An Adaptive Forecasting Framework for EV Charging Demand Using Variational Mode Decomposition and Louvain Community Detection

Abstract

Accurate short-term forecasting of electric vehicle (EV) charging demand is crucial for demand-side management and grid stability in modern power systems, especially within the context of virtual power plant (VPP) operations. However, EV load profiles exhibit strong stochasticity and multi-scale variability, making traditional single-model predictors prone to overfitting, mode mixing, or degraded performance under shifting operating conditions. This study proposes a hybrid decomposition–clustering–adaptive forecasting framework that integrates variational mode decomposition (VMD), Louvain community detection and a lightweight adaptive model pool. First, VMD decomposes the raw load signal into mode components with reduced frequency overlap. Second, a correlation-based similarity graph is constructed and processed by the Louvain algorithm to automatically group modes with coherent temporal characteristics. Finally, an adaptive prediction mechanism selects or refines models for each community based on a normalised MSE threshold. Experiments on three real-world charging-station datasets show that the proposed method significantly improves forecasting performance, achieving a 60.78%–75.18% reduction in MAPE compared with conventional single-model baselines, and a 40.55%–53.70% improvement compared with nonadaptive VMD–LSTM schemes, while maintaining manageable computational cost. These results demonstrate the framework's robustness and its potential applicability to real-time EV charging management and other nonstationary energy forecasting tasks.