Comparative of prediction algorithms for energy consumption by electric vehicle chargers for demand side management

Ayoub Abida, Redouane Majdoul, Mourad Zegrari

Abstract


This study focuses on demand side management (DSM), specifically managing electric vehicle (EV) charging consumption. Power distributors must consider numerous factors, such as the number of EVs, charging station availability, time of day, and EV user behavior, to accurately predict EV charging demand. We utilized machine learning algorithms and statistical modeling to predict the energy required by EV users for a specific charger and compared algorithms like K-Nearest Neighbors, XGBoost, random forest regressor, and ridge regressor. To contribute to the existing literature, which lacks studies on future energy prediction for a specific period, we conducted predictions for the next year 2024 on the energy consumption of electric vehicles for an electric vehicle charging point in a Moroccan city. These predictions can be generalized to other chargers as well. Our results showed that K-nearest neighbors (KNN) outperformed other algorithms in accuracy. This study provides valuable insights for distribution operators to manage energy resources efficiently and contributes to the DSM field by highlighting the effectiveness of KNN in predicting EV charging demand.

Keywords


Artificial intelligence; Demand side management; Electric vehicle; Machine learning; Prediction algorithm; Requested energy

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DOI: http://doi.org/10.11591/ijece.v15i4.pp4192-4201

Copyright (c) 2025 Ayoub Abida, Redouane Majdoul, Mourad Zegrari

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by theĀ Institute of Advanced Engineering and Science (IAES).