A new artificial intelligence-based demand side management method for EV charging stations
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Today, the rapid spread of the use of electric vehicles (EVs), and accordingly EV charging stations will lead to an imbalance between generation and consumption resources. While waiting for the determination of the appropriate charging time and the determination of the suitable charge amount at the EV charging station, the most effective load management should be carried out by obtaining information from the user, including the current charging capacity, the next journey distance, the time the vehicle can stay connected to the charging station, and whether the vehicle has V2G support. In this study, a new approach is based on the ensemble learning classifier method that performs higher performance classification by bringing together the results obtained from multiple classifiers in a system with more than one EV charging station; By evaluating parameters, the system for the charging station that should be used and for how long is decided by the ensemble learning classifier structure. A scenario of the proposed intelligent demand side management (DSM) system for charging stations with multiple charging units is shown in Figure 2.1. The results show that the proposed method can perform DSM with high accuracy of 99.1% for Case-1 and 98.4% for Case-2. © 2024 Elsevier Inc. All rights reserved.
Açıklama
Anahtar Kelimeler
demand side management, EV charging station, machine learning, machine learning and RUS boost tree ensemble classifiers, RUS boost tree ensemble classifiers, smart grid
Kaynak
WoS Q Değeri
Scopus Q Değeri
N/A
