Research article

Modeling the hourly consumption of electricity during period of power crisis

  • Received: 08 May 2023 Revised: 10 July 2023 Accepted: 19 July 2023 Published: 27 July 2023
  • In this paper, we capture the dynamic behavior 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 behavior 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, and the high electricity consumption regime is expected to last 2.3 hours before switching to the low demand regime. 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.

    Citation: Samuel Asante Gyamerah, Henry Ofoe Agbi-Kaiser, Keziah Ewura Adjoa Amankwah, Patience Anipa, Bright Arafat Bello. Modeling the hourly consumption of electricity during period of power crisis[J]. Clean Technologies and Recycling, 2023, 3(3): 148-165. doi: 10.3934/ctr.2023010

    Related Papers:

  • In this paper, we capture the dynamic behavior 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 behavior 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, and the high electricity consumption regime is expected to last 2.3 hours before switching to the low demand regime. 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.



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