RES-EV: Identifying EV-Households under High AC Load Using a Residual-Based Model

Abstract

This paper introduces a novel residual-based model to identify households with Battery Electric Vehicles (EVs) under high Air Conditioning (AC) load. The considerable energy demands of AC units can obscure charging events for EVs. In this work we propose a residual based model which leverages the distinctive characteristics of EV charging patterns, marked by unpredictable spikes in energy consumption, and the more predictable nature of AC load. Our proposed approach involves training a lightweight forecasting model to predict overall household consumption and utilizes the residuals of this model for identifying household with EVs. The residual-based model, ResEV-AR, demonstrated a substantial advantage in F1 score (5.8% and 7.32%) compared to state-of-the-art models such as EVS and KBF, respectively. Additionally, a simpler residual model, ResEV-SRM, exhibited a 3.5% F1 score advantage over EVS, coupled with an impressive 11-fold reduction in computation time.