Demand Forecasting for Thailand EV Charging Station Based on Deep Learning Techniques

EasyChair Preprint no. 11867

Abstract

The rapid of electric vehicles (EVs) is challenges and opportunities for energy grid management and infrastructure planning. This research is aims to fill the knowledge gap by employing advanced analytical methods on a 503-day time series dataset from Thailand's EV charging stations. The dataset includes information on date, station name, connector type, energy consumed in kWh, payment in Baht, vehicle brand and model, as well as customer ID. This study focuses on three main objectives: (1) Forecasting daily energy demand with a focus on the top 5 stations in terms of kWh consumption to identify seasonality and trends, (2) Predicting daily revenue based on energy consumption, and (3) Conducting a Geo-Spatial Analysis to recommend optimal locations for installing new EV charging stations. The insights derived are expected to assist in efficient grid management, revenue planning, and strategic infrastructure deployment.

Keyphrases: Charging Load Prediction., Decision Support System., Electric Vehicle Charging Stations., EV Adoption.

@Booklet{EasyChair:11867,
  author = {Kotchakan Fuangngam and Supanan Tantayakul and Arthittaya Narak and Maleerat Maliyeam},
  title = {Demand Forecasting for Thailand EV Charging Station Based on Deep Learning Techniques},
  howpublished = {EasyChair Preprint no. 11867},

  year = {EasyChair, 2024}}