Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060
The rapid uptake of electric vehicles (EVs) in the Jawa–Madura–Bali (Jamali) grid produces highly variable charging demands that threaten the supply–demand balance. To forestall instability, we developed a predictive simulation based on long short-term memory (LSTM) networks that combines historical...
Saved in:
Main Authors: | Joshua Veli Tampubolon, Rinaldy Dalimi, Budi Sudiarto |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-07-01
|
Series: | World Electric Vehicle Journal |
Subjects: | |
Online Access: | https://www.mdpi.com/2032-6653/16/7/408 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A comprehensive review on AIoT applications for intelligent EV charging/discharging ecosystem
by: Lilia Tightiz, et al.
Published: (2025-07-01) -
Development of EV charging topologies and communication protocols for resilient grid integration and V2X applications in sustainable energy systems
by: Muhammad Adnan Khan, et al.
Published: (2025-07-01) -
Unraveling the Smart Charging Technologies, Energy Sources, and Regulatory Standards for EVs
by: Ansif Arooj, et al.
Published: (2025-01-01) -
Performance Analysis of a Solar Carport Located in Neoville Campus of the Federal University of Technology of Paraná in EVs Context
by: Mirella Augusto Rodrigues, et al.
Published: (2025-07-01) -
Grid Forming Inverters for Electric Vehicle Charging Stations to Enhance Distribution Grid Resilience
by: Stefano Barsali, et al.
Published: (2025-01-01)