[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124349-en":3,"doc-seo-124349-105":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":4,"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},124349,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Plithogenic Machine Learning Solutions to Material Selection in Renewable Energy Systems - Paper","Plithogenic-based decision models improve the design of optimal solutions for complex problems. This study proposes an integrated decision framework combining plithogeny and machine learning to address selecting smart and sustainable materials for renewable energy systems. Ten evaluation criteria are considered across multiple material categories including photovoltaic, thermoelectric, piezoelectric, phase change, supercapacitor, and electrochromic. Random forest identifies the most crucial criteria, while plithogenic TOPSIS ranks materials, with accuracy compared against support vector machines and supplemented by sensitivity analysis.","TDAF  \nIETI Transactions on  \nData Analysis and Forecasting  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niTDAF | eISSN: 2959-0442 | Vol. 3 No. 3 (2025) |   \n[https://doi.org/10.3991/itdaf.v3i3.57085](https://doi.org/10.3991/itdaf.v3i3.57085)  \nPAPER  \nPlithogenic Machine Learning Solutions to Material Selection in Renewable Energy Systems  \nNivetha Martin1,2  (􀀍), Gabriel XG Yue3, Davron Aslonqulovich Juraev4,5   \n1Department of Mathematics, Arul Anandar College (Autonomous), Karumathur, Tamil Nadu, India  \n2Post Doctoral Department, International Engineering and Technology Institute, Hong Kong, China  \n3Department of Computer Science and Engineering, European University Cyprus, Nicosia, Cyprus  \n4Scientific Research Center, Baku Engineering University, Baku, Azerbaijan  \n5Department of Mathematical Analysis and Differential Equations, Karshi State University, Karshi, Uzbekistan  \nnivetha.martin710@ [gmail.com](gmail.com)  \nABSTRACT  \nPlithogenic-based decision models are more effective in designing optimal solutions to intricate problems. This study work proposes an integrated decisioning model conjoining plithogeny and machine learning algorithms. This study considers the decision-making problem of selecting smart and sustainable materials for the effective functioning of renewable energy systems. The decisioning model has ten evaluation criteria and considers alternatives for materials subjected to five categories of photovoltaic, thermoelectric, piezoelectric, phase change, supercapacitor, and electrochromic. This work employs the algorithm of a random forest classifier in determining the most crucial criteria for selecting smart and sustainable materials. The plithogenic-based decision method of TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is employed in ranking materials of each kind. The proposed decisioning approach is the combination of a machine learning algorithm anda plithogenic decision approach, which is further facilitated by the intervention of Python programming. The criteria selection accuracy is compared with a support vector machine algorithm to demonstrate the efficacy of this integrated decision approach in ranking the materials used in formulating robust renewable energy systems. Sensitivity analysis is also performed to exhibit the efficacy of this proposed model. This model has few limitations, as it considers a few selected materials under each of the categories.  \nKEYWORDS  \nmachine learning algorithms, plithogeny, sustainable materials, renewable energy system  \n1 INTRODUCTION  \nThe increasing demand for cleaner energy primarily contributes to the transition towards renewable energy systems (RES) to handle the challenges of environmental sustainability. The materials, such as photovoltaic, thermoelectric, piezoelectric, phase change, supercapacitor, and electrochromic, are primarily applied in renewable technologies. However, the effective functioning of these renewable systems is highly dependent on the material selection. The robustness and sustainability of  \nMartin, N., Yue, G. X. G., Juraev, D. A. (2025) . Plithogenic Machine Learning Solutions to Material Selection in Renewable Energy Systems. IETI Transactions on Data Analysis and Forecasting (iTDAF), 3(3), pp. 21–34. [https://doi.org/10.3991/itdaf.v3i3.57085](https://doi.org/10.3991/itdaf.v3i3.57085)[ ](https://doi.org/10.3991/itdaf.v3i3.57085)[Article submitted 2025-06-09. Revision uploaded 2025-08-06. Final acceptance 2025-08-07.](Article submitted 2025-06-09. Revision uploaded 2025-08-06. Final acceptance 2025-08-07.)  \n© 2025 by the authors of this article. Published under CC-BY.  \niTDAF | Vol. 3 No. 3 (2025) IETI Transactions on Data Analysis and Forecasting (iTDAF) 21  \nMartin et al.  \nRES are reliant on the attributes of the materials, such as Energy Efficiency (EE), Cost Efficiency (CE), Durability (D), Environmental Impact (EI), Ther","cbCaieP6ukUDl8kY","https://ap.wps.com/l/cbCaieP6ukUDl8kY","pdf",624108,1,14,"English","en",105,"# Introduction\n## Renewable energy transition and material selection needs\n## Integrated plithogenic logic with machine learning approach\n# Methodology\n## Attribute significance via Random Forest\n## Material ranking via Plithogenic TOPSIS\n# Evaluation\n## Comparison with Support Vector Machine\n## Sensitivity analysis","[{\"question\":\"What integrated approach does the paper propose for material selection?\",\"answer\":\"It combines plithogeny-based decision models with machine learning, using Random Forest to identify key criteria and Plithogenic TOPSIS to rank material alternatives.\"},{\"question\":\"Which material types and evaluation criteria are considered?\",\"answer\":\"The study evaluates materials across five categories: photovoltaic, thermoelectric, piezoelectric, phase change, supercapacitor, and electrochromic, using ten evaluation criteria/attributes.\"},{\"question\":\"How is the effectiveness of the proposed method validated?\",\"answer\":\"Criterion selection accuracy is compared with a support vector machine model, and sensitivity analysis is performed to demonstrate the model’s efficacy.\"}]","Plithogenic Machine Learning Solutions to Material Selection in Renewable Energy Systems - Paper | PDF",1785821755,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"plithogenic-machine-learning-solutions-to-material-selection-in-renewable-energy-systems-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/plithogenic-machine-learning-solutions-to-material-selection-in-renewable-energy-systems-paper/124349/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What integrated approach does the paper propose for material selection?","Question",{"text":75,"@type":76},"It combines plithogeny-based decision models with machine learning, using Random Forest to identify key criteria and Plithogenic TOPSIS to rank material alternatives.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which material types and evaluation criteria are considered?",{"text":80,"@type":76},"The study evaluates materials across five categories: photovoltaic, thermoelectric, piezoelectric, phase change, supercapacitor, and electrochromic, using ten evaluation criteria/attributes.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the effectiveness of the proposed method validated?",{"text":84,"@type":76},"Criterion selection accuracy is compared with a support vector machine model, and sensitivity analysis is performed to demonstrate the model’s efficacy.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]