[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124996-en":3,"doc-seo-124996-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124996,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Analyzing the Impact of Design Factors on Solar Module Thermomechanical Durability Using Interpretable Machine Learning Techniques","Solar modules in utility-scale systems are expected to last for decades, yet cyclic thermomechanical loading can progressively reduce long-term performance. This study addresses the difficulty of separating which design components drive durability in a complex module composition by analyzing a dataset linking bill-of-materials (BOM) and thermal cycling power loss from 251 module designs. Random forest modeling combined with SHAP identifies predominant factors such as silicon type, encapsulant thickness, busbar count, and wafer thickness, guiding interpretability-driven design decisions.","arXiv:2402.11911v2 [[physics. app-ph](physics. app-ph)] 12 May 2024  \nAnalyzing the Impact of Design Factors on Solar Module Thermomechanical Durability Using Interpretable Machine Learning Techniques  \nXin Chen ∗†, Todd Karin ‡, Anubhav Jain ∗  \n∗Lawrence Berkeley National Laboratory, Berkeley, CA, U.S.A  \n†University of California, Berkeley, Berkeley, CA, U.S.A  \n‡Kiwa PVEL, Member of Kiwa Group, Napa, CA, U.S.A  \nAbstract  \nSolar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model’s decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.  \nKeywords  \nPV module, thermomechanical durability, bill of materials, interpretable machine learning  \n\n| Nomenclature\u003Cbr>PV Photovoltaic\u003Cbr>BOM Bill of materials\u003Cbr>TC Thermal cycling\u003Cbr>CTE Coefficient of thermal expansion PQP Product qualification program Encaps Encapsulant\u003Cbr>Glass2 Rear glass of glass-glass modules Mono-c Monocrystalline\u003Cbr>Poly-c Polycrystalline\u003Cbr>EVA Ethylene vinyl acetate | POE\u003Cbr>TPT\u003Cbr>ML\u003Cbr>KNN\u003Cbr>SVR\u003Cbr>RF\u003Cbr>RMSE\u003Cbr>SHAP\u003Cbr>ϕ\u003Cbr>CI\u003Cbr>ANOVA | Polyolefin elastomer Tedlar Polyester Tedlar Machine learning\u003Cbr>K-nearest neighbors Support vector regression Random forest\u003Cbr>Root mean square error SHapley Additive exPlanation Shapley value\u003Cbr>Confidence interval Analysis of variance |\n| --- | --- | --- |\n\nI. INTRODUCTION  \nUtility-scale photovoltaic (PV) systems are expected to achieve an extended operating lifetime to be competitive with conventional energy sources [1] . However, solar modules installed in the field are subject to multiple environmental stresses  \nThe project was primarily funded and intellectually led as part of the Durable Modules Consortium (DuraMAT), an Energy Materials Network Consortium funded under Agreement 32509 by the U.S. Department of Energy (DOE), Office of Energy Efficiency & Renewable Energy, Solar Energy Technologies Office (EERE, SETO) . Lawrence Berkeley National Laboratory is funded by the DOE under award DE-AC02-05CH11231 .  \nThe authors declare no conflicts of interest. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. government. Instruments and materials are identified in this paper to describe the experiments. In no case does such identification imply recommendatio","cbCaijLfuqRVr5Pf","https://ap.wps.com/l/cbCaijLfuqRVr5Pf","pdf",9258023,1,27,"English","en",105,"# Introduction\n## Thermomechanical degradation in utility-scale PV modules\n## Module structure and key materials\n# Abstract and Methods (Random Forest + SHAP)\n## Dataset and target variable: thermal cycling power loss\n## Design factors identified and interpretations","[{\"question\":\"Why is thermomechanical durability critical for utility-scale solar modules?\",\"answer\":\"Utility-scale PV modules must sustain long service lifetimes, but cyclic thermal and mechanical stresses can degrade internal components and reduce power output over time.\"},{\"question\":\"What data does the study use to relate design factors to durability?\",\"answer\":\"It analyzes a comprehensive dataset combining bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs.\"},{\"question\":\"Which design factors most strongly influence thermomechanical degradation according to the interpretability analysis?\",\"answer\":\"The main factors include silicon type (monocrystalline vs. polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness, with silicon type and encapsulant behavior showing notable effects.\"}]","Analyzing the Impact of Design Factors on Solar Module Thermomechanical Durability Using Interpretable Machine Learning Techniques | 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is thermomechanical durability critical for utility-scale solar modules?","Question",{"text":75,"@type":76},"Utility-scale PV modules must sustain long service lifetimes, but cyclic thermal and mechanical stresses can degrade internal components and reduce power output over time.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data does the study use to relate design factors to durability?",{"text":80,"@type":76},"It analyzes a comprehensive dataset combining bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs.",{"name":82,"@type":73,"acceptedAnswer":83},"Which design factors most strongly influence thermomechanical degradation according to the interpretability analysis?",{"text":84,"@type":76},"The main factors include silicon type (monocrystalline vs. polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness, with silicon type and encapsulant behavior showing notable 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