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This manuscript introduces two Adaptive Neuro-Fuzzy Inference Systems developed to predict the energy output of Photovoltaic cells. These models are trained using Electroluminescence imagery of the cells for input data along their Current–Voltage curves, which offer insights output power of cells. The input characteristics of the cells are quantified based on pixel distribution and classified into three distinct categories: Black, White, and Gray values. The second model enhances this representation by incorporating an additional fuzzy categorization input, derived from a Mamdani Classifier Fuzzy Logic Model. By combining the rule-based interpretability of Fuzzy Logic with the adaptive learning capabilities of Artificial Neural Networks, the Adaptive Neuro-Fuzzy Inference System (ANFIS) emerges as an alternative to Convolutional Neural Networks (CNNs). This approach contributes to Explainable Artificial Intelligence by addressing one of the major limitations of CNNs—the lack of symbolic knowledge representation, while maintaining robust learning performance. Comparative analysis with other Machine Learning techniques demonstrates the enhanced performance provided by ANFIS models, achieving a Mean Absolute Error (MAE) of 0.053 and a Mean Squared Error (MSE) of 0.007.
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Acciani G, Falcone O, Vergura S (2010) Typical defects of PV-cells. In: 2010 IEEE international symposium on industrial electronics, pp 2745–2749
Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8(1):53
Article Google Scholar
Arrieta AB, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, Garcia S, Gil-Lopez S, Molina D, Benjamins R, Chatila R, Herrera F (2020) Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 58:82–115
Article Google Scholar
Chawla R, Singal P, Garg AK (2018) A mamdani fuzzy logic system to enhance solar cell micro-cracks image processing. 3D Res 9(3):34
Article Google Scholar
Gallardo-Saavedra S, Hernández-Callejo L, Duque-Perez O (2018) Technological review of the instrumentation used in aerial thermographic inspection of photovoltaic plants. Renew Sustain Energy Rev 93:566–579
Article Google Scholar
Hernández-Callejo L, Gallardo-Saavedra S, Alonso-Gómez V (2019) A review of photovoltaic systems: design, operation and maintenance. Sol Energy 188:426–440
Article Google Scholar
Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Article Google Scholar
Karaboga D, Kaya E (2019) Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev 52(4):2263–2293
Article Google Scholar
Khosrojerdi F, Taheri S, Cretu A (2016) An adaptive neuro-fuzzy inference system-based MPPT controller for photovoltaic arrays. In: 2016 IEEE Electrical Power and Energy Conference (EPEC), pp 1–6
Koester L, Lindig S, Louwen A, Astigarraga A, Manzolini G, Moser D (2022) Review of photovoltaic module degradation, field inspection techniques and techno-economic assessment. Renew Sustain Energy Rev 165:112616
Article Google Scholar
Köntges M, Kurtz S, Packard CE, Jahn U, Berger KA, Kato K, Friesen T, Liu H, Van Iseghem M, Wohlgemuth J et al (2014) Review of failures of photovoltaic modules. Technical report, IEA International Energy Agency
Lorente Ò, Riera I, Rana A (2021) Image classification with classic and deep learning techniques
Mateo Romero HF, Gonzalez Rebollo MA, Cardenoso-Payo V, Alonso Gomez V, Redondo Plaza A, Moyo RT, Hernandez-Callejo L (2022) Applications of artificial intelligence to photovoltaic systems: a review. Appl Sci 12(19):10056
Article Google Scholar
Mateo-Romero HF, Hernandez-Callejo L, Rebollo MAG, Cardeñoso-Payo V, Gomez VA, Bello HJ, Moyo RT, Aragones JI (2023) Synthetic dataset of electroluminescence images of photovoltaic cells by deep convolutional generative adversarial networks. Sustainability 15(9):7175
Article Google Scholar
Mateo-Romero HF, Carbonó dela Rosa ME, Hernández-Callejo L, González-Rebollo MÁ, Cardeñoso-Payo V, Alonso-Gómez V, Gallardo-Saavedra S (2024) Enhancing photovoltaic cell classification through mamdani fuzzy logic: a comparative study with machine learning approaches employing electroluminescence images. Prog Artif Intell 14:49–59
Mateo-Romero HF, Hernández Callejo L, Alonso Gómez V (2025) Dataset for the electrical and imaging-based characterization of photovoltaic cells and modules. https://doi.org/10.5281/zenodo.15719875
Mellit A, Kalogirou SA (2008) Artificial intelligence techniques for photovoltaic applications: a review. Prog Energy Combust Sci 34(5):574–632
Article Google Scholar
Mellit A, Kalogirou SA (2011) ANFIS-based modelling for photovoltaic power supply system: a case study. Renew Energy 36(1):250–258
Article Google Scholar
Morales-Aragonés JI, Gómez VA, Gallardo-Saavedra S, Redondo-Plaza A, Fernández-Martínez D, Hernández-Callejo L (2022) Low-cost three-quadrant single solar cell I–V tracer. Appl Sci 12(13):6623
Article Google Scholar
Parida B, Iniyan S, Ranko G (2011) A review of solar photovoltaic technologies. Renew Sustain Energy Rev 15(3):1625–1636
Article Google Scholar
Pillai DS, Blaabjerg F, Rajasekar N (2019) A comparative evaluation of advanced fault detection approaches for PV systems. IEEE J Photovolt 9(2):513–527
Article Google Scholar
REN21 (2023) Renewables Global Status Report. REN 21
Romero H (2024) Employing artificial intelligence techniques for the estimation of energy production in photovoltaic solar cells based on electroluminescence images, PhD thesis. Universidad de Valladolid
Romero HFM, Hernández-Callejo L, Rebollo MÁG, Cardeñoso-Payo V, Gómez VA, Aragonés JIM, Moyo RT (2023) Optimized estimator of the output power of PV cells using EL images and I-V curves. Sol Energy 265:112089
Article Google Scholar
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern SMC 15(1):116–132
Article Google Scholar
Thirugnanasambandam M, Iniyan S, Ranko G (2010) A review of solar thermal technologies. Renew Sustain Energy Rev 14(1):312–322
Article Google Scholar
Trupke T, Mitchell B, Weber JW, McMillan W, Bardos RA, Kroeze R (2012) Photoluminescence imaging for photovoltaic applications. Energy Proc 15:135–146
Article Google Scholar
Vilone G, Longo L (2021) Notions of explainability and evaluation approaches for explainable artificial intelligence. Inf Fusion 76:89–106
Article Google Scholar
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We appreciate the help of other non-funding programs such as “Convenio general de cooperación entre la Universidad de Valladolid (España) y la Corporación Universidad de la Costa (Colombia)”.
This work was supported by the following entities: “Contratos Predoctorales UVA 2020” funded by Universidad de Valladolid and Santander Bank. PID2023-148369OB-C43 (DETECCION-FV-N) by MCIU/AEI/10.13039/501100011033, FEDER, EU. ERASMUS+ KA-107 from the Universidad of Valladolid. MOVILIDAD DE DOCTORANDOS Y DOCTORANDAS UVa 2023 from the University of Valladolid.
Universidad Autónoma de Madrid, Madrid, Spain
Hector Felipe Mateo-Romero
Universidad de Valladolid, Valladolid, Spain
Luis Hernández-Callejo, Miguel Ángel González-Rebollo, Valentín Cardeñoso-Payo, Victor Alonso-Gómez, Oscar Martínez-Sacristán & Sara Gallardo-Saavedra
Universidad Nacional Abierta y a Distancia, Bogotá, Colombia
Mario Eduardo Carbonó de la Rosa
Universidad de la Costa, Barranquilla, Colombia
Adalberto José Opsino Castro
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Conceptualization, H-F.M-R and L.H-C; methodology, H-F.M-R, V.C-P, M.E.C.D and M-A.G-R; validation, H-F.M-R., V.A-G, O.M.S. and A.R-P.; writing—original draft preparation, H-F.M-R; writing—review and editing, H-F.M-R, S.G.S. L.H-C, V.C-P, A.J.E.C; project administration, L.H-C; All authors have read and agreed to the published version of the manuscript.
Correspondence to Luis Hernández-Callejo.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Mateo-Romero, H.F., Carbonó de la Rosa, M.E., Hernández-Callejo, L. et al. ANFIS-based output power estimation in photovoltaic cells using electroluminescence image features. Soft Comput 30, 3069–3086 (2026). https://doi.org/10.1007/s00500-025-11066-0
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DOI: https://doi.org/10.1007/s00500-025-11066-0
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