[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126015-en":3,"doc-seo-126015-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126015,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",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 deliver decades of lifetime, yet cyclic thermomechanical loading can degrade long-term performance. This work tackles the challenge of separating how individual bill-of-materials components affect durability. Using a dataset covering 251 module designs, random-forest modeling combined with SHAP links design factors to thermal-cycling power loss, enabling interpretable model decisions. Key drivers include silicon type, encapsulant thickness, busbar count, and wafer thickness.","Lawrence Berkeley National Laboratory  \nLBL Publications  \nTitle  \nAnalyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques  \nPermalink  \n[https://escholarship.org/uc/item/2kp131d1](https://escholarship.org/uc/item/2kp131d1)  \nAuthors  \nChen, Xin  \nKarin, Todd Jain, Anubhav  \nPublication Date  \n2025  \nDOI  \n10.1016/j.apenergy.2024.124462  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nApplied Energy 377 (2025) 124462  \n| Analyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques✩\u003Cbr>Xin Chen a,b, Todd Karin c, Anubhav Jaina,∗\u003Cbr>a Lawrence Berkeley National Laboratory, Berkeley, CA, USA b University of California, Berkeley, Berkeley, CA, USA cKiwa PVEL, Member of Kiwa Group, Napa, CA, USA |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O\u003Cbr>Dataset link: [https://datahub.duramat.org/data](https://datahub.duramat.org/data)[ ](https://datahub.duramat.org/data)[set/bom_thermal_cycling_degradation](set/bom_thermal_cycling_degradation)\u003Cbr>Keywords:\u003Cbr>PV module Thermomechanical durability Bill of materials\u003Cbr>Interpretable machine learning | A B S T R A C T\u003Cbr>Solar 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 0.7 mm 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. |  |\n\n1. 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 such as ultraviolet light, temperature variation, mechanical loading induced by snow, wind, hail [2]. These factors introduce multiple pathways of degradation, reducing the durability of the module. One of the sources of long-term degradation is the cyclic thermomechanical deformation of solar modules caused by temperature variation. Over time, ther","cbCaim6fRLar99ig","https://ap.wps.com/l/cbCaim6fRLar99ig","pdf",3289886,4,1,13,"English","en",105,"# Introduction\n## Utility-scale lifetime and environmental stresses\n## Thermomechanical degradation and need for design optimization\n## Module material structure and thermal expansion mismatch","[{\"question\":\"Why is thermomechanical durability important for utility-scale solar modules?\",\"answer\":\"Utility-scale photovoltaic systems must maintain long operating lifetime, but cyclic thermomechanical deformation caused by temperature variation degrades module components and reduces power output.\"},{\"question\":\"How does the study determine which design factors matter most?\",\"answer\":\"It analyzes a dataset of 251 module designs using random-forest modeling and Shapley additive explanation (SHAP) to connect bill-of-materials features with measured thermal-cycling power loss and interpret the model’s decisions.\"},{\"question\":\"Which design factors are found to predominantly influence degradation?\",\"answer\":\"The interpretation shows silicon type, encapsulant thickness, busbar numbers, and wafer thickness as predominant contributors. Monocrystalline cells generally show lower average power loss than polycrystalline cells.\"}]","Analyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques | PDF",1785902562,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"analyzing-the-impact-of-design-factors-on-solar-module-thermomechanical-durability-using-interpretable-machine-learning-techniques-126015","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/analyzing-the-impact-of-design-factors-on-solar-module-thermomechanical-durability-using-interpretable-machine-learning-techniques-126015/126015/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is thermomechanical durability important for utility-scale solar modules?","Question",{"text":76,"@type":77},"Utility-scale photovoltaic systems must maintain long operating lifetime, but cyclic thermomechanical deformation caused by temperature variation degrades module components and reduces power output.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the study determine which design factors matter most?",{"text":81,"@type":77},"It analyzes a dataset of 251 module designs using random-forest modeling and Shapley additive explanation (SHAP) to connect bill-of-materials features with measured thermal-cycling power loss and interpret the model’s decisions.",{"name":83,"@type":74,"acceptedAnswer":84},"Which design factors are found to predominantly influence degradation?",{"text":85,"@type":77},"The interpretation shows silicon type, encapsulant thickness, busbar numbers, and wafer thickness as predominant contributors. 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