Time Series Analysis of Industrial Electricity Consumption in Nigeria Using Harvey Model and Autoregressive Model

Ogungbemi Emmanuel Oluropo

doi:10.11648/j.ijepe.20170603.14

Ogungbemi Emmanuel Oluropo

Open Access

https://doi.org/10.11648/j.ijepe.20170603.14

Copy DOI

Abstract

In this paper time series modeling and forecasting of industrial electricity consumption in Nigeria is presented. Specifically, Harvey Model and Autoregressive Model, (AR) are used. The data used are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin for industrial electricity consumption ranging from 1979 – 2014. The results shows that Harvey Model has (r 2 ) = 80.1% and RMSE = 65.2513 whereas Autoregressive Model has (r 2 ) = 50.1% and RMSE = 71.3985. Obviously, Harvey model has better prediction accuracy than the AR model. The Harvey model was then used to forecast industrial electricity consumption in Nigeria for the next 15 years (from 2015 to 2029). According to the forecast result by the year 2029 the industrial consumption of Nigeria will stand at 539.65 MW/h as against 468.18 MW/h in 2015.

Highlights

Across the globe, heavy dependence on electricity results in high power demand and it requires planning of resources of electricity well in advance to ensure a continuous supply of electricity both and in the future [1-4]
Studies have shown that industrial power consumption can be related to the level of industrialization and productivity of a nation [12-14]
Effective power supply can boost the economy of a nation

Summary

Introduction

Heavy dependence on electricity results in high power demand and it requires planning of resources of electricity well in advance to ensure a continuous supply of electricity both and in the future [1-4]. In this study, the focus is on modeling and forecasting the industrial power consumption in Nigeria. The quality of the demand forecast methods depends significantly on the availability of historical consumption data as well as on the knowledge about the main influence parameters on the energy consumption. These factors determine the selection of the best suitable forecast tool.

Theoretical Background

The Harvey Model

Statistical Tests for the Forecasting Models

Autoregressive Model

Conclusion