gummfd

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

1300-1884 1304-4915

Gazi Üniversitesi

10.17341/gazimmfd.1473453

Electrical Energy Transmission, Networks and Systems Electrical Engineering (Other)

Elektrik Enerjisi Taşıma, Şebeke ve Sistemleri Elektrik Mühendisliği (Diğer)

Derin öğrenme tabanlı konut yükü tanımlama modeli ve performans analizi

https://orcid.org/0000-0002-3495-8490

Eşlik

Ardan Hüseyin

AFYON KOCATEPE ÜNİVERSİTESİ

https://orcid.org/0000-0002-5918-7266

Akarslan

Emre

AFYON KOCATEPE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK MÜHENDİSLİĞİ BÖLÜMÜ, ELEKTRİK MÜHENDİSLİĞİ PR.

https://orcid.org/0000-0003-2122-9528

Doğan

Rasim

AFYON KOCATEPE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK MÜHENDİSLİĞİ BÖLÜMÜ, ELEKTRİK MÜHENDİSLİĞİ PR.

08 21 2025

40 3 1637 1646 04 25 2024 01 06 2025

1986

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

Modern çağın hızla değişen enerji ihtiyaçlarına cevap vermek, evlerin enerji yönetimini daha da kritik hale getirmektedir. Akıllı ev teknolojilerinin yükselişiyle birlikte, konut yüklerinin etkili bir şekilde tanımlanması ve yönetilmesi giderek daha büyük bir önem kazanmaktadır. Bu çalışmada, konut yükü tanımlaması için CNN derin öğrenme tabanlı yeni bir yaklaşım önerilmiştir. Önerilen modelin etkinliği ve uygulanabilirliği, geleneksel makine öğrenimi yöntemleri olan Rastgele Orman, Karar Ağaçları ve K-En Yakın Komşu ile karşılaştırılarak değerlendirilmiştir. Afyon Kocatepe Üniversitesi laboratuvarlarında gerçekleştirilen deneysel verilerle desteklenen çalışma sonuçları, CNN derin öğrenme modelinin doğruluk, kesinlik, duyarlılık ve F-ölçütü gibi kritik metriklerde en üstün performansı sergilediğini ortaya koymuştur.

Konut Yükü Tanımlaması Derin Öğrenme Evrişimli Sinir Ağları Zaman Serisi Sınıflandırması Makine Öğrenmesi

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