Journals / Gazi University Journal of Science / 2026 / Volume: 39 Issue: 3

Conditioning Of Photovoltaic Power Losses Through Deviation And Residual Analysis

Sapma ve Artık Analizi Yoluyla Fotovoltaik Güç Kayıplarının Koşullandırılması

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Publication Date
2026-09-01
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AI Summary & Key Takeaways

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

The results indicate a cumulative loss of 54,663 Wh, corresponding to 26.62% of the total theoretical energy production.

📌 3 Key Takeaways

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Objective & Hypothesis:The performance evaluation of the photovoltaic system is conducted by combining a theoretical power-estimation approach with environmental observations.
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Key Findings & Data:These residuals are generally concentrated around zero.
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Conclusion & Impact:The results indicate a cumulative loss of 54,663 Wh, corresponding to 26.62% of the total theoretical energy production.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Photovoltaic systems · Theoretical power model · Performance deviation

Abstract

The performance evaluation of the photovoltaic system is conducted by combining a theoretical power-estimation approach with environmental observations. The dataset includes irradiance, temperature, air density, and cloud cover, enabling examination of the system's operating conditions in relation to atmospheric variability. A reference power signal is obtained from a formulation dependent on temperature and irradiance intensity, and this signal is used as a reference for comparison with the measured output. From this reference point, residuals describing instantaneous deviations from expected behavior can be derived. These residuals are generally concentrated around zero. However, they are interrupted by distinct power drops at more infrequent intervals. These intervals are not randomly distributed over time. They coincide during periods when intense solar radiation coincides with high temperatures. This indicates that thermal effects and short-term radiation fluctuations jointly shape the observed response. To examine these effects, loss maps were created for light intensity-temperature and cloud cover-light intensity pairs. Ultimately, the most significant losses occurred when module temperatures increased alongside high radiation and under partially cloudy conditions. The study combined theoretical comparison, residue-based evaluation, and loss mapping. As a result, a new framework was presented to interpret PV performance deviations and understand how atmospheric variability leads to reductions in energy efficiency. The results indicate a cumulative loss of 54,663 Wh, corresponding to 26.62% of the total theoretical energy production. Residual magnitudes increased significantly under high irradiance conditions, often exceeding 200 W, highlighting the dominant role of combined thermal and atmospheric effects in shaping performance deviations.

Özet

Fotovoltaik sistemin performans değerlendirmesi, teorik bir güç tahmin yaklaşımı ile saatlik çözünürlükteki çevresel gözlemlerin birlikte kullanılmasıyla gerçekleştirilmiştir. Veri seti; ışınım, ortam sıcaklığı, hava yoğunluğu ve bulutluluk bilgilerini içermekte olup, sistemin atmosferik değişkenlik bağlamındaki çalışma koşullarının incelenmesine olanak tanımaktadır. Sıcaklık ve ışınım şiddetine bağlı bir formülasyon aracılığıyla bir referans güç sinyali elde edilmiş ve bu sinyal, ölçülen güç çıktısının karşılaştırılması için temel alınmıştır. Bu referans üzerinden, beklenen davranıştan anlık sapmaları tanımlayan artıklar türetilmiştir. Söz konusu artıklar genel olarak küçük değerlere sahiptir ve sıfır etrafında yoğunlaşmaktadır. Bununla birlikte, bu yapı zaman zaman belirgin güç düşüşleriyle kesintiye uğramaktadır. Bu düşüşlerin zaman içinde rastgele dağılmadığı, yüksek güneş ışınımının yüksek sıcaklıklarla birlikte görüldüğü dönemlerde ortaya çıktığı belirlenmiştir. Bu durum, ısıl etkiler ile kısa süreli ışınım dalgalanmalarının birlikte sistem tepkisini şekillendirdiğini göstermektedir. Bu etkilerin daha ayrıntılı incelenebilmesi amacıyla, ışınım–sıcaklık ve bulutluluk–ışınım çiftleri için kayıp haritaları oluşturulmuştur. Sonuçlar, en belirgin güç kayıplarının modül sıcaklığının yüksek ışınım koşulları altında arttığı ve parçalı bulutluluk durumlarında meydana geldiğini ortaya koymuştur. Çalışmada teorik karşılaştırma, artık tabanlı değerlendirme ve kayıp haritalama yaklaşımları birlikte kullanılmıştır. Bu çerçevede, fotovoltaik sistem performansındaki sapmaların yorumlanmasına ve atmosferik değişkenliğin enerji verimliliğinde meydana gelen azalmaları nasıl tetiklediğinin anlaşılmasına yönelik yeni bir değerlendirme çerçevesi sunulmuştur.

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Cite This Article

Yüksek, G., *, G. Y. (2026). Conditioning Of Photovoltaic Power Losses Through Deviation And Residual Analysis. Gazi University Journal of Science, Volume: 39 Issue: 3. https://doi.org/10.35378/gujs.1844176

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References (23)

  1. Nguyen, V.G., Sirohi, R., Tran, M.H., Truong, T.H., Duong, M.T., Pham, M.T., and Cao, D.N. “Renewable energy role in low-carbon economy and net-zero goal: Perspectives and prospects”, Energy & Environment. 36, 2248–2287 (2025). DOI: https://doi.org/10.1177/0958305X241253772
  2. Yüksek, G., Mete, A.N., “A P&O Based Variable Step Size MPPT Algorithm for Photovoltaic Applications”, Gazi University Journal of Science. 36, 608–622 (2023). DOI: https://doi.org/10.35378/gujs.1050325
  3. Yuksek, G., and Mete, A.N., “A hybrid variable step size MPPT method based on P&O and INC methods”, In: 2017 10th International Conference on Electrical and Electronics Engineering (ELECO 2017), (2018).
  4. Araújo, N.M.F.T.S., Medeiros, S.E.L., and Abrahão, R., "Variability and Sensitivity of Models Used to Estimate Photovoltaic Production", Energies (Basel), 17:4177, (2024). DOI: https://doi.org/10.3390/en17164177
  5. Alshawaf, M., Poudineh, R., and Alhajeri, N.S., "Solar PV in Kuwait: The effect of ambient temperature and sandstorms on output variability and uncertainty", Renewable and Sustainable Energy Reviews, 134:110346, (2020). DOI: https://doi.org/10.1016/j.rser.2020.110346
  6. Smith, R.M., Matam, M., and Seigneur, H., "Mismatch losses in a PV system due to shortened strings", Energy Conversion and Management, 250:114891, (2021). DOI: https://doi.org/10.1016/j.enconman.2021.114891
  7. Tao, Y., Bai, J., Pachauri, R.K., Wang, Y., Li, J., and Attaher, H.K., "Parameterizing mismatch loss in bifacial photovoltaic modules with global deployment: A comprehensive study", Applied Energy, 303:117636, (2021). DOI: https://doi.org/10.1016/j.apenergy.2021.117636
  8. Brahma, B., and Wadhvani, R., "A residual ensemble learning approach for solar irradiance forecasting", Multimedia Tools and Applications, 82:33087–33109, (2023). DOI: https://doi.org/10.1007/s11042-023-14616-6
  9. Azad, A.K., Parvin, S., and Hossain, T., "Performance evaluation of nanofluid-based photovoltaic thermal (PVT) system with regression analysis", Heliyon, 10:e29252, (2024). DOI: https://doi.org/10.1016/j.heliyon.2024.e29252
  10. Acikgoz, H., Korkmaz, D., and Budak, U., "Photovoltaic cell defect classification based on integration of residual-inception network and spatial pyramid pooling in electroluminescence images", Expert Systems with Applications, 229:120546, (2023). DOI: https://doi.org/10.1016/j.eswa.2023.120546
  11. Bošnjaković, M., Stojkov, M., Katinić, M., and Lacković, I., "Effects of Extreme Weather Conditions on PV Systems", Sustainability, 15:16044, (2023). DOI: https://doi.org/10.3390/su152216044
  12. Borah, P., Micheli, L., and Sarmah, N., "Analysis of Soiling Loss in Photovoltaic Modules: A Review of the Impact of Atmospheric Parameters, Soil Properties, and Mitigation Approaches", Sustainability, 15:16669, (2023). DOI: https://doi.org/10.3390/su152416669
  13. Aghaei, M., Kolahi, M., Nedaei, A., Venkatesh, N.S., Esmailifar, S.M., Moradi Sizkouhi, A.M., Aghamohammadi, A., Oliveira, A.K.V., Eskandari, A., Parvin, P., Milimonfared, J., Sugumaran, V., and Rüther, R., "Autonomous Intelligent Monitoring of Photovoltaic Systems: An In-Depth Multidisciplinary Review", Progress in Photovoltaics: Research and Applications, 33:381–409, (2025). DOI: https://doi.org/10.1002/pip.3859
  14. Kolahi, M., Esmailifar, S.M., Moradi Sizkouhi, A.M., and Aghaei, M., "Digital-PV: A digital twin-based platform for autonomous aerial monitoring of large-scale photovoltaic power plants", Energy Conversion and Management, 321:118963, (2024). DOI: https://doi.org/10.1016/j.enconman.2024.118963
  15. Al-Dulaime, A.A., Al-Kafaji, M.S.S., and Duhis, A.H., “Solar PV monitoring techniques, state of art review”, AIP Conference Proceedings 3105 (1): 080002 (2024). DOI: https://doi.org/10.1063/5.0212838
  16. Yang, D., Wang, W., Gueymard, C.A., Hong, T., Kleissl, J., Huang, J., Perez, M.J., Perez, R., Bright, J.M., Xia, X., van der Meer, D., and Peters, I.M., "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality", Renewable and Sustainable Energy Reviews, 161:112348, (2022). DOI: https://doi.org/10.1016/j.rser.2022.112348
  17. Iheanetu, K.J., "Solar Photovoltaic Power Forecasting: A Review", Sustainability, 14:17005, (2022). DOI: https://doi.org/10.3390/su142417005
  18. Allal, Z., Noura, H.N., Salman, O., and Chahine, K., "Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions", Journal of Environmental Management, 354:120392, (2024). DOI: https://doi.org/10.1016/j.jenvman.2024.120392
  19. World Bank Group, "Solar Measurements Pakistan Lahore WB-ESMAP_QC, “https://energydata.info/dataset/pakistan-solar-radiation-measurement-data/resource/39716012-5fb1-455f-8efd-cf79db7ddf78”
  20. Gelaro, R., McCarty, W., Suárez, M.J., Todling, R., Molod, A., Takacs, L., Randles, C.A., Darmenov, A., Bosilovich, M.G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A.M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J.E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S.D., Sienkiewicz, M., and Zhao, B., "The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)", Journal of Climate, 30: 5419–5454, (2017). DOI: https://doi.org/10.1175/JCLI-D-16-0758.1
  21. Gülmez, M., Yüksek, G., and Alkaya, A., "Analysis and Comparison of Tree-Based Shallow Learning Methods for Forecasting Solar Energy Production", In: 2025 9th International Symposium on Innovative Approaches in Smart Technologies (ISAS), 1–9, IEEE, (2025). DOI: https://doi.org/10.1109/ISAS66241.2025.11101861
  22. Duffie, J.A., Beckman, W.A., and Blair, N., "Solar Engineering of Thermal Processes, Photovoltaics and Wind", Wiley, (2020). DOI: https://doi.org/10.1002/9781119540328
  23. Dobos, A., "PVWatts Version 5 Manual", Golden, CO, “https://research-hub.nlr.gov/en/publications/pvwatts-version-5-manual/” (2014).