Estimation of Future Number of Electric Vehicles and Charging Stations: Analysis of Sakarya Province with LSTM, GRU and Multiple Linear Regression Approaches
APPLIED SCIENCES, cilt.15, sa.21, ss.1-23, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 15 Sayı: 21
- Basım Tarihi: 2025
- Doi Numarası: 10.3390/app152111462
- Dergi Adı: APPLIED SCIENCES
- Derginin Tarandığı İndeksler: Applied Science & Technology Source, Scopus, Aerospace Database, Agricultural & Environmental Science Database, Science Citation Index Expanded (SCI-EXPANDED), Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
- Sayfa Sayıları: ss.1-23
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Ankara Hacı Bayram Veli Üniversitesi Adresli: Evet
Özet
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 hyperparameteroptimized 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.
Keywords: electric vehicle charging station; artificial intelligence; LSTM model; GRU
model; multiple linear regression