A Machine Learning Based Hybrid Clustering and Prediction Approach to Energy Efficiency in Buildings

Yıl 2025, Cilt: 15 Sayı: 4, 1399 - 1425, 15.12.2025

Aslıhan Sağıroğlu , Alev Taşkın

https://doi.org/10.31466/kfbd.1533972

Öz

Efficiency in energy usage has become increasingly important in today's World due to the rising costs and the need to prevent environmental pollution. As buildings are responsible for a major portion of energy consumption, energy efficiency in buildings has gained significant attention, which has led to the creation of energy classifications, including the concepts as green buildings. These classifications are based on two types of energy consumptions: heating load and cooling load, which are more significant than lighting and ventilation. This study utilized machine learning methods to predict energy usage in buildings by analyzing heating and cooling loads. Prior cluster analysis was performed to enhance the prediction performance. The study employed SPSS and Python programs for the analysis.

Anahtar Kelimeler

Clustering , Prediction , Energy efficiency , Machine learning

Binalarda Enerji Verimliliğine Makine Öğrenmesi Temelli Hibrit Bir Kümeleme ve Tahminleme Yaklaşımı

Öz

Günümüzde enerji kullanımında verimlilik hem maliyetlerin yükselmesi hem de çevresel kirliliğin önlenmesi açısından çok önemli bir konumda bulunmaktadır. Enerji kullanımının önemli bir kısmı binalarda gerçekleştiği için binalarda enerji verimliliği ayrıca bir çalışma konusu olmuş, yeşil bina gibi kavramlarla birlikte binalar için enerji sınıfları oluşturulmuştur. Bu enerji sınıfları oluşturulurken dikkate alınan enerji tüketim türlerinden iki çeşidi; ısıtma yükü ve soğutma yüküdür. Binalarda aydınlanma ve havalandırma amaçlı enerji tüketimine kıyasla, ısıtma ve soğutma yükleri enerji tüketimi açısından daha fazla göze çarpmaktadır. Bu çalışmada ısıtma ve soğutma yükleri üzerinden binalarda enerji kullanım tahminleri makine öğrenmesi yöntemleriyle gerçekleştirilmiş, öncesinde yapılan kümeleme analizi ile tahminleme performansının iyileştirilmesi sağlanmaya çalışılmıştır. Analizler sırasında SPSS programı ve Python programlama dili kullanılmıştır.

Anahtar Kelimeler

Kümeleme , Tahminleme , Enerji verimliliği , Makine öğrenmesi

Kaynakça

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