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Journals / Zeki Sistemler Teori ve Uygulamaları Dergisi / 2026 / Cilt: 9 Sayı: 2026

Deep Learning–Driven Solar Radiation Estimation Using Meteorological Time-Series Data

Meteorolojik Zaman Serisi Verileri Kullanılarak Derin Öğrenme Tabanlı Güneş Işınımı Tahmini

Çağatay Teke(ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ)0000-0002-6975-8544
Pages
1–9
Publication Date
2026-10-12
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AI Summary & Key Takeaways

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

The results showed that the MAPE value was 1.3% and the MSE value was 0.026.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Today, renewable energy sources are becoming increasingly important due to the continuous increase in energy demand and the limited availability of fossil fuel resources.
📊
Key Findings & Data:Accurate estimation of solar radiation plays a critical role for efficient design, operation and management of solar energy systems.
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Conclusion & Impact:The results showed that the MAPE value was 1.3% and the MSE value was 0.026.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Güneş ışınımı · derin öğrenme · yapay sinir ağı

Abstract

Today, renewable energy sources are becoming increasingly important due to the continuous increase in energy demand and the limited availability of fossil fuel resources. Solar energy is becoming popular in many countries as a clean and sustainable energy source. Accurate estimation of solar radiation plays a critical role for efficient design, operation and management of solar energy systems. In this study, solar radiation estimation is performed using meteorological data of Gümüşhane province. In the estimation process, Multi Layer Perceptron (MLP) artificial neural network method, which has a multilayer structure from deep learning approaches, was used. In the MLP model, 5 inputs, a hidden layer with 14 neurons and 1 output layer are structured. Error criteria such as Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE) were used to evaluate the performance of the model. The results showed that the MAPE value was 1.3% and the MSE value was 0.026. In addition, regression analysis was performed to examine the relationship between model outputs and actual values and the coefficient of determination R2 was calculated. The results of the study are in line with the results reported in similar studies in the literature and reveal that the developed MLP model provides high accuracy in solar radiation prediction.

Özet

Günümüzde, enerji talebindeki sürekli artış ve fosil yakıt kaynaklarının sınırlı olması nedeniyle yenilenebilir enerji kaynakları giderek daha fazla önem kazanmaktadır. Güneş enerjisi, temiz ve sürdürülebilir bir enerji kaynağı olarak birçok ülkede yaygınlaşmaktadır. Güneş ışınımının doğru bir şekilde tahmin edilmesi, güneş enerjisi sistemlerinin verimli tasarımı, işletilmesi ve yönetimi açısından kritik bir rol oynamaktadır. Bu çalışmada, Gümüşhane ili meteorolojik verileri kullanılarak güneş ışınımı tahmini gerçekleştirilmiştir. Tahmin sürecinde, derin öğrenme yaklaşımlarından çok katmanlı bir yapıya sahip Çok Katmanlı Algılayıcı (MLP) yapay sinir ağı yöntemi kullanılmıştır. MLP modelinde 5 giriş, 14 nörondan oluşan bir gizli katman ve 1 çıkış katmanı yapılandırılmıştır. Modelin performansını değerlendirmek amacıyla Ortalama Mutlak Yüzde Hata (MAPE) ve Ortalama Kare Hata (MSE) gibi hata ölçütleri kullanılmıştır. Sonuçlar, MAPE değerinin %1,3 ve MSE değerinin 0,026 olduğunu göstermiştir. Ayrıca, model çıktıları ile gerçek değerler arasındaki ilişkiyi incelemek amacıyla regresyon analizi yapılmış ve belirlilik katsayısı R2 hesaplanmıştır. Çalışmanın sonuçları, literatürdeki benzer çalışmalarda bildirilen sonuçlarla uyumludur ve geliştirilen MLP modelinin güneş ışınımı tahmininde yüksek doğruluk sağladığını ortaya koymaktadır.

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Cite This Article

Teke, Ç. (2026). Deep Learning–Driven Solar Radiation Estimation Using Meteorological Time-Series Data. Zeki Sistemler Teori ve Uygulamaları Dergisi, Cilt: 9 Sayı: 2026, 1–9. https://doi.org/10.38016/jista.2027778

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