Estimating Future Electric Vehicle and Charging Station Numbers Using LSTM and GRU Deep Learning Methods: Kocaeli Province Example

Year 2025, EARLY VIEW, 1 - 1

Ayşe Tuğba Yapıcı , Nurettin Abut

https://doi.org/10.2339/politeknik.1674525

Abstract

In this study, the number of electric vehicles and charging stations in Kocaeli in 2030 was estimated with two different deep learning models.Data obtained from TURKSTAT and EMRA were used for training LSTM and GRU models.In addition to the prediction results, a statistical prediction was made for 2030 with the support of an analysis company using existing data.Thus, three different prediction results were obtained. To understand which of the LSTM and GRU models provides higher prediction accuracy, low error rate and high success scores were used.LSTM and GRU achieved the same score with a value of 0.99 in the R2 success metric.While LSTM provided a value of 0.5007 and GRU a value of 0.38 in the MAE error metric, LSTM provided a value of 3.05 and GRU a value of 2.92 in the MSE error metric.The DTW metric scores are 126.97 in LSTM and 125.35 in GRU. According to the metric scores, it was determined that the GRU model gave the best result.2030 Kocaeli province charging station GRU model predictions were positioned on a neighborhood basis by preserving the existing charging stations.

Keywords

Electric vehicle , charging station , deep learning , artifical intelligence , python.

Geleceğe Yönelik Elektrikli Araç ve Şarj istasyonu Sayılarının LSTM VE GRU Derin Öğrenme Yöntemleri Kullanılarak Tahmin Edilmesi: Kocaeli İli Örneği

Abstract

Yapılan bu çalışmada iki farklı derin öğrenme modeli ile 2030 yılı Kocaeli ili elektrikli araç ve şarj istasyonu sayılarının tahmini yapılmıştır. LSTM ve GRU modellerinin eğitimi için TÜİK ve EPDK’dan alınan veriler kullanılmıştır. Yapılan tahmin sonuçlarına ek olarak mevcut veriler ile bir analiz firmasından alınan destek ile 2030 yılı için istatiksel bir tahmin yapılmıştır. Böylece üç farklı tahmin sonucu elde edilmiştir. LSTM ve GRU modellerinden hangisinin daha yüksek tahmin doğruluğunu sağladığı, düşük hata oranı ve yüksek başarı skorları ile belirlenmiştir. LSTM ve GRUR2 başarı metriğinde 0,99 değeri ile aynı skoru sağlamışlardır.MAE hata metriğinde LSTM 0.5007, GRU ise 0,38 değerini sağlarken, MSE hata metriğinde LSTM 3,05 ve GRU 2,92 değerini sağlamıştır. DTW metriği skorları ise LSTM’de 126,97, GRU ‘da ise 125,35’tir. Metrik skorlarına göre GRU modelinin en iyi sonucu verdiği belirlenmiştir. 2030 yılı Kocaeli ili şarj istasyonu GRU modeli tahminlerinin, mevcut şarj istasyonları muhafaza edilerek mahalle bazında konumlandırması yapılmıştır.

Keywords

Elektrikli araç , şarj istasyonu , derin öğrenme , yapay zeka , python.

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