Forecasting gasoline consumption using machine learning algorithms during COVID-19 pandemic

ABSTRACT

Due to travel restrictions and the general economic slowdown caused by the Coronavirus Disease 2019 (COVID-19), the gasoline consumption profile has exhibited unusual behavior. Depending on the severity of lockdown policies, the consumption pattern has changed even at different stages of the epidemic. Forecasting gasoline demand has become a more difficult and essential tool for energy planning. Therefore, reliable models are needed to ensure energy security in pandemic conditions. Presenting a case study on Turkey, this paper investigates the impact of the COVID-19 pandemic on gasoline demand. Four common machine learning models, including Gaussian Process Regression, Sequential Minimal Optimization Regression, Multi-Layer Perceptron Regressor, and Random Forest, were used to estimate daily gasoline consumption. In the training of the models, inputs such as historical gasoline demand, national holidays, date attributes, gasoline price, and COVID-19 related factors such as curfews and travel bans were considered. Analysis results showed that the Random Forest model performed best with the highest correlation coefficient (0.959) and the lowest mean absolute percentage error (11.526%), and root mean square percentage error (17.022%) values in the test dataset. This study can help policymakers understand the impact of such an emergency on the energy industry and respond quickly to potential threats.

KEYWORDS:

• COVID-19 pandemic
• gasoline consumption
• prediction
• machine learning
• lockdown period

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Notes on contributors

Zeynep Ceylan

Zeynep Ceylan is currently an Associate Professor in the Department of Industrial Engineering at Samsun University, Turkey. She received her BSc degree in Industrial Engineering from Gaziantep University and her MSc and PhD degree in Industrial Engineering from Marmara University. Dr. Ceylan's research expertise and interests are Artificial Intelligence, Scheduling, Data Mining, and Metaheuristic Algorithms.

Derya Akbulut

Derya Akbulut obtained her PhD in Industrial Engineering from Middle East Technical University. She is currently an Assistant Professor at Industrial Engineering Department of Samsun University in Turkey and her area of research interests includes mathematical programming models especially for optimization in data mining problems.

Engin Baytürk

Engin Baytürk is currently an Assistant Professor of Industrial Engineering at Samsun University, Turkey. He received his BSc in Industrial Engineering from İstanbul Kültür University in 2012, his MSc in Industrial Engineering from İstanbul University in 2016 and his PhD in Industrial Engineering from İstanbul University – Cerrahpaşa in 2020. His areas of Interests include Stochastic Processes, Facility Location, Machine Learning.