20.500.12556/DKUM-96937 Optimizing electric vehicle charging strategies using multi-layer perception-based spatio-temporal prediction of charging station loading The production and sales of electric vehicles have been increasing in line with the gradual adaptation of consumers to electric vehicles, driven by various government policies and subsidies over the past decade. As a result, the deployment of charging facilities, which are essential for supporting electric vehicles, has also been extensive. However, despite the rapid development in scale, charging facilities face challenges such as low utilization during off-peak hours and excessive congestion during peak hours, leading to resource wastage and a diminished user charging experience. To address these issues, this study proposes a spatio-temporal prediction model for charging station load. The model introduces a global spatial enhancement module to simultaneously learn short-range and long-range spatial dependencies in the data, resulting in improved prediction accuracy. The aim of this research is to provide practical guidance in terms of conserving charging resources and enhancing user charging experience based on spatio-temporal prediction effectiveness. electric vehicles spatio-temporal prediction harging station load multi-layer perception MLP električna vozila prostorsko-časovna napoved polnilne postaje true false true Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Članek v reviji 2026-02-04 10:30:12 2026-02-04 10:30:13 2026-02-05 03:12:09 0000-00-00 00:00:00 2024 0 0 str. 443-459 no. 4 Vol. 19 Dec. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-08-06 2024-12-12 2024-12-30 621.3 267220227 10.14743/apem2024.4.518 1854-6250 APEM19-4_443-459.pdf APEM19-4_443-459.pdf 1 313A3D59C1E6E96E20E5DE853E67E1CE e2745c0ca2ccf75922b44af29e04d43940fcdc2ff72c75d6ae365d64e4c1ed8e 7a49e044-01ac-11f1-841f-00155d581219 https://dk.um.si/Dokument.php?lang=slv&id=203035 https://apem-journal.org/Archives/2024/Abstract-APEM19-4_443-459.html 0 8028b43e-01ac-11f1-841f-00155d581219 https://dk.um.si/Dokument.php?lang=slv&id=203036 Fakulteta za strojništvo Advances in production engineering & management 0 0 0