Pandemic-Aware Day-Ahead Demand Forecasting Using Ensemble Learning

• Published in: IEEE access Vol. 10; pp. 7098 - 7106
• Main Authors: Arjomandi-Nezhad, Ali, Ahmadi, Amirhossein, Taheri, Saman, Fotuhi-Firuzabad, Mahmud, Moeini-Aghtaie, Moein, Lehtonen, Matti
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; COVID-19; COVID-19 pandemic; decision tree ensembles; Decision trees; Demand forecasting; Economic forecasting; Electric power demand; Electricity consumption; Ensemble learning; Forecasting; Load modeling; Machine learning; Mathematical models; Operation scheduling; Pandemics; Power consumption; Prediction models; Predictive models; probabilistic; Statistical analysis; Uncertainty
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3142351

Electricity demand forecast is necessary for power systems' operation scheduling and management. However, power consumption is uncertain and depends on several factors. Moreover, since the onset of covid-19, the electricity consumption pattern went through significant changes across the globe, which made the forecasting demand more challenging. This is mainly due to the fact that pandemic-driven restrictions changed people's lifestyles and work activities. This calls for new forecasting algorithms to more effectively handle these conditions. In this paper, ensemble-based machine learning models are utilized for this task. The lockdown temporal policies are added to the feature set in order to make the model capable of correcting itself in pandemic situations and enhance data quality for the forecasting task. Several ensemble-based machine learning models are examined for the short-term country-level demand prediction model. Besides, the quantile random forest regression is implemented for a probabilistic point of view. For case studies, the models are trained for predicting Germany's country-level demand. The results indicate that ensemble models, especially boosting and bagging-boosting models, are capable of accurate country-level demand forecast. Besides, the majority of these models are robust against missing the pandemic policy data. However, utilizing the pandemic policy data as features increases the forecasting accuracy during the pandemic situation significantly. Furthermore, the probabilistic quantile regression demonstrated high accuracy for the aforementioned case study.