[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126022-en":3,"doc-seo-126022-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126022,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Leveraging machine learning for sustainable integration of renewable energy generation - abstract","Integration of renewable energy sources into electrical networks delivers long-term economic benefits and sustainability, yet intermittency and environmental dependence create production–consumption imbalance. Wind and solar variability makes accurate forecasting essential for planning and for maintaining grid stability, while consumption abnormalities must be handled. The thesis develops instruments and algorithms to improve renewable energy generation estimates using techniques including maximum power point tracking and machine learning models such as Random Forest, AdaBoost, and XGBoost, validating performance with accuracy above 80%.","Leveraging machine learning for sustainable integration of renewable energy generation  \nPushpa Sreenivasan1, Keerthiga Ganesan2, Iffath Fawad3, Sathya Sureshkumar4,  \nKirubakaran Dhandapani5  \n1Department of Electrical and Electronics Engineering, Panimalar Engineering College, Chennai, India 2Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai, India 3Department Electronics and Telecommunication Engineering, Dayananda Sagar College of Engineering, Bengaluru, India 4Department of Electrical and Electronics Engineering, SA Engineering College, Chennai, India 5Department of Electrical and Electronics Engineering, St. Joseph’s Institute of Technology, Chennai, India  \n\n| Article history:\u003Cbr>Received Apr 26, 2024 Revised Aug 8, 2024 Accepted Aug 26, 2024 | Long-term economic benefits and sustainability are provided by the integration of renewable energy sources (RESs) into electrical networks. However, because of their intermittent nature and reliance on environmental factors, RESs pose issues in production and consumption balance. Because renewable energy sources like wind and solar are unpredictable, forecasting their output is essential for planning purposes and maintaining grid stability. This thesis focuses on developing effective instruments and algorithms to improve renewable energy generation estimates and handle abnormalities in consumption. These tools and algorithms include maximum power point tracking and machine learning models like random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) . The methods' effectiveness is confirmed by accuracies higher than 80%, which provides speedier and more user-friendly solutions in comparison to the traditional ways. In the end, our effort seeks to offer practical instruments for anticipatory modelling and mitigating intermittentness in renewable energy sources, enabling their assimilation into current power structures to adequately supply energy requirements in a sustainable manner.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Demand response Energy forecasting Grid stability Machine learning\u003Cbr>Renewable energy integration Sustainable energy systems |  |\n\nCorresponding Author:  \nPushpa Sreenivasan  \nDepartment of Electrical and Electronics Engineering, Panimalar Engineering College Chennai, Tamilnadu, India  \n[Email: puvehava@gmail.com](Email: puvehava@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe energy sector is at a turning point in its development, with major sustainability and safety issues that call for quick solutions to avoid instability. There is a clear shift away from fossil fuels, as interest in renewable energy sources like geothermal, wind, and solar power grows [1]−[3] . In order to avoid blackoutsand preserve system stability, striking a balance between the supply and demand of energy is crucial. The generation of energy contributes significantly to greenhouse gas emissions, thus finding sustainable solutions is essential to halting climate change. Nonetheless, more precise forecasting and smooth grid integration are required due to the unpredictability of solar and wind energy output [4]−[6] . Promising paths for accurate energy forecasting and optimization can be found in machine learning. With the goal of maximizing the penetration of renewable energy sources while minimizing costs, this research suggests a hybrid model for a renewable energy system. The plan places a high priority on sustainability and dependability in order to reduce costs and guarantee effective grid integration [7]−[10] .  \nIntegration challenges with renewables: wind and solar energy are examples of renewable energy sources that require skilled grid management because they are naturally unpredictable owing to environmental conditions. A careful planning and efficient generation approach are essential, as the cost of energy storage systems for solar ","cbCair3BCoyw9UGm","https://ap.wps.com/l/cbCair3BCoyw9UGm","pdf",578855,6,1,9,"English","en",105,"# Abstract\n## Introduction\n## Integration challenges with renewables\n## Solutions offered for the difficulties of integrated renewable energy","[{\"question\":\"Why is forecasting important for renewable energy integration?\",\"answer\":\"Wind and solar output are unpredictable due to environmental conditions. Accurate forecasting supports planning and helps maintain grid stability and balance.\"},{\"question\":\"Which machine learning models are proposed in the thesis?\",\"answer\":\"The document highlights Random Forest (RF), AdaBoost, and Extreme Gradient Boosting (XGBoost), used to improve renewable generation estimates.\"},{\"question\":\"How do the proposed methods validate effectiveness?\",\"answer\":\"The approach reports effectiveness through accuracies higher than 80%, and claims faster, more user-friendly results compared with traditional methods.\"}]","Leveraging machine learning for sustainable integration of renewable energy generation - abstract | PDF",1785902595,23,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"leveraging-machine-learning-for-sustainable-integration-of-renewable-energy-generation-abstract","",{"@graph":37,"@context":87},[38,55,70],{"@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":54},"https://docshare.wps.com/document/leveraging-machine-learning-for-sustainable-integration-of-renewable-energy-generation-abstract/126022/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is forecasting important for renewable energy integration?","Question",{"text":77,"@type":78},"Wind and solar output are unpredictable due to environmental conditions. Accurate forecasting supports planning and helps maintain grid stability and balance.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which machine learning models are proposed in the thesis?",{"text":82,"@type":78},"The document highlights Random Forest (RF), AdaBoost, and Extreme Gradient Boosting (XGBoost), used to improve renewable generation estimates.",{"name":84,"@type":75,"acceptedAnswer":85},"How do the proposed methods validate effectiveness?",{"text":86,"@type":78},"The approach reports effectiveness through accuracies higher than 80%, and claims faster, more user-friendly results compared with traditional methods.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":108,"slug":138},19,"General","general"]