fang · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · 2024

Showing 4 of 7 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

15

Page views

0

PDF downloads

2

Outbound clicks

7

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.