Computational Models for Forecasting Electric Vehicle Energy Demand. (3rd February 2023)

• Record Type: Journal Article
• Authors: Oyedeji, Mojeed O.; AlDhaifallah, Mujahed; Rezk, Hegazy; Mohamed, Ahmed Ali A.
• Other Names: Mikhaylov Alexey Academic Editor.
• Is Part Of: International journal of energy research. Volume 2023(2023)
• Publication Date: 2023-02-03
• Subjects: Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1155/2023/1934188
• Languages: English
• ISSNs: 0363-907X

Abstract

Abstract : Electric vehicles (EV) are fast becoming an integral part of our evolving society. There is a growing movement in advanced countries to replace gas-driven vehicles with EVs towards cutting down pollution from emissions. When fully integrated into society, electric vehicles will share from energy available on the grid; therefore, it is important to understand consumption profiles for EVs. In this study, some computation models are developed from predicting day-ahead energy consumption for electric vehicles in the city of Barcelona. Five different machine learning algorithms namely support vector regression (SVR), Gaussian process regression (GPR), artificial neural networks (ANN), decision tree (DT), and ensemble learners were used to train the forecasting models. The hyperparameters for each of the ML algorithms were tuned by Bayesian optimization algorithm. In order to propose efficient features for modeling EV demand, two different model structures were investigated, named Type-I and Type-II model. In the instance of the Type-I model, seven regressors representing the consumption of the previous seven days were considered as input features. The Type-II models considered only the EV consumption on the previous day and on the same day in the previous week. Based on the results in this study, we find that the performance of the Type-II models was as good as the Type-I models across all the algorithms considered although less input features were considered. Overall, the all algorithms employed in this study gave about 75-80% model accuracy based on the R 2 performance criterion. The models formulated in this study may prove useful for planning and unit commitment functions in city energy management functions.