Forecasting of Electricity Demand by Hybrid ANN-PSO under Shadow of the COVID-19 Pandemic

Abstract

Here in the research paper, we did not use smart methods to predict the future but rather to show the impact of the pandemic, we used the hybrid method using the PSO-ANN algorithm to demonstrate the impact of COVID-19 on electricity consumption and to demonstrate that we used two basic steps. The first step is to demonstrate that the hybrid method is effective for prediction. We showed that the prediction for 2019 was good, and that was before the onset of COVID-19. As for the second step, we applied the same hybrid algorithm after the emergence of COVID-19, i.e. for 2020, to note the difference between the prediction and the current pregnancy, which represents the impact of this epidemic, and this prediction in the short term. A short-term role in the operation of a power system in terms of achieving an economical electrical output and avoiding losses or outages. We've collected four consecutive years of data that is downloaded every quarter-hour of the day. Electricity consumption in Algeria is used as an input to the PSO-ANN learning algorithm. The results of the PSO-ANN pregnancy prediction algorithm have better accuracy than the ANN prediction. In the future but with the emergence of a pandemic that has had a clear difference and represents economic losses in the field of electricity, the epidemic should be viewed as a short-term variable to reduce the level of energy loss and generation cost.