Yığılmış Topluluk Makine Öğrenmesi Modeli ile Enerji Tüketim Tahmini

Year 2025, Volume: 3 Issue: 2, 1 - 14, 31.08.2025

Mahamoud Abdi Abdillahi , Taha Etem

https://doi.org/10.70988/ajeas.1694578

Abstract

Hızlı kentleşme, aşırı enerji tüketimi ve nüfus artışı sonucunda son yıllarda enerji ihtiyacı önemli ölçüde artmıştır. Bu durum iklim değişikliği, su ve hava kirliliği gibi çevresel sorunlara yol açmaktadır. Enerji tüketimini tahmin etmek bu sorunları azaltabilir ve enerji yönetimine ve etkinliğine yardımcı olabilir. Bu makalede, Fas'taki Tetouan şehrinde enerji tüketimini tahmin etmek için doğrusal regresyon, K-En Yakın Komşu, destek vektör regresörü, rastgele orman, gradyan artırma ve istifleme gibi çeşitli makine öğrenimi yöntemlerinin performansını araştırılmıştır. Bu modellerin performansını değerlendirmek için MAE, RMSE ve R2 gibi değerlendirme ölçütleri kullanılmıştır. Yığınlama yöntemi, 1., 2. ve 3. bölgelerde sırasıyla %98,13, %98,11 ve %99,05 doğrulukla olağanüstü performans ve en iyi sonucu sağlamaktadır.

Keywords

Makine Öğrenmesi , Topluluk Modeli , Enerji Tüketimi , Tahmin

Stacked Ensemble Machine Learning Model of Energy Consumption Prediction

Abstract

The need for energy has significantly increased in recent decades as a result of rapid urbanization, excessive energy consumption and population growth. This leads to environmental problems such as climate change, water and air pollution. Predicting energy consumption can reduce these problems and helps energy management and efficacity. In this paper, we investigate the performance of several machine learning methods, such as linear regression, K-Nearest neighbor, support vector regressor, random forest, gradient boosting, and stacking to predict energy consumption in Tetouan city, in Morocco. To evaluate the performance of these models, evaluation metrics such as MAE, RMSE, and R2 were used. Stacking method provided outstanding performance and the best result with accuracy of 98.13%, 98.11% and 99.05% in zone 1, 2, 3, respectively.

Keywords

Machine Learning , Ensemble Model , Energy Consumption , Prediction

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