[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117501-en":3,"doc-seo-117501-105":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},117501,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Comparative Predictive Analysis through Machine Learning in Solar Cooking Technology","Renewable energy research addresses global environmental challenges, and solar cooking is a sustainable alternative to conventional methods in regions with strong sunlight. Existing studies often analyze single models or omit rigorous comparisons, leaving uncertainty about the most accurate predictive approach for solar cooking performance. This paper compares predictive accuracy for pan and pot temperatures using multiple machine learning regressors trained on cooking time, heat-transfer fluid mass flow rate, fluid type, and global solar radiation. Results identify extreme gradient boosting as the best-performing model with high R² and low error metrics, while random forest shows test overfitting.","Comparative Predictive Analysis through  \nMachine Learning in Solar Cooking Technology  \nOriginal Scientific Paper  \nKarankumar Chaudhari*  \nDepartment of Mechanical Engineering, G H Raisoni College of Engineering, Nagpur, India  \n[karan.chaudhari01@gmail.com](karan.chaudhari01@gmail.com)  \nPramod Walke  \nDepartment of Mechanical Engineering, G H Raisoni College of Engineering, Nagpur, India  \n[pramod.walke@raisoni.net](pramod.walke@raisoni.net)  \nSagar Shelare  \nDepartment of Mechanical Engineering, Priyadarshini College of Engineering, Nagpur, India Centre for Research Impact and Outcomes, Chitkara University, Rajpura, Punjab, India[sagmech24@gmail.com](sagmech24@gmail.com)  \n*Corresponding author  \nAbstract – Renewable energy technology has helped solve global environmental issues in recent years. Solar cooking technology is a sustainable alternative to conventional cooking, particularly in regions with ample sunlight. Although there is a growing interest into solar cooking, however, there is a lack of comprehensive comparison research upon the machine learning models predictive accuracy. Prior studies frequently concentrate upon individual models or fail to conduct comprehensive comparative analyses, resulting in a knowledge deficit regarding the most effective predictive methodologies for solar cooking technology. This research article compares solar cooking with special types of cooking utensils used for indoor cooking by predictive analysis of different kinds of machine learning models. To achieve proper cooking, the temperature of both pan and pot is to be monitored constantly. For this, a machine learning (ML) system model was constructed for predicting pan and pot temperature as a response parameter. By leveraging datasets encompassing time duration of the cooking, mass flow rate of heat transfer fluid, type of heat transfer fluid, and global solar radiations, a range of machine learning algorithms, including decision tree regressor, linear regression, extreme gradient boosting, and random forest regressor algorithms, are employed for predicting pan and pot temperature of solar cookers. Extreme gradient boosting is the best machine learning model for solar utensil temperature, with maximum R2 and minimum mean squared error, mean absolute error, and root mean squared error values that perfectly predict all answers. Also, extreme gradient boosting predicts well on training and testing datasets, whereas Random forest predicts well on training datasets but poorly on test data, causing overfitting. This research shows that machine learning could revolutionize solar cooking technology, promising a future for renewable energy and sustainable living.  \nKeywords: Solar Cooking, Machine Learning, Regression Analysis, XGBoost, Statistical Analysis  \nReceived: February 12, 2024; Received in revised form: April 22, 2024; Accepted: April 23, 2024  \n1. INTRODUCTION  \nIn recent years, the global quest for sustainable and eco-friendly practices has gained unprecedented momentum, prompting a critical reevaluation of conventional processes across various sectors. Clean, renewable energy is necessary to combat climate change, environmental degradation, and the depletion of fossil fuels. One of the domains that needs a paradigm shift is cooking. Traditional methods use environmentally  \nharmful non-renewable energy. Solar energy in culinary applications overcomes environmental concerns connected with conventional fuel sources, reduces climate change, and supports global sustainable development. Modern cooking consumes considerable energy, adding to greenhouse gas emissions and resource depletion. This article examines the constraints of conventional cooking and the potential benefits of solar energy to demonstrate how sustainable energy solutions improve the culinary sector. Through an ex-  \nVolume 15, Number 6, 2024 543  \namination of existing research, technological advancements, and successful case studies, we will explore the multifa","cbCaifwvcQN2PdDJ","https://ap.wps.com/l/cbCaifwvcQN2PdDJ","pdf",1451597,1,10,"English","en",105,"# Abstract\n# Introduction\n## Sustainability motivation for cooking\n## Related work on solar cookers and optimization","[{\"question\":\"Why is solar cooking considered important in this study?\",\"answer\":\"Solar cooking provides a sustainable substitute for conventional cooking by reducing environmental concerns tied to non-renewable fuel use and greenhouse gas emissions, especially where sunlight is ample.\"},{\"question\":\"What inputs are used to predict pan and pot temperatures?\",\"answer\":\"The model uses datasets covering cooking duration, mass flow rate of the heat transfer fluid, heat transfer fluid type, and global solar radiation.\"},{\"question\":\"Which machine learning model performs best for predicting solar utensil temperatures?\",\"answer\":\"Extreme gradient boosting achieves the highest predictive quality, with maximum R² and the lowest values of mean squared error, mean absolute error, and root mean squared error, outperforming others on both training and testing datasets.\"}]","Comparative Predictive Analysis through Machine Learning in Solar Cooking Technology | PDF",1785676369,25,{"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},"comparative-predictive-analysis-through-machine-learning-in-solar-cooking-technology","",{"@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/comparative-predictive-analysis-through-machine-learning-in-solar-cooking-technology/117501/",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","2026-08-02",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 solar cooking considered important in this study?","Question",{"text":75,"@type":76},"Solar cooking provides a sustainable substitute for conventional cooking by reducing environmental concerns tied to non-renewable fuel use and greenhouse gas emissions, especially where sunlight is ample.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What inputs are used to predict pan and pot temperatures?",{"text":80,"@type":76},"The model uses datasets covering cooking duration, mass flow rate of the heat transfer fluid, heat transfer fluid type, and global solar radiation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performs best for predicting solar utensil temperatures?",{"text":84,"@type":76},"Extreme gradient boosting achieves the highest predictive quality, with maximum R² and the lowest values of mean squared error, mean absolute error, and root mean squared error, outperforming others on both training and testing datasets.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]