Gizem AKKÖSE, Ayça DURAN, İpek GÜRSEL DİNO, Çağla MERAL AKGÜL

Makina öğrenmesi ile pencere parametrelerinin bina performansına etkisinin iklim değişikliği gözetilerek incelenmesi

İklim ve yapılı çevre, güçlü ve dinamik bir ilişki içindedir. Bu ilişki, iklim değişikliği krizi ile son yıllarda daha da önem kazanmıştır. Bu bağlamda, binaların çevresel yüklerinin azaltılması ve bina kullanıcılarının ısıl konforunun ve sağlığının korunması daha da kritik bir hale gelmiştir. İklim değişikliği araştırmalarında eğitim binaları yüksek iç yükleri, kendine özgü bina kullanım profilleri ve ana kullanıcılarının öğrenciler olması sebebi ile diğer bina tipolojilerinden ayrılır. Ayrıca, öğrenciler yaşları, vücutları ve metabolizmalarındaki farklılıklar sebebi ile ısıl konfor ve iç ortam hava kalitesine karşı daha hassastır. Bu sebepler ile, eğitim binalarında iklim değişikliği çerçevesinde performans iyileştirmesi gerekli hale gelmektedir. Enerji kaybını azaltmak ve ısıl konfor dengesini sağlamak için en etkili yöntemlerden biri, pencerelerin parametrelerini optimize etmektir. Bu çalışma, iklim değişikliğinin eğitim binası enerji ve ısıl performansı üzerindeki etkilerini ve pencere performansına dayalı pasif iyileştirme senaryolarının etkinliğini makine öğrenmesi ve istatistiksel analizler ile incelemektedir. Araştırma bina simülasyonlarına dayalı, dört aşamalı bir yaklaşıma dayanmaktadır ve sırasıyla (i) iklim değişikliği senaryosu ile modifiye edilmiş iklim veri setlerinin oluşturulması ve analizi, (ii) mevcut bina üzerinde iklim değişikliği etki analizi, (iii) iyileştirme senaryolarının karşılaştırmalı analizi ve (iv) makine öğrenmesine dayalı tahmin modelleri analizi adımlarını takip eder. Seçilen performans göstergelerinin (bina enerji tüketimi ve kullanıcı ısıl konforu) değerlendirilmesi için Ankara'daki mevcut bir ortaokul binası örnek vaka olarak seçilmiştir. Farklı pencere parametreleriyle, olası 2025 farklı iyileştirme senaryosu parametrik olarak modellenmiştir. Performans simülasyonları sonucunda üretilen tüm veri betimsel istatistik yöntemleriyle incelendikten sonra, verinin bir alt kümesi ile Rastgele Orman (RO) tahmin modelleri eğitilmiştir. Her bir performans göstergesi için farklı pencere parametrelerinin önemi, 10 kat çapraz doğrulama yöntemiyle RO modelleri öznitelik önemleri hesaplanarak sıralanmıştır. RO modelleriyle yapılan performans tahminleri gerçek değerlerinden sadece ortalama %2 sapmakta ve yüksek tahmin kapasitesi göstermektedir. Öznitelik önem değerleri inceliğinde pencere SHGC değerinin test edilen değişkenler arasında performansa dayalı iyileştirme senaryolarının en önemli parametresi olduğu gözlemlenmiştir. Ayrıca güçlendirme senaryoları ile toplam enerji tüketimi %50'ye varan azalma gösterirken, iç mekan ısıl konforunda önemli bir iyileşme gözlemlenmektedir. Bu çalışmanın sonuçları, mevcut eğitim binalarında maksimum etki için cam performans kriterlerinin ve en etkili kombinasyon seçiminin önemini vurgulamaktadır. Sonuçlar, binaların iklim değişikliğine adaptasyonu süreçlerinde makine öğrenmesinin etkin bir şekilde kullanılabileceğini göstermektedir. Çalışmada kullanılan yöntem farklı bina parametrelerini ve bina teknolojilerini kapsayacak şekilde genişletilebilir.

Anahtar Kelimeler:

Makine öğrenmesi, İklim değişikliği, Bina enerji verimliliği, Retrofit, Isıl konfor, Öznitelik önemi

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