Impact of COVID-19 Lockdown in Short-term Load Forecasting
Miguel López, Sergio Valero, Carolina Senabre
ELECTRIMACS'24 - INTERNATIONAL CONFERENCE ON MODELING AND SIMULATION OF ELECTRIC MACHINES, CONVERTERS AND SYSTEMS  (Castelló de la Plana, Spain, 27-30 May 2024)
Ed. Universitat Jaume I International Association for Mathematics and Computer in Simulation (IMACS)

Resumen:

Abstract Accurate prediction of electrical demand is crucial for the efficient operation of power systems. However, the unprecedented activity restrictions imposed during the pandemic led to unforeseen disruptions in electrical consumption, challenging the predictive capabilities of existing systems. This phenomenon was widespread, affecting power systems globally, as evidenced by analyses of the Spanish electricity grid presented in this paper. The precision of prediction systems significantly diminished upon the implementation of activity restrictions. This article offers an in-depth analysis of the impact on prediction accuracy in the Spanish context. Moreover, it proposes a method to identify situations where the prediction system is out of control, necessitating corrective measures. The paper introduces a straightforward corrective measure that reduces errors during out-of-control periods. It is designed as an addition to the existing forecast model, modifying its output when an out-of-control situation is reached. The results suggest that further investigation could substantially mitigate the impact of such events and enhance prediction system resilience.