{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:16:50Z","timestamp":1760059010684,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Baoding Science and Technology Plan Project","award":["2394Z001"],"award-info":[{"award-number":["2394Z001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>This research introduces a novel hybrid machine learning framework for automated quality prediction and classification of silicon solar modules in production lines. Unlike conventional approaches that rely solely on encapsulation loss rate (ELR) for performance evaluation\u2014a method limited to assessing encapsulation-related power loss\u2014our framework integrates unsupervised clustering and supervised classification to achieve a comprehensive analysis. By leveraging six critical performance parameters (open circuit voltage (VOC), short circuit current (ISC), maximum output power (Pmax), voltage at maximum power point (VPM), current at maximum power point (IPM), and fill factor (FF)), we first employ k-means clustering to dynamically categorize modules into three performance classes: excellent performance (ELR: 0\u20130.77%), good performance (0.77\u20138.39%), and poor performance (&gt;8.39%). This multidimensional clustering approach overcomes the narrow focus of traditional ELR-based methods by incorporating photoelectric conversion efficiency and electrical characteristics. Subsequently, five machine learning classifiers\u2014decision trees (DT), random forest (RF), k-nearest neighbors (KNN), naive Bayes classifier (NBC), and support vector machines (SVMs)\u2014are trained to classify modules, achieving 98.90% accuracy with RF demonstrating superior robustness. Pearson correlation analysis further identifies VOC, Pmax, and VPM as the most influential quality determinants, exhibiting strong negative correlations with ELR (\u22120.953, \u22120.993, \u22120.959). The proposed framework not only automates module quality assessment but also enhances production line efficiency by enabling real-time anomaly detection and yield optimization. This work represents a significant advancement in solar module evaluation, bridging the gap between data-driven automation and holistic performance analysis in photovoltaic manufacturing.<\/jats:p>","DOI":"10.3390\/computation13050125","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T11:50:31Z","timestamp":1747741831000},"page":"125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Machine Learning-Driven Automated Quality Prediction and Classification of Silicon Solar Modules in Production Lines"],"prefix":"10.3390","volume":"13","author":[{"given":"Yuxiang","family":"Liu","sequence":"first","affiliation":[{"name":"College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinzhong","family":"Xia","sequence":"additional","affiliation":[{"name":"Yingli Energy (China) Co., Ltd., Baoding 071051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[{"name":"Yingli Energy (China) Co., Ltd., Baoding 071051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Yu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Photovoltaic Materials and Cells, Yingli Energy Development Co., Ltd., Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengmeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Yingli Energy (China) Co., Ltd., Baoding 071051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinchao","family":"Shi","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Photovoltaic Materials and Cells, Yingli Energy Development Co., Ltd., Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Ma","sequence":"additional","affiliation":[{"name":"Yingli Energy (China) Co., Ltd., Baoding 071051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"Yingli Energy (China) Co., Ltd., Baoding 071051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boyang","family":"Hu","sequence":"additional","affiliation":[{"name":"Yingli Energy (China) Co., Ltd., Baoding 071051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinying","family":"Wang","sequence":"additional","affiliation":[{"name":"Yingli Energy (China) Co., Ltd., Baoding 071051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3739-734X","authenticated-orcid":false,"given":"Bing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"021001","DOI":"10.1088\/2399-1984\/aa7d7c","article-title":"Sunlight-thin nanophotonic monocrystalline silicon solar cells","volume":"1","author":"Depauw","year":"2017","journal-title":"Nano Futures"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.3938\/jkps.61.1279","article-title":"Fabrication and characterization of monocrystalline-like silicon solar cells","volume":"61","author":"Han","year":"2012","journal-title":"J. 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