PV Santrallerinde DC Dizi Elektriksel Ölçümlerinden AC Gücün Yüksek Doğrulukla Yeniden Yapılandırılması için Fizik Tabanlı Derin Artık Öğrenme

Abstract

DC tarafındaki elektriksel ölçümlerden fotovoltaik santralin AC aktif gücünü doğru bir şekilde yeniden oluşturmak, dijital ikiz kalibrasyonu, sensör arıza toleransı ve SCADA doğrulaması açısından faydalıdır. Bu çalışma, toplu dizi DC gücüne dayalı fiziksel temelli bir DC-AC tahminini, invertör kusurları, çalışma rejimleri ve çevresel koşullara atfedilebilen sistematik sapmaları öğrenen hafif bir derin kalıntı modeli ile birleştirerek, invertörün AC gücünü yeniden oluşturan fizik odaklı bir derin kalıntı öğrenme çerçevesi önermektedir. Model, 6778 örnek içeren ve 01.05.2024 ile 31.07.2025 tarihlerini kapsayan saatlik veri setinden DC dizi ölçümlerini, iç sıcaklığı ve meteorolojik değişkenleri kullanır. Gündüz performansı sMAPE'nin %8,10'u kadar çok daha iyi olan önerilen yeniden yapılandırma, sırasıyla 0,1907 kW, 0,2704 kW, %34,73 ve 0,9999 değerlerinde MAE, RMSE, sMAPE ve R2 değerlerine ulaşmaktadır. İnvertör sıcaklığına karşı kalıntı tanıları, çalışma aralıkları genelinde düşük sapma gösterir ve invertörlerdeki bilinen termal güç düşürme etkileriyle tutarlı olarak yüksek sıcaklıklarda öngörülemeyen hata artışını ortaya çıkarır. Sonuçlar, fizik odaklı kalıntı öğrenmenin, operasyonel izleme ve dijital ikiz uygulamaları için uygun olan DC dizisi ölçümlerinden yüksek doğrulukta AC güç rekonstrüksiyonu sağlayabileceğini göstermektedir.

Keywords

• PV invertör
• AC güç tahmini
• Artık öğrenme
• Dijital ikiz
• SCADA

Physics-Guided Deep Residual Learning for High-Accuracy Reconstruction of PV Plant AC Power from DC String Electrical Measurements

Abstract

Reconstructing photovoltaic plant AC active power accurately from DC-side electrical measurements is useful for digital twin calibration, sensor failure tolerance, and SCADA validation. By combining a physically motivated DC to AC estimate based on aggregated array DC power with a lightweight deep residual model that learns systematic deviations attributable to inverter non-idealities, operating regimes, and environmental conditions, this work proposes a physics-guided deep residual learning framework that reconstructs inverter AC power. The model makes use of DC string measurements, internal temperature, and meteorological variables from an hourly dataset covering 2024/05/01 to 2025/07/31 with 6778 samples. With much better daytime performance as 8.10% of sMAPE, the suggested reconstruction achieves MAE, RMSE, sMAPE, and R2 as 0.1907 kW, 0.2704 kW, 34.73%, and 0.9999, respectively. Residual diagnostics versus inverter temperature demonstrate low bias across operating ranges and reveal unpredictable error growth at high temperatures, consistent with known thermal derating effects in inverters. The results show that physics-guided residual learning can provide high accuracy AC power reconstruction from DC string measurements suitable for operational monitoring and digital twin applications.

Keywords

• PV inverter
• AC power estimation
• Physics-guided ML
• Residual learning
• Digital twin
• SCADA

References

1. [1] G. Masson, A. Jäger-Waldau, I. Kaizuka, J. Lindahl, J. Donoso, M. De L’Epine, A Snapshot of the Global PV Market and Industry, Conference Record of the IEEE Photovoltaic Specialists Conference (2025) 508–510. https://doi.org/10.1109/PVSC59419.2025.11132892.
2. [2] N.M. Haegel, S.R. Kurtz, Global Progress Toward Renewable Electricity: Tracking the Role of Solar (Version 4); Global Progress Toward Renewable Electricity: Tracking the Role of Solar (Version 4), IEEE J. Photovolt. 15 (2024). https://doi.org/10.1109/JPHOTOV.2024.3450020.
3. [3] A. Jäger-Waldau, Snapshot of Photovoltaics – February 2024, EPJ PHOTOVOLTAICS 15 (2024). https://doi.org/10.1051/EPJPV/2024018.
4. [4] S. Maity, Z.H. Rather, S. Doolla, Maximum Available Power Estimation in Solar Photovoltaic Power Plants Using Reference Inverters: A Critical Assessment, IEEE PES Innovative Smart Grid Technologies Conference Europe (2023). https://doi.org/10.1109/ISGTEUROPE56780.2023.10408609.
5. [5] P. Pachaivannan, S. Manimuthu, V. Jegadeesan, Comparative energy performance analysis of solar water pumping systems across diverse climate zones, Journal of Engineering Research 13 (2025) 923–937. https://doi.org/10.1016/J.JER.2024.03.011.
6. [6] A. Bouzbiba, Y. Taleb, A. Abbou, Evaluation of the Energy Quality of a 2 MWp Photovoltaic Power Plant Integrated into the Distribution Network: Revision of the EN50160 Standard on Total Harmonic Distortion and Voltage Variations, (2025). https://doi.org/10.20944/preprints202502.1106.v1.
7. [7] F. Doğan, O. Polat, F. Yardimci, A new method for detecting beaconing attacks in IoT-based scada systems, Int. J. Inf. Secur. 24 (2025) 244-. https://doi.org/10.1007/S10207-025-01161-6/FIGURES/12.
8. [8] R. Romano, A. Lampasi, S. Pipolo, F. Do, ˘ Gan, S. Oyucu, D. Betul Unsal, A. Aksöz, M. Vafaeipour, Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning, Applied Sciences 2025, Vol. 15, Page 336 15 (2025) 336. https://doi.org/10.3390/APP15010336.

9. [9] L. de O. Santos, T. AlSkaif, G.C. Barroso, P.C.M. de Carvalho, Photovoltaic power estimation and forecast models integrating physics and machine learning: A review on hybrid techniques, Solar Energy 284 (2024) 113044. https://doi.org/10.1016/J.SOLENER.2024.113044.
10. [10] S. Maity, Z.H. Rather, S. Doolla, Estimation of Maximum Available Power in Solar Photovoltaic Power Plants under Normal Operation and Cloud Shading, Proceedings of the 11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022 (2022) 165–169. https://doi.org/10.1109/ISGTASIA54193.2022.10003623.
11. [11] W. Gu, B. Li, C. Tong, Estimating the Typical Curve of Photovoltaic Generation Based on a Single Reference Station, 2025 IEEE 3rd International Conference on Power Science and Technology, ICPST 2025 (2025) 1407–1412. https://doi.org/10.1109/ICPST65050.2025.11089265.
12. [12] Manju, R. Kumar, S. Rajwade, P.K. Yadaw, A. ingle, M.K. Sahu, The Advancements in Modeling, Estimation Techniques, and Fault Analysis in Solar Photovoltaic Systems: A Comprehensive Review, Journal of Technology Innovations and Energy 3 (2024) 23–48. https://doi.org/10.56556/JTIE.V3I2.917.
13. [13] M. Aktaş, F. Doğan, İ. Türkoğlu, Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models, Sakarya University Journal of Computer and Information Sciences 7 (2024) 217–226. https://doi.org/10.35377/SAUCIS...1463788.
14. [14] A. Meflah, F. Chekired, N. Drir, L. Canale, Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies, Resources 2024, Vol. 13, Page 166 13 (2024) 166. https://doi.org/10.3390/RESOURCES13120166.
15. [15] J. Shan, Y. Xu, W. Gu, Z. Zheng, R. Yu, Y. Yao, K. Liu, S. Lu, A.H. Abolmasoumi, L. Mili, Dynamic State Estimation for Photovoltaic under Variations of Solar Irradiance, IEEE Trans. Industr. Inform. 21 (2025) 7609–7620. https://doi.org/10.1109/TII.2025.3576852.
16. [16] E. Chodakowska, J. Nazarko, Ł. Nazarko, H.S. Rabayah, Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends, Energies 2024, Vol. 17, Page 3156 17 (2024) 3156. https://doi.org/10.3390/EN17133156.
17. [17] B. Espinar, J.-L. Aznarte, R. Girard, A. Mbairadjim Moussa, G. Kariniotakis, G. Kariniotakis Photovoltaic, B. Espinar, J. Aznarte, R. Girard, A. Moussa, G. Kariniotakis, Photovoltaic Forecasting: A state of the art, (n.d.). https://minesparis-psl.hal.science/hal-00771465v1 (accessed May 24, 2026).
18. [18] W. Liu, J. Cui, Y. Guo, Y. Wang, L. Pei, Y. Yang, B. Zhang, Summary of the Power Prediction Studies of Photovoltaic Systems, 2025 International Conference of Clean Energy and Electrical Engineering, ICCEEE 2025 (2025). https://doi.org/10.1109/ICCEEE63357.2025.11156521.
19. [19] M. Jasiński, Z. Leonowicz, J. Jasiński, L. Martirano, R. Gono, PV Advancements & Challenges: Forecasting Techniques, Real Applications, and Grid Integration for a Sustainable Energy Future, Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023 (2023). https://doi.org/10.1109/EEEIC/ICPSEUROPE57605.2023.10194796.
20. [20] O. Trull, J.C. García-Díaz, A. Peiró-Signes, A Comparative Study of Statistical and Machine Learning Methods for Solar Irradiance Forecasting Using the Folsom PLC Dataset, Energies 2025, Vol. 18, Page 4122 18 (2025) 4122. https://doi.org/10.3390/EN18154122.
21. [21] A.I. Salamanis, G. Xanthopoulou, N. Bezas, C. Timplalexis, A.D. Bintoudi, L. Zyglakis, A.C. Tsolakis, D. Ioannidis, D. Kehagias, D. Tzovaras, Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting, Energies 2020, Vol. 13, Page 5978 13 (2020) 5978. https://doi.org/10.3390/EN13225978.
22. [22] A. Niccolai, A. Dolara, E. Ogliari, Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches, Energies 2021, Vol. 14, Page 451 14 (2021) 451. https://doi.org/10.3390/EN14020451.
23. [23] M.J. Mayer, Benefits of physical and machine learning hybridization for photovoltaic power forecasting, Renewable and Sustainable Energy Reviews 168 (2022) 112772. https://doi.org/10.1016/J.RSER.2022.112772.
24. [24] C.L. Huang, Y.K. Wu, Y.Y. Li, Deterministic and Probabilistic Solar Power Forecasts: A Review on Forecasting Models, 2021 7th International Conference on Applied System Innovation, ICASI 2021 (2021) 15–18. https://doi.org/10.1109/ICASI52993.2021.9568482.
25. [25] R. Ulbricht, M. Hahmann, H. Donker, W. Lehner, Systematical Evaluation of Solar Energy Supply Forecasts, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8817 (2014) 108–121. https://doi.org/10.1007/978-3-319-13290-7_9.
26. [26] C.K. Rao, S.K. Sahoo, F.F. Yanine, Forecasting Electric Power Generation in a Photovoltaic Power Systems for Smart Energy Management, 2022 International Conference on Intelligent Controller and Computing for Smart Power, ICICCSP 2022 (2022). https://doi.org/10.1109/ICICCSP53532.2022.9862396.
27. [27] R. Herbazi, Z. Ben Seddik, K. Amechnoue, A. Khouya, I. Hadj Baraka, M. Diani, A. Chahboun, Performance evaluation and experimental validation of different empirical models for predicting photovoltaic output power, International Journal of Ambient Energy 43 (2022) 7437–7453. https://doi.org/10.1080/01430750.2022.2068069.
28. [28] P. Di Leo, A. Ciocia, G. Malgaroli, F. Spertino, Advancements and Challenges in Photovoltaic Power Forecasting: A Comprehensive Review, Energies 2025, Vol. 18, Page 2108 18 (2025) 2108. https://doi.org/10.3390/EN18082108.
29. [29] O.E. Hassan, A.K. Abdelsalam, New Time Horizon Based Classification of PV Power Generation Forecasting Techniques, 30th International Conference on Computer Theory and Applications, ICCTA 2020 - Proceedings (2020) 88–95. https://doi.org/10.1109/ICCTA52020.2020.9477679.
30. [30] C. Liu, M. Li, Y. Yu, Z. Wu, H. Gong, F. Cheng, A Review of Multitemporal and Multispatial Scales Photovoltaic Forecasting Methods, IEEE Access 10 (2022) 35073–35093. https://doi.org/10.1109/ACCESS.2022.3162206.
31. [31] O.A. Odejobi, K.O. Alawode, M.O. Lawal, EFFICIENT METHOD FOR FORECASTING SOLAR IRRADIANCE - A REVIEW, FUDMA JOURNAL OF SCIENCES 8 (2024) 285–298. https://doi.org/10.33003/FJS-2024-0806-2786.
32. [32] M.J. Mayer, Benefits of physical and machine learning hybridization for photovoltaic power forecasting, Renewable and Sustainable Energy Reviews 168 (2022) 112772. https://doi.org/10.1016/J.RSER.2022.112772.
33. [33] T.N. Nguyen, F. Müsgens, What drives the accuracy of PV output forecasts?, Appl. Energy 323 (2022) 119603. https://doi.org/10.1016/J.APENERGY.2022.119603.
34. [34] Y. Huang, J. Lu, C. Liu, X. Xu, W. Wang, X. Zhou, Comparative study of power forecasting methods for PV stations, 2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010 (2010). https://doi.org/10.1109/POWERCON.2010.5666688.
35. [35] F. Jafari, J. Moerschell, K. Riesen, Comparison of PV Power Prediction Techniques: From Regression to LSTM Models, 2025 International Conference on Control, Automation and Diagnosis, ICCAD 2025 (2025). https://doi.org/10.1109/ICCAD64771.2025.11099459.
36. [36] Y. Kamarianakis, Y. Pantazis, E. Kalligiannaki, T.D. Katsaounis, K. Kotsovos, I. Gereige, M. Abdullah, A. Jamal, A. Tzavaras, Robust day-ahead solar forecasting with endogenous data and sliding windows, Journal of Renewable and Sustainable Energy 16 (2024). https://doi.org/10.1063/5.0190493/3282993.
37. [37] J.F. Duran, L.I. Minchala, A Comparative Study on Time Series Prediction of Photovoltaic-Power Production Through Classic Statistical Techniques and Short-Term Memory Networks, 9th 2023 International Conference on Control, Decision and Information Technologies, CoDIT 2023 (2023) 1512–1517. https://doi.org/10.1109/CODIT58514.2023.10284303.
38. [38] T.C. Carneiro, P.C.M. De Carvalho, H.A. Dos Santos, M.A.F.B. Lima, A.P. De Souza Braga, Review on Photovoltaic Power and Solar Resource Forecasting: Current Status and Trends, Journal of Solar Energy Engineering, Transactions of the ASME 144 (2022). https://doi.org/10.1115/1.4051652/1114259.
39. [39] J. Yan, Z. Huang, C. Hu, W. Cao, Overview of Photovoltaic Power Forecasting Methods, Proceedings of 2024 IEEE 7th International Electrical and Energy Conference, CIEEC 2024 (2024) 4846–4852. https://doi.org/10.1109/CIEEC60922.2024.10583510.
40. [40] N. Al Masood, M.I. Asif, A.M. Alam, S.R. Deeba, T. Aziz, Forecasting of Photovoltaic Power Generation: Techniques and Key Factors, Proceedings of 2019 IEEE Region 10 Symposium, TENSYMP 2019 (2019) 457–461. https://doi.org/10.1109/TENSYMP46218.2019.8971337.
41. [41] I. Gallardo, D. Amor, Á. Gutiérrez, Recent Trends in Real-Time Photovoltaic Prediction Systems, Energies 2023, Vol. 16, Page 5693 16 (2023) 5693. https://doi.org/10.3390/EN16155693.
42. [42] M. Elsaraiti, A. Merabet, Solar Power Forecasting Using Deep Learning Techniques, IEEE Access 10 (2022) 31692–31698. https://doi.org/10.1109/ACCESS.2022.3160484.
43. [43] R. Asghar, F.R. Fulginei, M. Quercio, A. Mahrouch, Artificial Neural Networks for Photovoltaic Power Forecasting: A Review of Five Promising Models, IEEE Access 12 (2024) 90461–90485. https://doi.org/10.1109/ACCESS.2024.3420693.
44. [44] P. Li, K. Zhou, X. Lu, S. Yang, A hybrid deep learning model for short-term PV power forecasting, Appl. Energy 259 (2020) 114216. https://doi.org/10.1016/J.APENERGY.2019.114216.
45. [45] A. Mellit, A.M. Pavan, V. Lughi, Deep learning neural networks for short-term photovoltaic power forecasting, Renew. Energy 172 (2021) 276–288. https://doi.org/10.1016/j.renene.2021.02.166.
46. [46] R. Asghar, F.R. Fulginei, M. Quercio, L. Sabino, F. Crescimbini, M. Abusara, A Hybrid LSTM-CNN Model for Short-Term Photovoltaic Power Forecasting in Italy, Digest of Technical Papers - IEEE International Conference on Consumer Electronics (2024) 67–73. https://doi.org/10.1109/ISCT62336.2024.10791191.
47. [47] H. Elmousalami, F.K. Peng Hui, A.A. Alnaser, Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF), Buildings 2025, Vol. 15, Page 2785 15 (2025) 2785. https://doi.org/10.3390/BUILDINGS15152785.
48. [48] A. Ahsan, A. Zafar, M.A. Afzal, M. Javed, D. Zafar, M. Ali, Improved Solar Power Prediction Using CNN-LSTM Models for Optimized Smart Grid Performance, Journal of Engineering, Science and Technological Trends 1 (2024). https://doi.org/10.48112/JESTT.V1I2C.5.
49. [49] S.C. Lim, J.H. Huh, S.H. Hong, C.Y. Park, J.C. Kim, Solar Power Forecasting Using CNN-LSTM Hybrid Model, Energies 2022, Vol. 15, Page 8233 15 (2022) 8233. https://doi.org/10.3390/EN15218233.
50. [50] A.B. Owolabi, A. Yahaya, A.O. Yakub, N.N. Same, M. Amir, M.A. Adeshina, D. Suh, Hybrid Deep Learning Models for Power Output Forecasting of Grid-Connected Solar PV Systems: A Monocrystalline and Polycrystalline PV Panel Analysis, Int. J. Energy Res. 2025 (2025) 9925615. https://doi.org/10.1155/ER/9925615.
51. [51] R. Raman, B. Mewada, R. Meenakshi, G.M. Jayaseelan, K.S. Sharmila, S.N. Taqui, E.A. Al-Ammar, S.M. Wabaidur, A. Iqbal, Forecasting the PV Power Utilizing a Combined Convolutional Neural Network and Long Short-Term Memory Model, Electric Power Components and Systems 52 (2024) 233–249. https://doi.org/10.1080/15325008.2023.2217193.
52. [52] K. Shivashankaraiah, G. Shashibhushan, A. Prema Kirubakaran, C. Anjanappa, P.K. Sharma, I. Mayiladuthurai Vaidyanathan, S.N. Taqui, S. Al Obaid, S. Alfarraj, Developing a Deep Learning and Reliable Optimization Techniques for Solar Photovoltaic Power Prediction, Electric Power Components and Systems (2024). https://doi.org/10.1080/15325008.2024.2317369.
53. [53] W. Liu, Z. Mao, Short-term photovoltaic power forecasting with feature extraction and attention mechanisms, Renew. Energy 226 (2024) 120437. https://doi.org/10.1016/J.RENENE.2024.120437.
54. [54] J. Li, R. Wang, G. Zhang, Z. Zhang, Z. Yang, B. Fu, L. Zhang, H. Ran, Photovoltaic Power Prediction Model Based on Temporal Attention, 2024 4th International Conference on Energy Engineering and Power Systems, EEPS 2024 (2024) 444–448. https://doi.org/10.1109/EEPS63402.2024.10804403.
55. [55] D. Pujic, N. Tomasevic, Hybrid ensemble neural network approach for photovoltaic production forecast, 2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings (2021). https://doi.org/10.1109/TELFOR52709.2021.9653369.
56. [56] L. Liu, K. Guo, J. Chen, L. Guo, C. Ke, J. Liang, D. He, A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model, Electronics 2023, Vol. 12, Page 2764 12 (2023) 2764. https://doi.org/10.3390/ELECTRONICS12132764.
57. [57] D. Salman, C. Direkoglu, M. Kusaf, M. Fahrioglu, Hybrid deep learning models for time series forecasting of solar power, Neural Comput. Appl. 36 (2024) 9095–9112. https://doi.org/10.1007/S00521-024-09558-5/FIGURES/12.
58. [58] D. Kothona, K. Spyropoulos, C. Valelis, C. Sarigiannidis, K.C. Chatzisavvas, G.C. Christoforidis, Efficient 24-hour ahead PV energy production forecasting employing a transformer-based model, SyNERGY MED 2022 - 2nd International Conference on Energy Transition in the Mediterranean Area, Proceedings (2022). https://doi.org/10.1109/SYNERGYMED55767.2022.9941461.
59. [59] T.S. Amreen, R. Panigrahi, N.R. Patne, Solar Power Forecasting Using Deep Learning Approach, (2023) 83–93. https://doi.org/10.1007/978-981-99-4175-9_8.
60. [60] A. Ait Mansour, A. Tilioua, M. Touzani, Bi-LSTM, GRU and 1D-CNN models for short-term photovoltaic panel efficiency forecasting case amorphous silicon grid-connected PV system, Results in Engineering 21 (2024) 101886. https://doi.org/10.1016/J.RINENG.2024.101886.
61. [61] R. Aman, M. Rizwan, A. Kumar, Novel and EEcient Hybrid Deep Learning Approach for Solar Photovoltaic Power Forecasting Considering Meteorological Parameters, (2023). https://doi.org/10.21203/rs.3.rs-3260351/v1.
62. [62] W.C. Kuo, C.H. Chen, S.H. Hua, C.C. Wang, Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System, Applied Sciences 2022, Vol. 12, Page 7529 12 (2022) 7529. https://doi.org/10.3390/APP12157529.
63. [63] E. Chodakowska, J. Nazarko, Ł. Nazarko, H.S. Rabayah, Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends, Energies 2024, Vol. 17, Page 3156 17 (2024) 3156. https://doi.org/10.3390/EN17133156.
64. [64] Y.K. Wu, C.L. Huang, Q.T. Phan, Y.Y. Li, Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints, Energies 2022, Vol. 15, Page 3320 15 (2022) 3320. https://doi.org/10.3390/EN15093320.
65. [65] S.N.A. Kazmi, F. Yang, M.J. Sanjari, Enhancing Photovoltaic Power Forecasting with Physics-Informed Neural Networks for Sustainable Energy Management, 2025 IEEE Region 10 Symposium: Innovations in Technology: Shaping the Future, TENSYMP 2025 (2025). https://doi.org/10.1109/TENSYMP63728.2025.11144998.
66. [66] A.L. Pico, M.A. Ríos, L. Felipe Giraldo -, R. Hassanian, N. Yeganeh, Á. Helgadóttir, al -, F. Khadoum Alhousni, H. Abdullah Alhinai, H. Xia, F. Gao, W. Wang, B. Liu, H. Zhang, D. Yang, Conversion of Solar Irradiance to Photovoltaic Power with Hybrid Model Chains, J. Phys. Conf. Ser. 2774 (2024) 012031. https://doi.org/10.1088/1742-6596/2774/1/012031.
67. [67] Y. Yao, Y. Wang, H. Jia, Y. Mu, An analytical approach based on coupled multi-physics model for photovoltaic arrays performance simulation, Electric Power Systems Research 224 (2023) 109773. https://doi.org/10.1016/J.EPSR.2023.109773.
68. [68] F.E. Ait Salah, N. Maouhoub, K. Tifidat, Efficient Analytical Models for Accurate Peak Power Forecasting of Photovoltaic Panels under Real-World Conditions, 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024 (2024). https://doi.org/10.1109/IRASET60544.2024.10548484.
69. [69] D.V. Pombo, H.W. Bindner, S.V. Spataru, P.E. Sørensen, P. Bacher, Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning, Sensors 2022, Vol. 22, Page 749 22 (2022) 749. https://doi.org/10.3390/S22030749.
70. [70] S. Shah, A.R. Boozula, R.G. Lampuse, J.J. Thakkar, A Systematic Framework for Reconciling Solar Energy Production Models with Operational Data: A Literature Synthesis and Methodology Development, European Journal of Energy Research 5 (2025) 7–23. https://doi.org/10.24018/EJENERGY.2025.5.4.168.
71. [71] S. Abousaid, L. Benabbou, H. Dagdougui, I. Belhaj, H. Bouzekri, A. Berrado, PV Power Forecasting Using Deep Learning and Physical Models: Case Study of Morocco, Advances in Science, Technology and Innovation (2024) 59–67. https://doi.org/10.1007/978-3-031-49772-8_8/SAVE-RESEARCH.
72. [72] P. Gupta, R. Singh, Indirect Forecasting of Hourly PV Power Generation Based on a Hybrid Model Combining Data Analysis and Machine Learning Technique, Lecture Notes in Civil Engineering 391 (2024) 193–200. https://doi.org/10.1007/978-981-99-6616-5_21/SAVE-RESEARCH.
73. [73] J. Thaker, R. Höller, Hybrid model for intra-day probabilistic PV power forecast, Renew. Energy 232 (2024) 121057. https://doi.org/10.1016/J.RENENE.2024.121057.
74. [74] F.E. Ait Salah, N. Maouhoub, K. Tifidat, Efficient Analytical Models for Accurate Peak Power Forecasting of Photovoltaic Panels under Real-World Conditions, 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024 (2024). https://doi.org/10.1109/IRASET60544.2024.10548484.
75. [75] D.V. Pombo, H.W. Bindner, S.V. Spataru, P.E. Sørensen, P. Bacher, Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning, Sensors 2022, Vol. 22, Page 749 22 (2022) 749. https://doi.org/10.3390/S22030749.
76. [76] S. Shah, A.R. Boozula, R.G. Lampuse, J.J. Thakkar, A Systematic Framework for Reconciling Solar Energy Production Models with Operational Data: A Literature Synthesis and Methodology Development, European Journal of Energy Research 5 (2025) 7–23. https://doi.org/10.24018/EJENERGY.2025.5.4.168.
77. [77] O. Idbouhouch, N. Rabbah, N. Lamrini, H. Oufettoul, I.A. Abdelmoula, M. Zegrari, Assessing PV inverter efficiency degradation under semi-arid conditions: A case study in Morocco, Heliyon 10 (2024) e36906. https://doi.org/10.1016/j.heliyon.2024.e36906.
78. [78] L. Wang, B.F. Brown, A. Siraj, A.K. Das, S. Jin, Z. Zhang, PV Inverter Reliability Assessment: Methodology and Case Study, Conference Record of the IEEE Photovoltaic Specialists Conference (2025) 1490–1495. https://doi.org/10.1109/PVSC59419.2025.11133245.
79. [79] H. Gao, S. Qiu, J. Fang, N. Ma, J. Wang, K. Cheng, H. Wang, Y. Zhu, D. Hu, H. Liu, J. Wang, Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM, Sustainability 2023, Vol. 15, Page 8266 15 (2023) 8266. https://doi.org/10.3390/SU15108266.
80. [80] S.A. Yahya, M.A. Dah, B. Bilal, M. Boukhnifer, Accurate Solar Energy Prediction based on the Meteorological and Operational Parameters, 2025 International Conference on Control, Automation and Diagnosis, ICCAD 2025 (2025). https://doi.org/10.1109/ICCAD64771.2025.11099438.
81. [81] H. Mou, Y. Chen, X. Jiang, T. Liu, J. Zhang, Photovoltaic output power prediction based on thermoelectric coupling digital twin model of inverter, Journal of Computational Methods in Sciences and Engineering (2025). https://doi.org/10.1177/14727978251366546.
82. [82] G. Figueiredo, F.I.M. Mares, A.A. Manito, P.F. Torres, W.N. MacÊdo, Mathematical Modeling and Computational Tool That Consider the Thermal Behavior of Inverters in the Forecast of Energy Yield in Grid-Connected Photovoltaic Systems, Journal of Solar Energy Engineering, Transactions of the ASME 143 (2021). https://doi.org/10.1115/1.4050061/1097161.
83. [83] A. Desai, T. Joshi, I. Mukhopadhyay, A. Ray, Effect of Temperature on Conversion Efficiency of Single-Phase Solar PV Inverter, Conference Record of the IEEE Photovoltaic Specialists Conference (2021) 2376–2381. https://doi.org/10.1109/PVSC43889.2021.9518415.
84. [84] K. Chumpolrat, V. Sangsuwan, N. Udomdachanut, S. Kittisontirak, S. Songtrai, P. Chinnavornrungsee, A. Limmanee, J. Sritharathikhun, K. Sriprapha, Effect of Ambient Temperature on Performance of Grid-Connected Inverter Installed in Thailand, International Journal of Photoenergy 2014 (2014) 502628. https://doi.org/10.1155/2014/502628.
85. [85] A.L. Perin, C.W.M. Prieb, A. Krenzinger, MODELAGEM TÉRMICA DE UM INVERSOR FOTOVOLTAICO CONECTADO À REDE, Anais Congresso Brasileiro de Energia Solar - CBENS (2016) 1–8. https://doi.org/10.59627/CBENS.2016.1978.
86. [86] S.C. Lim, B.G. Kim, J.C. Kim, Analysis of Inverter Efficiency Using Photovoltaic Power Generation Element Parameters, Sensors 2024, Vol. 24, Page 6390 24 (2024) 6390. https://doi.org/10.3390/S24196390.
87. [87] G.H. Park, J.C. Kim, Multivariate Variable-Based LSTM-AE Model for Solar Power Prediction., Int. J. Adv. Sci. Eng. Inf. Technol. 15 (2025) 293. https://doi.org/10.18517/IJASEIT.15.1.20944.
88. [88] P.M. Rodrigo, R. Velázquez, E.F. Fernández, DC/AC conversion efficiency of grid-connected photovoltaic inverters in central Mexico, Solar Energy 139 (2016) 650–665. https://doi.org/10.1016/J.SOLENER.2016.10.042.