By Florian Nachtigall – TUB<\/em><\/p>\n[\/et_pb_text][et_pb_button button_text=”Read the publication” _builder_version=”4.22.2″ _module_preset=”default” button_url=”https:\/\/doi.org\/10.1016\/j.compenvurbsys.2023.102010″ hover_enabled=”0″ sticky_enabled=”0″ button_alignment=”center”][\/et_pb_button][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” text_font_size=”11px” text_line_height=”1.3em” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n
Figure<\/strong><\/em>:\u00a0Results overview of a targeted prioritization of buildings with a heating demand above 150 kWh\/m2a. (A) Energy savings per m2 of floor space for different levels of knowledge of the year of construction: ground truth knowledge (left), predicted year according to different cross-validation approaches (middle), and no knowledge (right). (B) Regional heterogeneity in the share of buildings in need of retrofitting in France.\u00a0(C) Illustration of accuracy in determining retrofit needs exemplified by the city of Valence, France.<\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"
In a recent study, our partner Technische Universit\u00e4t Berlin (Sustainability Economics of Human Settlements) – TUB looked at 25 million buildings in France, Spain and the Netherlands and tried to estimate the construction year and retrofit need. The overarching goal was to assess if machine learning methods can facilitate the identification of retrofit candidates at scale.<\/p>\n","protected":false},"author":52,"featured_media":978,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[16,9],"tags":[],"class_list":["post-969","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-news","category-news"],"jetpack_featured_media_url":"https:\/\/circeular.org\/wp-content\/uploads\/sites\/21\/2023\/10\/News-week40-TUB.png","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/circeular.org\/wp-json\/wp\/v2\/posts\/969","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/circeular.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/circeular.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/circeular.org\/wp-json\/wp\/v2\/users\/52"}],"replies":[{"embeddable":true,"href":"https:\/\/circeular.org\/wp-json\/wp\/v2\/comments?post=969"}],"version-history":[{"count":8,"href":"https:\/\/circeular.org\/wp-json\/wp\/v2\/posts\/969\/revisions"}],"predecessor-version":[{"id":983,"href":"https:\/\/circeular.org\/wp-json\/wp\/v2\/posts\/969\/revisions\/983"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/circeular.org\/wp-json\/wp\/v2\/media\/978"}],"wp:attachment":[{"href":"https:\/\/circeular.org\/wp-json\/wp\/v2\/media?parent=969"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/circeular.org\/wp-json\/wp\/v2\/categories?post=969"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/circeular.org\/wp-json\/wp\/v2\/tags?post=969"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}