[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123759-en":3,"doc-seo-123759-105":30,"detail-sidebar-cat-0-en-105":95},{"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},123759,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",6,"Technology","Optimizing Solar Energy Harvesting - Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions","Solar power is widely used as a clean, readily accessible energy source, yet photovoltaic systems face variable output under changing weather and partial shading. This study applies supervised machine learning to peak power point tracking to maximize PV arrangement power despite non-linear solar intensity–output relationships. The squared multiple squared exponential Gaussian process regression (SGPRA) method is tested across three rapidly varying environmental conditions and validated in Matlab/Simulink against the variable step size incremental conductance algorithm (VINA). Results show over 90% peak power efficiency, faster tracking (0.13 s), low mean error (0.042), and improved stability.","Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions  \nZaiba Ishrat a,1,*, Kunwar Babar Ali b,2, Satvik Vats c,3, Surender Kumar d,4  \naMeerut Institute of Technology, Meerut, Bypass Road Bahgpat Crossing, Meerut, 250005, U.P. India b Meerut Institute of Engineering and Technology, N.H. 58, Delhi Roorkee Highway, Meerut, 250005, U.P. India c Graphic Era Hill University, Road Sociuety Area, Celement Town, Dehradun, 248002, Uttrakhand, India d IIMT College of Engineering, Knowledge Park III, Greater Noida, 201310, U.P, India  \n[1](1 zaibaishrat01@gmail.com)[ zaibaishrat01@gmail.com](1 zaibaishrat01@gmail.com); [2](2 kunwarbabrali1@gmail.com)[ kunwarbabrali1@gmail.com](2 kunwarbabrali1@gmail.com); [3](3 Svats@gehu.ac.in)[ Svats@gehu.ac.in](3 Svats@gehu.ac.in); [4](4 skladhoura88@gmail.com)[ skladhoura88@gmail.com](4 skladhoura88@gmail.com)  \n* Corresponding Author  \nARTICLE INFO  \nArticle history  \nReceived October 03, 2023 Revised November 24, 2023 Accepted December 14, 2023  \nKeywords  \nPV System (PVS);  \nMxPPT;  \nSGPRA;  \nMatlab/Simulink  \nABSTRACT  \nSolar Power is one of the significant prevalent forms of clean energy due to its perceived to be pollution-free and easily accessible. The market for renewable energy was established by the rapid development in electrical energy consumption and the diminution of conventional energy resources (CER) . Under varying weather condition extracted energy from solar system is not constant and maximum. This study suggests the applicability of machine learning algorithm (MLA) in Peak power point tracking (P3T) methods to maximize power of a PV arrangement under varying weather conditions. Machine learning methods optimize peak powerpoint tracking in solar photovoltaic systems by bringing agility, data-driven decisionmaking, and increased accuracy. MLAs improve the overall efficiency, stability, and dependability of these systems by handling the unpredictability of solar energy production under varying weather circumstances and PSCs Because MLAs are able to learn and adjust to non-linear relationships between solar intensity and PVS output. In this study, the squared multiple squared exponential Gaussian process regression method SGPRA tested in three rapidly varying ecological conditions. The performance of ML-P3T methods is validated using Matlab/Simulink, and the simulation outcome are compared with one of the most used algorithms, the variable step size incremental conductance algorithm (VINA) . The Matlab/Simulink findings show that SGPRA operates significantly better under varying weather circumstances, harnessing more peak power efficiency > 90%, shorter tracking time 0.13 sec, a mean error of 0.042, and superior stability.  \nThis is an open-access article under the CC–BY-SA license.  \n1. Introduction  \nMassive interest in the use of green energy resources (GER) has been sparked by the rise in demand, rising costs of fossil fuels, and concern about environmental issues. Because it is so readily available, solar energy is one of them [1], [2] . It appears encouraging that solar energy power will expand from 227 GW in 2015 to 1362 GW by 2030 [38], [39] .  \nDespite the advantages that a PV system (PVS) can provide, PVS has certain drawbacks; including a high installation cost, poor energy conversion efficiency, and unpredictable power output due to a reliance on constantly shifting climatic circumstances [3], [25] . The most commercial solar panels' efficiency falling between 15% and 22%, a sizable amount of sunshine does not get converted into electrical power. When a solar panel is partially shaded, either the system as a whole or specific sections ofit are, resulting in uneven lighting. This may occur as a result of adjacent structures, trees, or even cloud cover. In addition to low ouput power PSCs also responsible for mismatch in power loses. There are several peaks on the P-V characteristics (PVC) curve unde","cbCaimmhjrhp0Nhe","https://ap.wps.com/l/cbCaimmhjrhp0Nhe","pdf",1160667,1,14,"English","en",105,"# Introduction\n## Challenges in PV output under varying weather and partial shading\n## Existing maximum power tracking approaches\n## AI-based and optimization-based methods\n# Proposed ML-driven peak power point tracking approach\n## SGPRA method and test conditions\n## Validation setup in Matlab/Simulink and comparison with VINA","[{\"question\":\"Why does PV peak power tracking become difficult under partial shading and varying weather?\",\"answer\":\"Under partial shading, the P–V curve contains multiple local peaks, making it hard for conventional trackers to follow the global peak. Weather changes also alter solar intensity, causing output to vary and reducing tracking reliability.\"},{\"question\":\"What supervised machine learning method is tested for peak power point tracking?\",\"answer\":\"The study tests the squared multiple squared exponential Gaussian process regression method (SGPRA) for peak power point tracking.\"},{\"question\":\"How is the proposed ML-P3T approach validated and compared?\",\"answer\":\"Validation is performed using Matlab/Simulink simulations across three rapidly varying environmental conditions. The results are compared with the variable step size incremental conductance algorithm (VINA).\"},{\"question\":\"What performance improvements are reported versus the comparison algorithm?\",\"answer\":\"SGPRA achieves higher peak power efficiency (\\u003e90%), shorter tracking time (0.13 s), lower mean error (0.042), and superior stability under varying weather conditions.\"}]","Optimizing Solar Energy Harvesting - Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions | PDF",1785818380,35,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"optimizing-solar-energy-harvesting-supervised-machine-learning-driven-peak-power-point-tracking-for-diverse-weather-conditions","",{"@graph":36,"@context":89},[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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/optimizing-solar-energy-harvesting-supervised-machine-learning-driven-peak-power-point-tracking-for-diverse-weather-conditions/123759/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why does PV peak power tracking become difficult under partial shading and varying weather?","Question",{"text":75,"@type":76},"Under partial shading, the P–V curve contains multiple local peaks, making it hard for conventional trackers to follow the global peak. Weather changes also alter solar intensity, causing output to vary and reducing tracking reliability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What supervised machine learning method is tested for peak power point tracking?",{"text":80,"@type":76},"The study tests the squared multiple squared exponential Gaussian process regression method (SGPRA) for peak power point tracking.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the proposed ML-P3T approach validated and compared?",{"text":84,"@type":76},"Validation is performed using Matlab/Simulink simulations across three rapidly varying environmental conditions. The results are compared with the variable step size incremental conductance algorithm (VINA).",{"name":86,"@type":73,"acceptedAnswer":87},"What performance improvements are reported versus the comparison algorithm?",{"text":88,"@type":76},"SGPRA achieves higher peak power efficiency (>90%), shorter tracking time (0.13 s), lower mean error (0.042), and superior stability under varying weather conditions.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,117,122,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":115,"slug":116},50,"technology",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},7,"Healthcare",40,"healthcare",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},8,"Research & Report",30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]