Deneysel Çalışmaya Dayalı Fotovoltaik/Termal Sistemin Yapay Sinir Ağı ile Modellenmesi

Year 2023, Issue: 52, 153 - 160, 15.12.2023

Muhammed Musab Bayat , Ertan Buyruk

Abstract

Bu çalışmada soğutmasız ve soğutmalı fotovoltaik panellerin sıcaklığa bağlı akım, gerilim ve çıkış gücü karakteristiklerini modellemek için Yapay Sinir Ağı modeli (YSA) kullanılmıştır. Bir önceki laboratuvar deneyinde fotovoltaik panellerin 20 ˚C- 65 ˚C sıcaklık aralığında bir saat boyunca ürettikleri akım ve gerilim değerleri ölçülmüştür. Soğutmasız ve iki farklı soğutmalı model olmak üzere bu üç PV/T'nin her biri için 60 örnek içeren deneysel verilerle Yapay Sinir Ağı tekniği kullanılarak modeller oluşturulmuştur. Yapay Sinir Ağı modelinin en düşük model hatasını sağlayan kombinasyonları ve özellikleri belirlenmiştir. Sinir Ağı modelinin performansı sırasıyla 1.15e-02, 6.76e-03 ve 6.10e-03 RMSE model hatalarıyla hem soğutmasız fotovoltaik, düz kanatçıklar/FDM ile soğutulan hem de delikli kanatçıklar/FDM ile soğutulan fotovoltaikte iyi performans gösterdi. Bu nedenle, bir saatlik deney sırasında ulaşılan tüm sıcaklıklarda akım, gerilim ve üretilen gücü modellemek için güçlü bir araç olarak önerildi.

Keywords

Yapay sinir ağı, Fotovoltaik/Termal Sistem, PV-FDM

Supporting Institution

Sivas Cumhuriyet Üniversitesi Bilimsel Araştırma Projeleri (CUBAP)

Project Number

M-2022 829

Modeling of Photovoltaic/Thermal System by Artificial Neural Network Based on The Experimental Study

Abstract

In this study, Artificial Neural Network model (ANN) has been used to model the temperature dependent current, voltage and output power characteristics of uncooled and cooled photovoltaic panels. In the previous laboratory experiment, the current and voltage values produced by the photovoltaic panels in the temperature range of 20 ˚C- 65 ˚C for one hour were measured. Models have been created using the Artificial Neural Network technique with experimental data containing 60 samples for each of these three PV/T, including uncooled and two different cooled models. The combinations and features of the Artificial Neural Network model that provide the lowest model error have been achieved. The performance of the Neural Network model performed well in both the uncooled photovoltaic, cooled with flat fins/PCM and cooled with perforated fins/PCM, with RMSE model errors of 1.15e-02, 6.76e-03 and 6.10e-03, respectively. Therefore, it was suggested as a potent tool for modeling current, voltage, and generated power at all temperatures reached during the hour-long experiment.

Keywords

Artificial neural network, Photovoltaic/Thermal System, PV-PCM

Project Number

M-2022 829

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