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Journals / Fırat Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 38 Sayı: 1

Machine Learning-Based Prediction of Key PVT Parameters Using Tree-Based and Classical Regression Models

Ağaç Tabanlı ve Klasik Regresyon Modelleri Kullanılarak Temel PVT Parametrelerinin Makine Öğrenmesine Dayalı Tahmini

Kamil Neyfel Çerçi(TARSUS ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0002-3126-707X
Sümeyye Büşra Durmaz(TARSUS ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0009-0008-1199-2750
Çağrı Kaymak(FIRAT ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0001-5343-226X
Ebru Akpınar(FIRAT ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0003-0666-9189
Pages
313–325
Publication Date
2026-03-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

All models achieved high predictive accuracy, with XGBoost providing the best performance (R2=0.9998, RMSE=0.16, MAE=0.12 for Ts; R2=0.9997, RMSE=0.19, MAE=0.12 for To).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With the continuous growth of the global population, energy demand is rapidly increasing, and this demand is still mainly supplied by fossil fuels with limited reserves.
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Key Findings & Data:Photovoltaic-thermal (PVT) panels, capable of simultaneously generating heat and electricity, have attracted significant research interest for applications such as space conditioning, heating, cooling, and drying.
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Conclusion & Impact:All models achieved high predictive accuracy, with XGBoost providing the best performance (R2=0.9998, RMSE=0.16, MAE=0.12 for Ts; R2=0.9997, RMSE=0.19, MAE=0.12 for To).
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Fotovoltaik-termal (PVT) kolektörler · rastgele orman · hafif gradyan güçlendirme makinesi

Abstract

With the continuous growth of the global population, energy demand is rapidly increasing, and this demand is still mainly supplied by fossil fuels with limited reserves. However, the environmental impacts of fossil fuels pose major challenges to sustainability. Therefore, reducing energy consumption and promoting renewable energy sources are of great importance. Among these, solar energy has gained prominence as an inexhaustible and environmentally friendly source. Photovoltaic-thermal (PVT) panels, capable of simultaneously generating heat and electricity, have attracted significant research interest for applications such as space conditioning, heating, cooling, and drying. To enable effective analysis of PVT systems, accurate modeling is essential. In this study, two main output parameters of PVT collectors, surface temperature (Ts) and fluid outlet temperature (To), were modeled, and the performance of the models was evaluated using regression performance metrics. A dataset generated through finite element analysis in COMSOL Multiphysics was used to develop machine learning models including Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost) in Python. Additionally, a linear regression model served as a baseline. All models achieved high predictive accuracy, with XGBoost providing the best performance (R2=0.9998, RMSE=0.16, MAE=0.12 for Ts; R2=0.9997, RMSE=0.19, MAE=0.12 for To).

Özet

Küresel nüfusun sürekli artmasıyla birlikte enerji talebi hızla artmaktadır ve bu talep hala sınırlı rezervlere sahip fosil yakıtlarla karşılanmaktadır. Ancak fosil yakıtların çevresel etkileri sürdürülebilirlik açısından büyük zorluklar yaratmaktadır. Bu nedenle, enerji tüketimini azaltmak ve yenilenebilir enerji kaynaklarını teşvik etmek büyük önem taşımaktadır. Bunlar arasında güneş enerjisi, tükenmez ve çevre dostu bir kaynak olarak öne çıkmıştır. Aynı anda ısı ve elektrik üretebilen fotovoltaik-termal (PVT) paneller, iklimlendirme, ısıtma, soğutma ve kurutma gibi uygulamalar için önemli bir araştırma konusu olmuştur. PVT sistemlerinin etkili bir şekilde analiz edilebilmesi için doğru modelleme çok önemlidir. Bu çalışmada, PVT kolektörlerinin iki temel çıktı parametresi olan yüzey sıcaklığı (Ts) ve sıvı çıkış sıcaklığı (To) modellenmiş ve modellerin başarımı regresyon performans ölçütleri üzerinden değerlendirilmiştir. COMSOL Multiphysics’te sonlu elemanlar analizi ile oluşturulan bir veri seti, Python’da Karar Ağacı (KA), Rastgele Orman (RO), Hafif Gradyan Güçlendirme Makinesi (HGGM) ve Aşırı Gradyan Güçlendirme (AGG) gibi makine öğrenmesi modellerini geliştirmek için kullanılmıştır. Ek olarak, doğrusal regresyon modeli bir temel olarak kullanılmıştır. Tüm modeller yüksek tahmin doğruluğu elde etmiş olup, AGG en iyi performansı göstermiştir (Ts için R2=0.9998, RMSE=0.16, MAE=0.12; To için R2=0.9997, RMSE=0.19, MAE=0.12).

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Cite This Article

Çerçi, K. N., Durmaz, S. B., Kaymak, Ç., Akpınar, E. (2026). Machine Learning-Based Prediction of Key PVT Parameters Using Tree-Based and Classical Regression Models. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 38 Sayı: 1, 313–325. https://doi.org/10.35234/fumbd.1811382

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