[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117387-en":3,"doc-seo-117387-105":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},117387,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Green Machine Learning - Analysing the Energy Efficiency of Machine Learning Models","Machine learning systems consume significant energy, raising concerns about sustainable deployment and the need to select models based on energy efficiency alongside predictive quality. This work applies the Green Machine Learning paradigm by jointly considering accuracy and energy use. The study compares six models across three binary-classification benchmark datasets, evaluating both F1-Score performance and energy consumption characteristics, and highlighting practical trade-offs for model choice. It also proposes directions to extend energy-aware evaluation to broader ML methods and to use metrics that balance effectiveness with resource demand.","2024 35th Irish Signals and Systems Conference (ISSC) ©2024 IEEE DOI: 10.1109/ISSC61953.2024.10603302| 979-8-3503-5298-6/24/$31.00 |   \nGreen Machine Learning: Analysing the Energy Efficiency of Machine Learning Models  \n1st Samara O. S. Santos Hamilton Institute Maynooth University Maynooth, Ireland  \n2nd Agustina Skiarski Hamilton Institute Maynooth University Maynooth, Ireland  \n3rd Daniel Garc´ıa-N´ ˜unez Hamilton Institute Maynooth University Maynooth, Ireland  \n4th Victor Lazzarini Dept Music Maynooth University Maynooth, Ireland  \n[samara.santos.2024@mumail.ie](samara.santos.2024@mumail.ie) agustina.skiarski.2024@mumail.ie daniel.garcianunez.2024@mumail.ie victor.lazzarini@mu.ie  \n5th Rafael de Andrade Moral Dept Mathematics and Statistics Maynooth University Maynooth, Ireland [rafael.deandrademoral@mu.ie](rafael.deandrademoral@mu.ie)  \n6th Edgar Galvan Dept Computer Science Maynooth University Maynooth, Ireland [edgar.galvan@mu.ie](edgar.galvan@mu.ie)  \n7th Andr L. C. Ottoni  \nDept Computer Science Federal University of Ouro Preto Ouro Preto, Brazil [andre.ottoni@ufop.edu.br](andre.ottoni@ufop.edu.br)  \n8th Erivelton Nepomuceno Dept of Electronic Eng Maynooth University Maynooth, Ireland [erivelton.nepomuceno@mu.ie](erivelton.nepomuceno@mu.ie)  \nAbstract—The consumption of energy by Machine Learning (ML) has increased significantly. There is growing concern about the sustainable use of ML, where choosing the best ML model should also consider energy efficiency. The main objective of the Green Machine Learning paradigm is the simultaneous optimisation of accuracy and energy consumption. The literature has presented some suggestions for metrics to be used. However, these metrics have not been extensively compared among different ML models. To address this aspect, in this paper, we have analysed six Machine Learning models applied to three benchmark datasets for binary classification tasks, focusing on performance and energy consumption. The results of the F1-Score show that the random forests model outperformed the other models, while logistic regression was more energy efficient. These results demonstrate the trade-offs between model performance and energy consumption, providing valuable guidance for algorithm selection. Performance metrics are an essential benchmark, with Python’s Scikit-Learn suite of models often outperforming neural networks in classification tasks. Future research should extend energy analysis to other machine learning methods and consider metrics that balance performance and energy consumption.  \nIndex Terms—Green Computing, Green Machine Learning, Energy-efficient Machine Learning, Computer Arithmetic  \nI. INTRODUCTION  \nGreen Computing commonly refers to an artificial intelligence system that is environmentally friendly and helps reduce carbon emissions [1], [2] . Lannelongue et al. [3] presented ten simple guidelines that can effectively improve the sustainability of computing applications. The applications of green computing are extensive and diverse, spanning fields such as robotics [4], biology [5], IoT [6], [7] and smart cities [8], [9] .  \nThis publication has emanated from research supported in part by a grant from Science Foundation Ireland under Grant number 18/CRT/6049 and 21/FFP-P/10065 . Erivelton Nepomuceno was supported by Brazilian Research Agencies: CNPq/INERGE (Grant No. 465704/2014-0), CNPq (Grant No. 425509/2018-4 and Grant No. 311321/2020-8) and FAPEMIG (Grant No. APQ-00870-17) .  \n979-8-3503-5298-6/24/$31.00 ©2024 IEEE  \nAn emerging area of research that has received considerable attention is Green Machine Learning (GML) [10], [11], which aims to develop strategies and techniques to reduce the carbon footprint and computational costs associated with Machine Learning (ML) environment. It is essential to focus on this aspect, as ML is increasingly used in industry [12] and research [13], addressing these algorithms’ energy consumption and carbon emissions is imperative.  \nTo a","cbCaie5bSvc0Nv6H","https://ap.wps.com/l/cbCaie5bSvc0Nv6H","pdf",237608,1,6,"English","en",105,"# Introduction\n## Measuring energy consumption for ML\n## Models and evaluation approach\n## Contributions\n# Methodology and experiments\n## Datasets for binary classification\n## Pre-processing: subsampling and feature reduction\n# Results and discussion\n## Performance and energy trade-offs\n# Future work","[{\"question\":\"What is the main objective of the Green Machine Learning approach in this paper?\",\"answer\":\"To optimize both accuracy and energy consumption simultaneously when selecting machine learning models.\"},{\"question\":\"Which models and datasets does the study compare?\",\"answer\":\"It compares six machine learning models on three benchmark datasets for binary classification tasks.\"},{\"question\":\"What do the results indicate about performance versus energy efficiency?\",\"answer\":\"Random forests achieve stronger F1-Score performance, while logistic regression is more energy efficient, illustrating clear trade-offs between the two goals.\"}]","Green Machine Learning - Analysing the Energy Efficiency of Machine Learning Models | PDF",1785675531,15,{"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},"green-machine-learning-analysing-the-energy-efficiency-of-machine-learning-models","",{"@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/green-machine-learning-analysing-the-energy-efficiency-of-machine-learning-models/117387/",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},"What is the main objective of the Green Machine Learning approach in this paper?","Question",{"text":75,"@type":76},"To optimize both accuracy and energy consumption simultaneously when selecting machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models and datasets does the study compare?",{"text":80,"@type":76},"It compares six machine learning models on three benchmark datasets for binary classification tasks.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results indicate about performance versus energy efficiency?",{"text":84,"@type":76},"Random forests achieve stronger F1-Score performance, while logistic regression is more energy efficient, illustrating clear trade-offs between the two goals.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"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"]