{"id":16623,"date":"2024-09-20T12:36:55","date_gmt":"2024-09-20T10:36:55","guid":{"rendered":"https:\/\/is.ijs.si\/?p=16623"},"modified":"2025-03-26T13:23:37","modified_gmt":"2025-03-26T12:23:37","slug":"measuring-and-modeling-co2-emissions-in-machine-learning-processes","status":"publish","type":"post","link":"https:\/\/is.ijs.si\/?p=16623","title":{"rendered":"Measuring and Modeling CO2 Emissions in Machine Learning Processes"},"content":{"rendered":"\n<p>Ivo Hrib, Jan \u0160turm, Oleksandra Topal and Maja \u0160krjanc<\/p>\n<p><strong>Abstract<\/strong><br \/>With the rapid expansion of the computing industry, efficient<br \/>energy utilization and reduction of CO2 emissions are critically<br \/>important. This research develops analytical tools to predict CO2<br \/>emissions from various machine learning processes. We present a<br \/>novel methodology for data acquisition and analysis of CO2 emis-<br \/>sions during model training and testing. Our results demonstrate<br \/>the environmental impact of different algorithms and provide<br \/>insights into optimizing energy consumption in artificial intelli-<br \/>gence applications.<\/p>\n<p>\u00a0<\/p>\n\n\n\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/is.ijs.si\/wp-content\/uploads\/2024\/10\/IS2024_-_SIKDD_2024_paper_23-1.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of IS2024_-_SIKDD_2024_paper_23-1.\"><\/object><a id=\"wp-block-file--media-4d407ee2-ba50-4bfa-8af2-dd2340aa3ea1\" href=\"https:\/\/is.ijs.si\/wp-content\/uploads\/2024\/10\/IS2024_-_SIKDD_2024_paper_23-1.pdf\">IS2024_-_SIKDD_2024_paper_23-1<\/a><a href=\"https:\/\/is.ijs.si\/wp-content\/uploads\/2024\/10\/IS2024_-_SIKDD_2024_paper_23-1.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-4d407ee2-ba50-4bfa-8af2-dd2340aa3ea1\">Download<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":29,"featured_media":24966,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[109,102],"tags":[],"class_list":["post-16623","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-doi-sikdd-2024","category-papers"],"_links":{"self":[{"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/posts\/16623","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/users\/29"}],"replies":[{"embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=16623"}],"version-history":[{"count":3,"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/posts\/16623\/revisions"}],"predecessor-version":[{"id":25015,"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/posts\/16623\/revisions\/25015"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/media\/24966"}],"wp:attachment":[{"href":"https:\/\/is.ijs.si\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16623"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16623"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16623"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}