Seemingly unrelated time series model for forecasting the peak and short-term electricity demand: Evidence from the Kalman filtered Monte Carlo method
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Date
2023-08
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Publisher
Heliyon
Abstract
In this extant paper, a multivariate time series model using the seemingly unrelated times series
equation (SUTSE) framework is proposed to forecast the peak and short-term electricity demand
using time series data from February 2, 2014, to August 2, 2018. Further the Markov Chain Monte
Carlo (MCMC) method, Gibbs Sampler, together with the Kalman Filter were applied to the
SUTSE model to simulate the variances to predict the next day’s peak and electricity demand.
Relying on the study results, the running ergodic mean showed the convergence of the MCMC
process. Before forecasting the peak and short-term electricity demand, a week’s prediction from
the 28th to the 2nd of August of 2018 was analyzed and it found that there is a possible decrease
in the daily energy over time. Further, the forecast for the next day (August 3, 2018) was about
2187 MW and 44090 MWh for the peak and electricity demands respectively. Finally, the
robustness of the SUTSE model was assessed in comparison to the SUTSE model without MCMC.
Evidently, SUTSE with the MCMC method had recorded an accuracy of about 96% and 95.8% for
Peak demand and daily energy respectively
Description
This article is published by Heliyon 2023 and is also available at https://doi.org/10.1016/j.heliyon.2023.e18821
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Citation
F.K. Owusu et al.