[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121035-en":3,"doc-seo-121035-105":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},121035,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Predictive Maintenance with Machine Learning - Comparative Analysis of Wind Turbines and PV Power Plants - Vol 2 No 2 2024","Renewable energy deployment demands innovations that ensure efficient and reliable operation of wind turbines and photovoltaic (PV) systems. Operational and maintenance challenges require strategies that improve failure prediction and reduce downtime, making predictive maintenance with machine learning (PdM-ML) highly relevant. This study evaluates PdM-ML for wind and PV by analyzing operational data, applying data preprocessing, and training machine learning models tailored to each system. Results show high-accuracy failure prediction for critical wind components and effective detection of PV efficiency declines, with both approaches lowering costs and improving operational efficiency compared with conventional maintenance.","Vol 2 No 2 2024  \n| Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants\u003Cbr>Uhanto Uhanto 1, Erkata Yandri 1,2,*, Erik Hilmi 1, Rifki Saiful 1 and Nasrullah Hamja 1\u003Cbr>1 Graduate School of Renewable Energy, Darma Persada University, Jl. Radin Inten 2, Pondok Kelapa, East Jakarta 13450, Indonesia; [uhanto.unud@gmail.com](uhanto.unud@gmail.com) (U.U.); [erkata@gmail.com](erkata@gmail.com) (E.Y.); [erikalthaf@gmail.com](erikalthaf@gmail.com) (E.H.); [rifkivanesa@gmail.com](rifkivanesa@gmail.com) (R.S.); [nasrullahpanasonic.nh@gmail.com](nasrullahpanasonic.nh@gmail.com) (N.H.)\u003Cbr>2 Center of Renewable Energy Studies, School of Renewable Energy, Darma Persada University, Jl. Radin Inten 2, Pondok Kelapa, East Jakarta 13450, Indonesia\u003Cbr>* Correspondence: [erkata@gmail.com](erkata@gmail.com) |  |\n| --- | --- |\n| Article History | Abstract |\n| Received 19 July 2024\u003Cbr>Revised 11 September 2024\u003Cbr>Accepted 20 September 2024\u003Cbr>Available Online 28 September 2024\u003Cbr>Keywords:\u003Cbr>Operational data analysis Failure prediction Maintenance cost reduction Component health monitoring Energy efficiency optimization Machine learning algorithms | The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems. |\n|  | Copyright: © 2024 by the authors. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License.([https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)) |\n\n. , . ,  \n1. Introduction  \nRenewable energy has become a global priority in addressing climate change [1, 2], meeting sustainable energy needs, and reducing greenhouse gas emissions [3] . Therefore, adopting renewable energy technologies is crucial to ensuring energy sustainability and reducing carbon emissions [4, 5] . Among various renewable energy technologies, photovoltaic (PV) solar power plants and wind turbines play a crucial role in reducing dependence on fossil fuels [1, 6, 7] . While substantial research has been conducted on the operational efficiency of these technologies, few studies have  \nprovided a comparative analysis of their predictive maintenance strategies, particularly in how these approaches minimize downtime and optimize performance.  \nPrevious studies have typically focused on a single technology, leaving a gap in cross-technology comparisons of predictive maintenance practices. This research builds on existing work by comparing these two key technologies comprehensively, highlighting the similarities and differences in their maintenance strategies. Wind turbines and PV systems are selected for their dominant roles in renewable energy production,  \nwith other technologies excluded due to their fundamentally different maintenance needs.  \nTo optimize ","cbCair4ZUEcEYXOq","https://ap.wps.com/l/cbCair4ZUEcEYXOq","pdf",1014264,1,12,"English","en",105,"# Introduction\n## Renewable energy priorities and challenges\n## Role of predictive maintenance\n# Predictive maintenance with machine learning (PdM-ML)\n## Comparative motivation: wind vs PV\n## Objectives and hypothesis\n# Energy management system (EMS) and evaluation dimensions\n## Availability, efficiency, affordability, sustainability, governance","[{\"question\":\"Why is predictive maintenance with machine learning important for renewable energy systems?\",\"answer\":\"It helps enhance failure prediction and reduce downtime, improving the reliability and efficiency of wind and PV operations.\"},{\"question\":\"How does the study compare predictive maintenance between wind turbines and PV systems?\",\"answer\":\"It analyzes operational data for each technology, preprocesses the data, and develops machine learning models specific to wind turbines and PV power plants.\"},{\"question\":\"What outcomes are reported for the wind turbine and PV models?\",\"answer\":\"The wind turbine model predicts failures in critical components like gearboxes and blades with high accuracy, while the PV model effectively predicts efficiency declines in inverters and solar panels.\"}]","Predictive Maintenance with Machine Learning - Comparative Analysis of Wind Turbines and PV Power Plants - Vol 2 No 2 2024 | PDF",1785733422,30,{"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},"predictive-maintenance-with-machine-learning-comparative-analysis-of-wind-turbines-and-pv-power-plants-vol-2-no-2-2024","",{"@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/predictive-maintenance-with-machine-learning-comparative-analysis-of-wind-turbines-and-pv-power-plants-vol-2-no-2-2024/121035/",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","2026-08-03",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},"Why is predictive maintenance with machine learning important for renewable energy systems?","Question",{"text":75,"@type":76},"It helps enhance failure prediction and reduce downtime, improving the reliability and efficiency of wind and PV operations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study compare predictive maintenance between wind turbines and PV systems?",{"text":80,"@type":76},"It analyzes operational data for each technology, preprocesses the data, and develops machine learning models specific to wind turbines and PV power plants.",{"name":82,"@type":73,"acceptedAnswer":83},"What outcomes are reported for the wind turbine and PV models?",{"text":84,"@type":76},"The wind turbine model predicts failures in critical components like gearboxes and blades with high accuracy, while the PV model effectively predicts efficiency declines in inverters and solar panels.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]