Modelling the Hourly Consumption of Electricity during Period of Power Crisis

TL;DR

This study models hourly electricity consumption during Ghana's power crisis using a two-state Markov switching autoregressive model, revealing regime durations and consumption patterns with high accuracy.

Contribution

It introduces a Markov switching autoregressive model tailored for capturing dynamic electricity demand during crises, outperforming traditional autoregressive models.

Findings

87% probability of remaining in low consumption regime

Low regime lasts approximately 7.8 hours daily

High regime lasts about 2.3 hours daily

Abstract

In this paper, we capture the dynamic behaviour of hourly consumption of electricity during the period of power crisis ("dumsor" period) in Ghana using two-state Markov switching autoregressive (MS-AR) and autoregressive (AR) models. Hourly data between the periods of January 1, 2014, and December 31, 2014 was obtained from the Ghana Grid company and used for the study. Using different information criteria, the MS(2)-AR(4) is selected as the optimal model to describe the dynamic behaviour of electricity consumption during periods of power crisis in Ghana. The parameters of the MS(2)-AR(4) model are then estimated using the expectation-maximization algorithm. From the results, the likelihood of staying under a low electricity consumption regime is estimated to be 87\%. The expected duration for a low electricity consumption regime is 7.8 hours daily, and the high electricity consumption regime is expected to last 2.3 hours daily. The proposed model is robust as compared to the autoregressive model because it effectively captures the dynamics of electricity demand over time through the peaks and significant fluctuations in consumption patterns. Similarly, the model can identify distinct regime changes linked to electricity consumption during periods of power crises.