Elektrikli Araç Büyümesine Dayalı Elektrik Talebi Tahmini: Gradient Boosting, KNN ve Extra Trees Modellerinin Karşılaştırmalı Analizi

Year 2025, Volume: 8 Issue: 2, 319 - 333, 25.10.2025

Ramiz Görkem Birdal

https://doi.org/10.51513/jitsa.1649077

Abstract

Bu araştırmada, elektrikli araç sayısının yıllık elektrik tüketimi üzerindeki etkisi makine öğrenmesi yöntemleri kullanılarak incelenmiştir. Güncel veriler kullanılarak, elektrik tüketimi tahmini için Gradient Boosting, K-Nearest Neighbors (KNN), Extra Trees, Bayesian Ridge ve Elastic Net modelleri uygulanmıştır. Modeller, MAE (Ortalama Mutlak Hata), RMSE (Kök Ortalama Kare Hata) ve R² (Determinasyon Katsayısı) gibi metriklerle değerlendirilmiştir. Elde edilen sonuçlara göre, en başarılı model KNN Regressor olmuştur. KNN modeli MAE = 17,186, RMSE = 21,003 ve R² = 0.81 değeriyle en yüksek doğruluğa ulaşmıştır. Gradient Boosting ve Extra Trees modelleri de sırasıyla 24,283 ve 22,965 RMSE değerleriyle rekabetçi sonuçlar üretmiştir. Buna karşılık, Bayesian Ridge modeli -0.04 R² skoru ile yetersiz bir performans göstermiştir, yani model veri setindeki ilişkileri başarılı bir şekilde öğrenememiştir. Elastic Net modeli ise RMSE = 33,064 ve R² = 0.52 ile orta seviyede bir başarı sergilemiştir. Bu sonuçlar, elektrikli araç sayısı ile elektrik tüketimi arasında güçlü ancak doğrusal olmayan bir ilişki olduğunu göstermektedir. Özellikle KNN gibi parametrik olmayan modellerin en iyi performansı göstermesi, elektrikli araçların enerji tüketimine olan etkisinin karmaşık ve doğrudan doğrusal olmayan bir yapı sergilediğini kanıtlamaktadır. Bu sonuç, gelecekte elektrikli araç sayısının artmasıyla elektrik talebinde de orantılı bir yükseliş olacağını ve enerji altyapısının bu doğrultuda planlanması gerektiğini göstermektedir. Özellikle şehirlerarası şarj istasyonlarının artırılması, yenilenebilir enerji kaynaklarının entegrasyonu ve akıllı şebeke yönetimi gibi stratejiler, bu büyüyen talebe uyum sağlamak için kritik öneme sahip olacaktır.

Keywords

Elektrikli Araçlar , Makine Öğrenmesi , Şehir Enerji Talep Planlaması

Electricity Demand Forecasting Based on Electric Vehicle Growth: A Comparative Analysis of Gradient Boosting, KNN, and Extra Trees Models

Abstract

In this study, the impact of the number of electric vehicles on annual electricity consumption was examined using machine learning methods. Using up-to-date data, Gradient Boosting, K-Nearest Neighbors (KNN), Extra Trees, Bayesian Ridge, and Elastic Net models were applied for electricity consumption forecasting. The models were evaluated using metrics such as MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and R² (Coefficient of Determination). According to the results obtained, the most successful model was the KNN Regressor. The KNN model achieved the highest accuracy with MAE = 17.186, RMSE = 21.003, and R² = 0.81. Gradient Boosting and Extra Trees models also produced competitive results, with RMSE values of 24.283 and 22.965, respectively. In contrast, the Bayesian Ridge model exhibited poor performance with an R² score of -0.04, indicating that it failed to successfully learn the relationships in the dataset. The Elastic Net model demonstrated moderate success with RMSE = 33.064 and R² = 0.52. These results indicate a strong but nonlinear relationship between the number of electric vehicles and electricity consumption. The fact that non-parametric models such as KNN performed best proves that the impact of electric vehicles on energy consumption follows a complex and inherently nonlinear pattern. This finding suggests that as the number of electric vehicles increases in the future, electricity demand will rise proportionally, necessitating appropriate planning of the energy infrastructure. Strategies such as expanding intercity charging stations, integrating renewable energy sources, and implementing smart grid management will be critical in adapting to this growing demand.

Keywords

Electric Vehicles , Machine Learning , Urban Energy Demand Planning

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