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

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 house-hold consumption and utilizes the residuals of this model for iden-tifying 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.

I. Introduction

The automotive industry has witnessed a significant trans-formation in recent years, with a notable shift towards Electric Vehicles (EVs) aimed at reducing greenhouse gas (GHG) emissions from the transportation sector [1]. Advances in battery technology, along with the introduction of fast home chargers, have facilitated the widespread adoption of electric vehicles. A type 2 home based charger operating at 22 Kw is capable of fully charging EV using overnight parking. Even a slower 7 kW charger, operating overnight, can fully charge an EV before departure approximately 97% of the time [2], highlighting the efficiency of home based Ev charging.