Work

Estimation of Future Number of Electric Vehicles and Charging Stations: Analysis of Sakarya Province with LSTM, GRU and Multiple Linear Regression Approaches

Year: 2025

Type: article

Abstract: This study estimates the number of electric vehicles (EVs) and charging stations in Sakarya Province, Türkiye, for 2030 using advanced artificial intelligence time series methods and statistical approaches. The novelty of the work lies in the application of hyperparameter-optimized LSTM and GRU models alongside Multiple Linear Regression (MLR) to a regional dataset, enabling accurate, data-driven forecasting for regional EV planning. Performance was evaluated using multiple metrics, including R2, MAE, MSE, DTW, RMSE, and MAPE, with the GRU model achieving the highest reliability and lowest errors (R2 = 0.99, MAE = 0.3, MSE = 2.9, DTW = 123.2, RMSE = 3.1, MAPE = 2.8%) under optimized parameters. The predicted EV counts and charging station numbers from GRU informed a neighborhood-level allocation of charging stations using Google Maps API, considering local population ratios. These results demonstrate the practical applicability of deep learning for regional infrastructure planning and provide a replicable framework for similar studies in other provinces.

Source: Applied Sciences

DOI: 10.3390/app152111462

Authors: Ayşe Tuğba Yapıcı, Nurettin Abut, Ahmet Yıldırım

Institutions: Kocaeli Üniversitesi, Ankara Hacı Bayram Veli University

Language: English

Topic: Electric Vehicles and Infrastructure

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities, Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $2,896