Comparison of the Analytical Model and the Data-Driven Long Short-Term Memory Model for the Digital Twin of a Photovoltaic Panel

Yıl 2025, Cilt: 13 Sayı: 4, 1806 - 1819, 31.12.2025

Halil Ertaş , Uğur Fesli , Şevki Demirbaş , Kemal Çelik

https://doi.org/10.29109/gujsc.1752900

https://izlik.org/JA95LD93XN

Öz

The importance of digital twin methods in system monitoring, energy management, fault detection, and assessment of system health is increasing day by day. Due to environmental pollution and global warming, photovoltaic (PV) panels have become an essential part of energy systems because of the widespread adoption of renewable energy systems. Factors such as variability in weather conditions and non-linearity of PV systems necessitate close monitoring. Digital twin methods emerge as a novel solution to this problem. Digital twin modeling methods are classified in the literature as analytical methods and neural methods. Analytical methods provide rapid results and straightforward approaches; however, the complexity and non-linearity of systems complicate the modeling of PV systems. Although neural methods achieve success in system-specific results, they pose challenges such as the necessity of individual training for each system, difficulty in obtaining sufficient training data, and complexities involved in data processing. In this study, a performance comparison was conducted between the neural digital twin and the analytical digital twin for PV systems. Data-driven Long Short-Term Memory (LSTM) was used as the neural method, whereas the Single-Diode Model (TDM-SDM) was utilized as the analytical method. Experimental studies indicated that the TDM-based PV digital twin method achieved a performance with a coefficient of determination R² greater than 0.95.

Anahtar Kelimeler

Digital twin , Long Short-Term Memory (LSTM) , Photovoltaic systems , Analytical modeling , Single-diode model

Fotovoltaik Panel Dijital İkizi için Analitik Model ile Veri Odaklı Uzun Kısa Süreli Bellek Modelin Karşılaştırılması

https://izlik.org/JA95LD93XN

Öz

Sistem izleme, enerji yönetimi, arıza tespiti ve sistem sağlığının değerlendirilmesi durumlarında dijital ikiz yöntemlerinin önemi her geçen gün artmaktadır. Çevre kirliliği ve küresel ısınma ile yenilenebilir enerji sistemlerinin yaygınlaşmasından dolayı fotovoltaik (FV) paneller, enerji sistemlerinin vazgeçilmez bir parçası haline gelmiştir. Hava şartlarının değişkenliği ve FV’lerin doğrusal olmaması gibi sebepler, FV sistemlerinin sıkı takip edilmesi zorunluluğunu doğurmaktadır. Dijital ikiz yöntemleri bu soruna yeni bir çözüm olarak ortaya çıkmaktadır. Dijital ikiz modelleme yöntemleri literatürde analitik yöntemler ve veri odaklı yöntemler olarak yer almaktadır. Analitik yöntemler, hızlı sonuç üretme ve doğrudan yaklaşımlar sunsa da sistem karmaşıklığı ve sistemlerin doğrusal olmaması, FV sistemlerinin modellenmesini zorlaştırmaktadır. Veri odaklı yöntemler, sistemlere özel sonuçlarda başarı sağlamasına karşın, her farklı sistem için ayrı eğitim gerekliliği, eğitim için yeterli veri elde etme ve verilerin işlenme zorluğu gibi sebeplerden dolayı sorunlar oluşturmaktadır. Bu çalışmada FV sistemleri için veri odaklı dijital ikiz ile analitik dijital ikiz arasında performans karşılaştırması yapılmıştır. Veri odaklı yöntem için uzun-kısa süreli bellek (LSTM), analitik yöntem için tek diyot modeli (SDM) kullanılmıştır. Yapılan deneysel çalışmalarda SDM tabanlı FV dijital ikiz yönteminin performansının R2=0,95’in üzerinde olduğu görülmüştür.

Anahtar Kelimeler

Dijital İkiz , Uzun Kısa Süreli Bellek , Fotovoltaik Sistem , Analitik Model , Tek diyot Model

Kaynakça

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