[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290297-105":59,"doc-detail-290297-en":134},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":127,"head_meta":129,"extra_data":131,"updated_unix":133},105,"en","machine-learning-reduces-soft-costs-for-residential-solar-photovoltaics-290297","Machine learning reduces soft costs for residential solar photovoltaics","","Further rollout of rooftop solar photovoltaics depends on lowering soft (non-hardware) costs, especially customer acquisition expenses that remain large and difficult to reduce. This study compares machine learning prediction-oriented models with logistic regression, the dominant significance-based approach in adoption research. Machine learning substantially improves adopter identification, raising the true positive rate from 66% to 87% and the true negative rate from 75% to 88%. The results enable a 15% reduction in acquisition costs and support new market opportunity discovery, with broader implications for clean energy adoption and policy challenges.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-reduces-soft-costs-for-residential-solar-photovoltaics-290297/290297/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-reduces-soft-costs-for-residential-solar-photovoltaics-290297/290297.png","ImageObject",300,407,{"name":92,"@type":93},"Lucas Martin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"Why are soft costs important for residential solar photovoltaics rollout?","Question",{"text":112,"@type":113},"Soft costs, especially customer acquisition expenses, make up the largest and more reduction-resistant share of installed PV prices, so lowering them is critical for diffusion.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the study improve PV adoption prediction compared with logistic regression?",{"text":117,"@type":113},"It uses machine learning to predict PV adopters and non-adopters, capturing complex variable interactions and nonlinear effects beyond what significance-based logistic regression provides.",{"name":119,"@type":110,"acceptedAnswer":120},"What cost impact does machine learning enable in the results?",{"text":121,"@type":113},"With more accurate predictions, the study reports a 15% reduction in customer acquisition costs (about $0.07/Watt) and better identification of market opportunities for solar companies.",{"name":123,"@type":110,"acceptedAnswer":124},"What broader implications does the research suggest?",{"text":125,"@type":113},"The findings support applying similar prediction methods to adoption of other clean energy technologies and inform policy challenges related to market growth and energy inequality.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},290297,1789641262,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":133,"read_time":148,"preview_page_count":4},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Lawrence Berkeley National Laboratory LBL Publications  \nTitle  \nMachine learning reduces soft costs for residential solar photovoltaics  \nPermalink  \n[https://escholarship.org/uc/item/2815b47r](https://escholarship.org/uc/item/2815b47r)  \nJournal  \nScientific Reports, 13(1)  \nISSN  \n2045-2322  \nAuthors  \nDong, Changgui  \nNemet, Gregory Gao, Xue  \net al.  \nPublication Date  \n2023-05-01  \nDOI  \n10.1038/s41598-023-33014-4  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-NoDerivatives License, available at [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning reduces soft costs for residential solar photovoltaics  \nChanggui Dong1*, Gregory Nemet2, Xue Gao3,4*, Galen Barbose 5, Benjamin Sigrin6 & Eric O’Shaughnessy7  \nFurther deployment of rooftop solar photovoltaics (PV) hinges on the reduction of soft (non-hardware) costs—now larger and more resistant to reductions than hardware costs. The largest portion of these soft costs is the expenses solar companies incur to acquire new customers. In this study, we demonstrate the value of a shift from significance-based methodologies to prediction-oriented models to better identify PV adopters and reduce soft costs. We employ machine learning to predict PV adopters and non-adopters, and compare its prediction performance with logistic regression, the dominant significance-based method in technology adoption studies. Our results show that machine learning substantially enhances adoption prediction performance: The true positive rate of predicting adopters increased from 66 to 87%, and the true negative rate of predicting non-adopters increased from 75 to 88%. We attribute the enhanced performance to complex variable interactions and nonlinear effects incorporated by machine learning. With more accurate predictions, machine learning is able to reduce customer acquisition costs by 15%($0.07/Watt) and identify new market opportunities for solar companies to expand and diversify their customer bases. Our research methods and findings provide broader implications for the adoption of similar clean energy technologies and related policy challenges such as market growth and energy inequality.  \nDeploying renewable energy technologies is key to mitigating climate change and fostering an energy transition1–3. Driven by rapid hardware cost reductions, solar photovoltaics (PV) is ready to be subsidy-free and power a sustainable future4,5. However, PV non-hardware or “soft” costs now account for over 60% of installed prices and are more resistant to reductions than hardware costs6. Soft cost stagnation could slow PV diffusion7–9. Customer acquisition costs, i.e., PV companies’ costs to identify and acquire new customers, are currently the largest component of PV soft costs. Customer acquisition costs amounted to 21%($0.43/Watt) of total PV soft costs in the U.S. in 202010. Furthermore, customer acquisition costs have been rising as rooftop solar diffusion shifts from early adopters to mass diffusion, and as customers get fatigue from door-to-door marketing, creating a substantial challenge for PV companies10, 11. Developing efficient and effective methods to identify prospective PV adopters could reduce customer acquisition costs, accelerating technology diffusion and the associated climate benefits. Improved adoption prediction could also benefit grid infrastructure planning, transmission and storage siting, and subsidy policy design for low-income communities via adoption ‘seeding’12.  \nPredicting household PV adoption differs from explaining PV adoption. Prior research has relied on significance-based methods with an extensive focus on the differences between PV ","cbCaif3uC8Q8Q7vW","https://ap.wps.com/l/cbCaif3uC8Q8Q7vW","pdf",2051365,15,"English","# Machine learning reduces soft costs for residential solar photovoltaics\n## Objective and motivation\n## Methodology and comparison\n## Results and implications","[{\"question\":\"Why are soft costs important for residential solar photovoltaics rollout?\",\"answer\":\"Soft costs, especially customer acquisition expenses, make up the largest and more reduction-resistant share of installed PV prices, so lowering them is critical for diffusion.\"},{\"question\":\"How does the study improve PV adoption prediction compared with logistic regression?\",\"answer\":\"It uses machine learning to predict PV adopters and non-adopters, capturing complex variable interactions and nonlinear effects beyond what significance-based logistic regression provides.\"},{\"question\":\"What cost impact does machine learning enable in the results?\",\"answer\":\"With more accurate predictions, the study reports a 15% reduction in customer acquisition costs (about $0.07/Watt) and better identification of market opportunities for solar companies.\"},{\"question\":\"What broader implications does the research suggest?\",\"answer\":\"The findings support applying similar prediction methods to adoption of other clean energy technologies and inform policy challenges related to market growth and energy inequality.\"}]","Machine learning reduces soft costs for residential solar photovoltaics | PDF",38]