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Journals / Türk Doğa ve Fen Dergisi / 2026 / Cilt: 15 Sayı: 3

Simulation and Estimating of Monthly Energy Production of Solar Power Plant Using PVsyst Program and Advanced Deep Learning Algorithms

PVsyst Programı ve Gelişmiş Derin Öğrenme Algoritmaları Kullanılarak Güneş Enerjisi Santralinin Aylık Enerji Üretiminin Simülasyonu ve Tahmini

Mehmet Çınar(BİTLİS EREN ÜNİVERSİTESİ)0000-0002-1542-9120
İrfan Ökten(BİTLİS EREN ÜNİVERSİTESİ, MÜHENDİSLİK-MİMARLIK FAKÜLTESİ)0000-0001-9898-7859
Pages
78–88
Publication Date
2026-09-30
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AI Summary & Key Takeaways

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

The study provides high-accuracy prediction to improve the integration of solar energy into power systems and reduce energy costs

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Solar energy production forecasting is crucial for optimally integrating renewable energy sources into power systems.
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Key Findings & Data:In this study; Modeling, simulation and estimation of solar energy that can be produced next year of the solar power plant with a total installed power of 1300 kW, established in Bitlis province in the Eastern Anatolia Region of Turkey, are shown.
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Conclusion & Impact:The study provides high-accuracy prediction to improve the integration of solar energy into power systems and reduce energy costs
🔬Design:Review & Systematic Survey
📍Scope:2024 Verileri
⚙️Variables:Yenilenebilir enerji · güneş enerjisi santrali · derin öğrenme algoritmaları

Abstract

Solar energy production forecasting is crucial for optimally integrating renewable energy sources into power systems. Deep learning techniques have emerged as promising alternatives for solar energy forecasting in recent years. In this study; Modeling, simulation and estimation of solar energy that can be produced next year of the solar power plant with a total installed power of 1300 kW, established in Bitlis province in the Eastern Anatolia Region of Turkey, are shown. PVsyst software program was used to analyze the performance ratio and different losses occurring in the system. Additionally, the study reviews deep learning techniques for solar energy forecasting with a special focus on Long Short Term Memory (LSTM) networks for short-term solar energy production forecasting. As a result of this examination, R2 score, MAE (Mean Absolute Error), and MSE (Mean Squared Error) were obtained as 0.863, 0.284 and 0.125, respectively. Estimated energy production in 2024 was calculated from the network trained with the LSTM model. Actual results were interpreted by comparing the results of the PVsyst program and the artificial intelligence algorithm. The study provides high-accuracy prediction to improve the integration of solar energy into power systems and reduce energy costs

Özet

Güneş enerjisi üretiminin tahmin edilmesi, yenilenebilir enerji kaynaklarının elektrik sistemlerine en uygun şekilde entegre edilmesi için çok önemlidir. Derin öğrenme teknikleri, son yıllarda güneş enerjisi tahmini için umut vadeden alternatifler olarak ortaya çıkmıştır. Bu çalışmada; Türkiye'nin Doğu Anadolu Bölgesi'ndeki Bitlis ilinde kurulan, toplam kurulu gücü 1300 kW olan güneş enerjisi santralinin gelecek yıl üretebileceği güneş enerjisinin modellenmesi, simülasyonu ve tahmini gösterilmiştir. Sistemde meydana gelen performans oranı ve farklı kayıpların analizi için PVsyst yazılım programı kullanılmıştır. Ayrıca, çalışma, kısa vadeli güneş enerjisi üretimi tahmini için Uzun Kısa Vadeli Bellek (LSTM) ağlarına özel bir odaklanma ile güneş enerjisi tahmini için derin öğrenme tekniklerini incelemektedir. Bu inceleme sonucunda, R2 skoru, MAE (Ortalama Mutlak Hata) ve MSE (Ortalama Kare Hata) sırasıyla 0,863, 0,284 ve 0,125 olarak elde edilmiştir. LSTM modeli ile eğitilen ağdan 2024 yılındaki tahmini enerji üretimi hesaplanmıştır. Elde edilen sonuçlar, PVsyst programının ve yapay zeka algoritmasının sonuçları karşılaştırılarak yorumlandı. Çalışma, güneş enerjisinin elektrik sistemlerine entegrasyonunu iyileştirmek ve enerji maliyetlerini düşürmek için yüksek doğrulukta tahminler sunmaktadır.

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Cite This Article

Çınar, M., Ökten, İ. (2026). Simulation and Estimating of Monthly Energy Production of Solar Power Plant Using PVsyst Program and Advanced Deep Learning Algorithms. Türk Doğa ve Fen Dergisi, Cilt: 15 Sayı: 3, 78–88. https://doi.org/10.46810/tdfd.1914730

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