{"id":96928,"date":"2021-07-28T08:51:14","date_gmt":"2021-07-28T07:51:14","guid":{"rendered":"https:\/\/www.pbctoday.co.uk\/news\/?p=96928"},"modified":"2022-07-01T16:06:46","modified_gmt":"2022-07-01T15:06:46","slug":"building-emissions-rates","status":"publish","type":"post","link":"https:\/\/www.pbctoday.co.uk\/news\/digital-construction-news\/building-emissions-rates\/96928\/","title":{"rendered":"New AI system predicts building emissions rates in under a second"},"content":{"rendered":"<h2>Computer scientists at <a href=\"https:\/\/www.lboro.ac.uk\/\" target=\"_blank\" rel=\"noopener noreferrer\">Loughborough University<\/a> have partnered with engineering consultancy,\u00a0<a href=\"https:\/\/cundall.com\/\" target=\"_blank\" rel=\"noopener noreferrer\">Cundall<\/a>, to create an artificial intelligence system that can predict building emissions rates (BER)<\/h2>\n<p><a href=\"https:\/\/www.lboro.ac.uk\/departments\/compsci\/staff\/academic-teaching\/georgina-cosma\/\" target=\"_blank\" rel=\"noopener noreferrer\">Dr Georgina Cosma<\/a>\u00a0and postgraduate student Kareem Ahmed of the\u00a0<a href=\"https:\/\/www.lboro.ac.uk\/science\/\" target=\"_blank\" rel=\"noopener noreferrer\">School of Science<\/a>, have\u00a0designed and trained an AI model to predict building emissions rates values with 27 inputs with little loss in accuracy.<\/p>\n<p>The proposed AI model \u2013\u00a0which was created with the support of Cundall\u2019s head of research and innovation, Edwin Wealend, and trained using large-scale data obtained from UK government <a href=\"https:\/\/www.pbctoday.co.uk\/news\/construction-technology-news\/energy-modelling-buildings\/88027\/\" target=\"_blank\" rel=\"noopener noreferrer\">energy performance<\/a> assessments \u2013 can generate a BER value almost instantly.<\/p>\n<p>Dr Cosma says the research \u201cis an important first step towards the use of machine learning tools for energy prediction in the UK\u201d and it shows how data can \u201cimprove current processes in the construction industry\u201d.<\/p>\n<h3>The AI model<\/h3>\n<p>In their latest paper, Dr Cosma and the team reveal their AI system can generate <a href=\"https:\/\/www.pbctoday.co.uk\/news\/energy-news\/carbon-emissions-from-buildings\/84234\/\" target=\"_blank\" rel=\"noopener noreferrer\">building emissions<\/a> rates for non-domestic buildings in less than a second and with as few as 27 variables with little loss in accuracy.<\/p>\n<p>They used a \u2018decision tree-based ensemble\u2019 machine algorithm and built and validated the system using 81,137 real data records that contain information for non-domestic buildings over the whole of England from 2010 to 2019.<\/p>\n<p>The data contained information such as building capacity, location, heating, cooling, lighting, and activity.<\/p>\n<p>The team focused on calculating the rates for non-domestic buildings \u2013 such as shops, offices, factories, schools, restaurants, hospitals, and cultural institutions \u2013 as these are some of the most inefficient buildings in the UK in terms of energy use.<\/p>\n<p>The project findings were presented at\u00a0<a href=\"https:\/\/www.cibse.org\/technical-symposium\" target=\"_blank\" rel=\"noopener noreferrer\">The Chartered Institution of Building Services Engineers (CIBSE) Technical Symposium 2021<\/a>\u00a0and the paper is to be published on CIBSE\u2019s website later this year.<\/p>\n<h3>\u2018Reducing energy consumption and reaching net zero\u2019<\/h3>\n<p>Dr Cosma, said: \u201cStudies on the applications of <a href=\"https:\/\/www.pbctoday.co.uk\/news\/energy-news\/embodied-carbon-ai\/87654\/\" target=\"_blank\" rel=\"noopener noreferrer\">machine learning<\/a> on energy prediction of buildings exist, but these are limited, and even though they only make up 8% of all buildings, non-domestic buildings account for 20% of UK\u2019s total CO<sub>2<\/sub>\u00a0emissions.\u201d<\/p>\n<p>Edwin Wealend, added: \u201cEventually, we hope to build on the techniques developed in this project to predict real operational energy consumption.<\/p>\n<p>\u201cBy predicting the energy consumption and emissions of non-domestic buildings quickly and accurately, we can focus our energy on the more important task \u2013 reducing energy consumption and reaching net zero.\u201d<\/p>\n<div class=\"mk-custom-module-container\">\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Computer scientists at Loughborough University have partnered with Cundall to create an artificial intelligence system that can predict building emissions rates (BER).<\/p>\n","protected":false},"author":16,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[141293],"tags":[172725,856,430,93645],"stakeholder_rel_tag":[],"contributor_rel_tag":[],"class_list":{"0":"post-96928","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-digital-construction-news","7":"tag-artificial-intelligence","8":"tag-construction-technology","9":"tag-energy-efficiency","10":"tag-net-zero"},"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v26.9) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>New AI system predicts building emissions rates in under a second<\/title>\n<meta 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