{"id":363,"date":"2017-11-02T00:00:00","date_gmt":"2017-11-01T23:00:00","guid":{"rendered":"https:\/\/blog.deepki.com\/2017\/11\/02\/clustering-donnees\/"},"modified":"2023-10-31T15:05:03","modified_gmt":"2023-10-31T14:05:03","slug":"clustering-donnees","status":"publish","type":"post","link":"https:\/\/www.deepki.com\/fr\/blog\/clustering-donnees\/","title":{"rendered":"Le clustering, ou comment ne pas se laisser d\u00e9border par les donn\u00e9es"},"content":{"rendered":"<p><span style=\"font-weight: bold;\"><span class=\"dropcap\">L<\/span><strong>a recherche d&rsquo;optimisation \u00e9nerg\u00e9tique est un enjeu majeur pour les gestionnaires de parcs de b\u00e2timents. Mais il n\u2019est pas toujours \u00e9vident de d\u00e9gager des informations pertinentes du tr\u00e8s grand nombre de variables \u00e0 leur disposition. Comment traiter cette masse de donn\u00e9es pour identifier les sites mauvais \u00e9l\u00e8ves en mati\u00e8re d\u2019efficacit\u00e9 \u00e9nerg\u00e9tique et faciliter la prise de d\u00e9cision ?<\/strong><\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u00c0 donn\u00e9es simples, interpr\u00e9tation simple<\/strong><\/span><\/h2>\n<p style=\"text-align: left;\"><span style=\"color: #000000;\">Croiser et interpr\u00e9ter des donn\u00e9es n\u2019a rien de sorcier lorsqu\u2019elles sont peu nombreuses. Si les deux seules variables \u00e0 prendre en compte dans l\u2019\u00e9valuation de la performance \u00e9nerg\u00e9tique \u00e9taient la consommation d\u2019\u00e9nergie et la surface, il devient alors possible d\u2019identifier sur un graphique en deux dimensions, les sites \u00e0 cibler en priorit\u00e9 dans le cadre d\u2019une recherche d\u2019\u00e9conomies d\u2019\u00e9nergie, voir <strong>Figure 1<\/strong>.<\/span><\/p>\n<p style=\"text-align: center;\"><span style=\"color: #000000;\"><img decoding=\"async\" class=\"aligncenter wp-image-3836 size-full\" src=\"https:\/\/www.deepki.com\/web\/wp-content\/uploads\/2020\/05\/figure1.png\" alt=\"consommation d'\u00e9lectricit\u00e9 en fonction de la surface\" width=\"944\" height=\"642\" \/><em><span style=\"text-decoration: underline;\"><strong>Figure 1 :<\/strong><\/span> Repr\u00e9sentation de la consommation d\u2019\u00e9nergie en fonction de la surface.<\/em><\/span><\/p>\n<p><span style=\"color: #000000;\">Le cercle vert rep\u00e8re les sites les plus consommateurs. Ce ne sont pas forc\u00e9ment les plus probl\u00e9matiques puisque ce sont aussi les plus grands. En revanche, les sites identifi\u00e9s dans le cercle rouge consomment trois fois plus d\u2019\u00e9lectricit\u00e9 que certains sites de m\u00eame surface. Ce sont donc ces sites que l\u2019on va prioriser.<\/span><\/p>\n<p><span style=\"color: #000000;\">Mais comment d\u00e9gager des tendances pertinentes lorsque le nombre de donn\u00e9es \u00e0 consid\u00e9rer (nombre d\u2019employ\u00e9s, localisation, donn\u00e9es m\u00e9t\u00e9orologiques, type de chauffage, typologie de b\u00e2timent, horaires d\u2019ouverture, etc.) est beaucoup plus \u00e9lev\u00e9 (voir <strong>Figure 2<\/strong>) ? Sans synth\u00e8se de ces informations, il est presque impossible d\u2019identifier facilement ce qui impacte la consommation et donc comment cibler la bonne action d\u2019\u00e9conomie \u00e0 mettre en \u0153uvre.<\/span><\/p>\n<p><span style=\"color: #000000;\"><img decoding=\"async\" class=\"aligncenter wp-image-3837 size-full\" src=\"https:\/\/www.deepki.com\/web\/wp-content\/uploads\/2020\/05\/figure2.png\" alt=\"extrait d'une base de donn\u00e9es\" width=\"1227\" height=\"473\" \/><\/span><\/p>\n<p style=\"text-align: center;\"><span style=\"color: #000000;\"><em><strong><span style=\"text-decoration: underline;\">Figure 2 :<\/span> <\/strong>Extrait d\u2019une base de donn\u00e9es patrimoniales contenant 193 variables et 589 individus<\/em><\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>Le clustering de donn\u00e9es ou l\u2019art de segmenter son parc pour y voir plus clair<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">C\u2019est l\u00e0 qu\u2019intervient le clustering de donn\u00e9es, m\u00e9thode permettant d\u2019analyser l\u2019ensemble des donn\u00e9es propres \u00e0 chaque patrimoine afin d\u2019aboutir \u00e0 une segmentation pertinente des b\u00e2timents. Les typologies qui en r\u00e9sultent permettent de classer les b\u00e2timents entre eux pour identifier les bons et les mauvais \u00e9l\u00e8ves.<\/span><\/p>\n<p><span style=\"text-decoration: underline; color: #000000;\"><em>Exemple avec l\u2019analyse d\u2019un r\u00e9seau de boulangeries :<\/em><\/span><\/p>\n<p><span style=\"color: #000000;\">Le r\u00e9seau contenait 158 sites avec pour chacun 118 variables de natures diverses (la description des \u00e9quipements, nombre d\u2019employ\u00e9s&#8230;). Le clustering a permis de comprendre quelles variables \u00e9taient les plus discriminantes et a permis de distinguer 3 typologies de site.<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>Graphiques obtenus suite au clustering des donn\u00e9es :<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\"><img decoding=\"async\" class=\"aligncenter wp-image-3840 size-full\" src=\"https:\/\/www.deepki.com\/web\/wp-content\/uploads\/2020\/05\/figures3-6-e1509272597562.png\" alt=\"graphiques clustering de donn\u00e9es\" width=\"1485\" height=\"1522\" \/><\/span><\/p>\n<p><span style=\"color: #000000;\">Une des typologies a particuli\u00e8rement attir\u00e9e notre attention. Elle se caract\u00e9risait par la pr\u00e9sence syst\u00e9matique d\u2019un p\u00e9trin (voir <strong>Figure 3<\/strong>), une propension plus grande \u00e0 consommer en heures creuses (voir <strong>Figure 4<\/strong>), des d\u00e9passements plus fr\u00e9quents (voir <strong>Figure 5<\/strong>), et une consommation surfacique d\u2019\u00e9nergie l\u00e9g\u00e8rement sup\u00e9rieure \u00e0 celle des autres classes (voir <strong>Figure 6<\/strong>).<\/span><\/p>\n<p><span style=\"color: #000000;\">En pr\u00e9sentant ces r\u00e9sultats aux \u00e9quipes techniques de l\u2019enseigne, ils ont tout de suite reconnu dans le groupe 3 les sites qu\u2019ils avaient \u00e9quip\u00e9 de nouveaux fours \u00e0 p\u00e9trin. Ces fours, cens\u00e9s \u00eatre plus performants, \u00e9taient en r\u00e9alit\u00e9 mal r\u00e9gul\u00e9s et fonctionnaient toute la journ\u00e9e ce qui menait finalement \u00e0 des d\u00e9passements et une surconsommation d\u2019\u00e9nergie. Ce r\u00e9sultat contre-intuitif (des \u00e9quipements plus performants entrainant une plus grande consommation), a \u00e9t\u00e9 mis en \u00e9vidence par une approche de classification, sans a priori sur les donn\u00e9es. Finalement, un simple rappel du bon fonctionnement de ce nouveau type de four a suffi \u00e0 faire baisser la facture d\u2019\u00e9nergie de ce client.<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>A travers cet exemple, on comprend bien comment la data analytics peut \u00eatre mise au service de l\u2019efficacit\u00e9 \u00e9nerg\u00e9tique. Ici, le clustring a permis de d\u00e9gager facilement les tendances d\u2019une base de donn\u00e9es en simplifiant les termes d\u2019un probl\u00e8me initialement complexe. On peut alors obtenir une vision plus claire et d\u00e9terminer o\u00f9 se cachent les \u00e9conomies d\u2019\u00e9nergie et comment les mettre en \u0153uvre.<\/strong><\/span><\/p>\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>La recherche d&rsquo;optimisation \u00e9nerg\u00e9tique est un enjeu majeur pour les gestionnaires de parcs de b\u00e2timents. Mais il n\u2019est pas toujours \u00e9vident de d\u00e9gager des informations pertinentes du tr\u00e8s grand nombre de variables \u00e0 leur disposition. Comment traiter cette masse de donn\u00e9es pour identifier les sites mauvais \u00e9l\u00e8ves en mati\u00e8re d\u2019efficacit\u00e9 \u00e9nerg\u00e9tique et faciliter la prise [&hellip;]<\/p>\n","protected":false},"author":44,"featured_media":75846,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[118,120],"tags":[],"class_list":["post-363","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-fr","category-performance-environnementale"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Le clustering, ou comment ne pas se laisser d\u00e9border par les donn\u00e9es - Deepki<\/title>\n<meta name=\"description\" content=\"Il n\u2019est pas toujours \u00e9vident de d\u00e9gager des informations pertinentes du tr\u00e8s grand nombre de variables \u00e0 leur disposition. 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