{"id":3066,"date":"2023-11-07T11:32:00","date_gmt":"2023-11-07T10:32:00","guid":{"rendered":"https:\/\/frogcast.com\/?p=3066"},"modified":"2026-02-13T11:08:14","modified_gmt":"2026-02-13T10:08:14","slug":"reseau-de-chaleur-compagnie-de-chauffage-de-grenoble","status":"publish","type":"post","link":"https:\/\/frogcast.com\/fr\/blog\/energy\/heating-network-grenoble-district-heating-company\/","title":{"rendered":"\u00c9tude de cas \u2013 CCIAG Grenoble : Am\u00e9liorer la performance \u00e9conomique et environnementale gr\u00e2ce \u00e0 des pr\u00e9visions de temp\u00e9rature plus pr\u00e9cises"},"content":{"rendered":"<h2 class=\"wp-block-heading\">Besoins du client<\/h2>\n\n\n\n<p>En tant qu'op\u00e9rateur public, <a href=\"https:\/\/www.compagniedechauffage.fr\/\" target=\"_blank\" rel=\"noreferrer noopener\">la Compagnie de Chauffage Intercommunale de l'Agglom\u00e9ration Grenobloise (CCIAG)<\/a> g\u00e8re le r\u00e9seau de chaleur de Grenoble-Alpes M\u00e9tropole. Ce vaste r\u00e9seau de chauffage urbain s'\u00e9tend sur plus de 180 kilom\u00e8tres et se classe comme le deuxi\u00e8me plus grand de France. Il fournit le chauffage et l'eau chaude sanitaire aux b\u00e2timents publics et priv\u00e9s, notamment les ensembles r\u00e9sidentiels et les bureaux. Au total, il produit une chaleur \u00e9quivalente \u00e0 celle de 100 000 logements, avec des ventes d'\u00e9nergie annuelles moyennes de 800 GWh. La CCIAG exploite 5 sites de production et s'appuie sur 10 types de combustibles diff\u00e9rents.<\/p>\n\n\n\n<p>La CCIAG choisit ses combustibles dans l'objectif de ma\u00eetriser sa consommation d'\u00e9nergie et de r\u00e9duire ses \u00e9missions de gaz \u00e0 effet de serre et pollution atmosph\u00e9rique. L'entreprise travaille activement \u00e0 r\u00e9duire progressivement l'utilisation du charbon, qui repr\u00e9sentait encore 12 % du mix \u00e9nerg\u00e9tique en 2022. Chaque site de production a ses propres contraintes sp\u00e9cifiques, mais la CCIAG pr\u00e9voit d'\u00e9liminer compl\u00e8tement le charbon d'ici 2026.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1000\" height=\"500\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/renewables_energies_an_recovery.webp\" alt=\"Diagramme circulaire illustrant le mix \u00e9nerg\u00e9tique 2021-2022 du r\u00e9seau de chauffage urbain de Grenoble, mettant en \u00e9vidence 80,1 % d&#039;\u00e9nergies renouvelables et de valorisation (d\u00e9chets, bois, biogaz).\" class=\"wp-image-3070\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/renewables_energies_an_recovery.webp 1000w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/renewables_energies_an_recovery-300x150.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/renewables_energies_an_recovery-768x384.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/renewables_energies_an_recovery-18x9.webp 18w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><figcaption class=\"wp-element-caption\">Figure 1 \u2013 Avec plus de 80 % de l\u2019\u00e9nergie provenant de sources renouvelables, des pr\u00e9visions de temp\u00e9rature pr\u00e9cises sont essentielles pour optimiser l\u2019utilisation de la biomasse et de l\u2019incin\u00e9ration des d\u00e9chets par rapport aux combustibles fossiles de secours.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Mix \u00e9nerg\u00e9tique et choix op\u00e9rationnels<\/h3>\n\n\n\n<p>Les types et proportions de chaque combustible sont choisis selon des crit\u00e8res techniques, \u00e9conomiques et environnementaux. La CCIAG ajuste r\u00e9guli\u00e8rement ces d\u00e9cisions, notamment lors des variations importantes des prix des combustibles. Le stockage thermique est utilis\u00e9 pour apporter de la flexibilit\u00e9, aide \u00e0 r\u00e9guler la production et r\u00e9duit l'utilisation des chaudi\u00e8res d'appoint.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Importance de pr\u00e9visions de temp\u00e9rature pr\u00e9cises<\/h3>\n\n\n\n<p>Pour ajuster sa production d'\u00e9nergie et d\u00e9finir l'utilisation optimale de ses ressources de production, la CCIAG doit anticiper les besoins en chaleur. La temp\u00e9rature ext\u00e9rieure influence directement la demande de chaleur, donc des pr\u00e9visions de temp\u00e9rature pr\u00e9cises sont essentielles. L'entreprise vise une erreur moyenne inf\u00e9rieure \u00e0 1,2 \u00b0C pendant la saison hivernale, alors que les syst\u00e8mes conventionnels offrent g\u00e9n\u00e9ralement une pr\u00e9cision d'environ 2 \u00b0C. FROGCAST fournit \u00e0 la CCIAG des pr\u00e9visions de temp\u00e9rature quatre fois par jour, jusqu'\u00e0 quatorze jours \u00e0 l'avance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Description de la solution de pr\u00e9vision<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Un environnement g\u00e9ographique complexe<\/h3>\n\n\n\n<p>La topographie unique de Grenoble en forme de cuvette rend les pr\u00e9visions de temp\u00e9rature difficiles. Les mod\u00e8les m\u00e9t\u00e9orologiques pr\u00e9sentent des biais syst\u00e9matiques et sp\u00e9cifiques \u00e0 chaque situation. Par exemple, les inversions de temp\u00e9rature sont fr\u00e9quentes : l'air froid se retrouve pi\u00e9g\u00e9 au fond de la vall\u00e9e tandis que l'air plus chaud se situe au-dessus. Ces inversions se produisent souvent dans des conditions anticycloniques avec des nuages bas persistants, que de nombreux mod\u00e8les ont du mal \u00e0 repr\u00e9senter.<\/p>\n\n\n\n<p>Les sorties brutes des mod\u00e8les m\u00e9t\u00e9orologiques ne r\u00e9pondent pas aux exigences de pr\u00e9cision de la CCIAG. Pour la plupart des mod\u00e8les, les erreurs absolues moyennes (MAE<sup data-fn=\"077cefe7-a2fc-48c7-9e48-801100e3b6a2\" class=\"fn\"><a id=\"077cefe7-a2fc-48c7-9e48-801100e3b6a2-link\" href=\"#077cefe7-a2fc-48c7-9e48-801100e3b6a2\">1<\/a><\/sup>) d\u00e9passaient 2 \u00b0C pendant les premi\u00e8res 24 heures de la pr\u00e9vision (hiver 2019-2022). Steadysun a cr\u00e9\u00e9 une solution personnalis\u00e9e pour r\u00e9duire ces erreurs, en se concentrant sur la diminution de l'erreur moyenne saisonni\u00e8re et la r\u00e9duction des occurrences d'erreurs tr\u00e8s importantes, en particulier celles sup\u00e9rieures \u00e0 2 \u00b0C.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_slider-1024x576.webp\" alt=\"Carte de la r\u00e9gion de Grenoble avec les zones du r\u00e9seau de chaleur mises en \u00e9vidence, illustrant le projet de chauffage urbain de la CCIAG.\" class=\"wp-image-3055\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_slider-1024x576.webp 1024w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_slider-300x169.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_slider-768x432.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_slider-1536x864.webp 1536w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_slider-18x10.webp 18w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_slider.webp 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Pr\u00e9cision pour les services publics. La compagnie de chauffage urbain de Grenoble (CCIAG) s'appuie sur FROGCAST pour atteindre une pr\u00e9cision de pr\u00e9vision d'environ 2 \u00b0C, optimisant la production et la distribution de chaleur.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Pr\u00e9vision multi-mod\u00e8les<\/h3>\n\n\n\n<p>La plateforme FROGCAST utilise les pr\u00e9visions des principaux mod\u00e8les m\u00e9t\u00e9orologiques mondiaux :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/meteofrance.com\/actualites-et-dossiers\/modeles-prevision-meteo\" target=\"_blank\" rel=\"noreferrer noopener\">M\u00e9t\u00e9o-France<\/a> (AROME, ARPEGE),<\/li>\n\n\n\n<li>German <a href=\"https:\/\/www.dwd.de\/EN\/ourservices\/nwp_forecast_data\/nwp_forecast_data.html\" target=\"_blank\" rel=\"noreferrer noopener\">DWD<\/a> (ICON-D2, ICON-EU),<\/li>\n\n\n\n<li>Administration nationale oc\u00e9anique et atmosph\u00e9rique am\u00e9ricaine \u2013 <a href=\"https:\/\/mag.ncep.noaa.gov\/model-guidance-model-area.php\" target=\"_blank\" rel=\"noreferrer noopener\">NOAA<\/a> (GFS, GEFS),<\/li>\n\n\n\n<li>Centre europ\u00e9en pour les pr\u00e9visions m\u00e9t\u00e9orologiques \u00e0 moyen terme \u2013 <a href=\"https:\/\/www.ecmwf.int\/en\/forecasts\" target=\"_blank\" rel=\"noreferrer noopener\">ECMWF<\/a> (IFS-HRES),<\/li>\n\n\n\n<li>et le service m\u00e9t\u00e9orologique Canadien (<a href=\"https:\/\/open.canada.ca\/data\/en\/dataset\/c041e79a-914a-5a4e-a485-9cbc506195df\" target=\"_blank\" rel=\"noreferrer noopener\">GDPS<\/a>).<\/li>\n<\/ul>\n\n\n\n<p>Ces sources d'information sont combin\u00e9es de mani\u00e8re optimale sur chaque point de grille du globe. Cette approche multi-mod\u00e8les r\u00e9duit les erreurs importantes en accordant plus de poids au sc\u00e9nario m\u00e9t\u00e9orologique le plus coh\u00e9rent, celui partag\u00e9 par la majorit\u00e9 des mod\u00e8les.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Post-traitement et correction par analogie<\/h3>\n\n\n\n<p>La CCIAG utilise un capteur de temp\u00e9rature pr\u00e8s de son site de production de la Poterne. Ces mesures locales constituent une source d'information essentielle, car elles permettent d'\u00e9valuer la qualit\u00e9 des pr\u00e9visions sur une p\u00e9riode prolong\u00e9e et de d\u00e9velopper des outils de post-traitement avanc\u00e9s. <\/p>\n\n\n\n<p>La m\u00e9thode choisie repose sur le principe d'analogie (Figure 2). Elle n\u00e9cessite l'acc\u00e8s \u00e0 des archives de pr\u00e9visions m\u00e9t\u00e9orologiques et \u00e0 des r\u00e9analyses atmosph\u00e9riques \u00e0 l'\u00e9chelle continentale pour certains param\u00e8tres (g\u00e9opotentiel, temp\u00e9rature, humidit\u00e9 relative, etc.), ainsi qu'aux pr\u00e9visions locales pour la variable corrig\u00e9e. Pour chaque nouvelle pr\u00e9vision, la situation m\u00e9t\u00e9orologique est analys\u00e9e et d\u00e9crite \u00e0 l'aide de champs m\u00e9t\u00e9orologiques \u00e0 grande \u00e9chelle appel\u00e9s pr\u00e9dicteurs. Des jours pr\u00e9sentant des conditions m\u00e9t\u00e9orologiques similaires sont s\u00e9lectionn\u00e9s. La m\u00e9thode s'appuie ensuite sur l'hypoth\u00e8se que pour des conditions m\u00e9t\u00e9orologiques similaires, les mod\u00e8les auront des biais similaires. Cela permet d'anticiper les erreurs de la pr\u00e9vision actuelle et de les corriger.<\/p>\n\n\n\n<p>Cette m\u00e9thode de post-traitement offre 2 avantages majeurs :<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>elle s'adapte \u00e0 la situation m\u00e9t\u00e9orologique<\/li>\n\n\n\n<li>elle s'am\u00e9liore au fil des ann\u00e9es \u00e0 mesure que la profondeur des archives augmente<\/li>\n<\/ol>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1000\" height=\"625\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_temperature_forecasts.webp\" alt=\"Sch\u00e9ma technique illustrant la m\u00e9thodologie de \u00ab post-traitement par analogie \u00bb : comparaison des champs atmosph\u00e9riques actuels avec les archives historiques pour corriger les pr\u00e9visions de temp\u00e9rature.\" class=\"wp-image-3069\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_temperature_forecasts.webp 1000w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_temperature_forecasts-300x188.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_temperature_forecasts-768x480.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_temperature_forecasts-18x12.webp 18w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><figcaption class=\"wp-element-caption\">Figure 2 \u2013 Innovation en mati\u00e8re de correction. Notre algorithme compare la pr\u00e9vision d'aujourd'hui avec des archives historiques (analogues) pour identifier et corriger les biais syst\u00e9matiques caus\u00e9s par la topographie complexe de la vall\u00e9e de Grenoble.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">R\u00e9sultats<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Am\u00e9lioration de la pr\u00e9cision des pr\u00e9visions<\/h3>\n\n\n\n<p>La pr\u00e9vision multi-mod\u00e8les FROGCAST, coupl\u00e9e \u00e0 la m\u00e9thode de post-traitement par analogie, est op\u00e9rationnelle \u00e0 la Compagnie de Chauffage depuis l'hiver 2021-2022. De plus, les archives m\u00e9t\u00e9orologiques ont permis d'\u00e9tendre l'\u00e9valuation de la m\u00e9thode sur deux hivers suppl\u00e9mentaires.<\/p>\n\n\n\n<p>La Figure 3 montre un exemple de pr\u00e9vision \u00e0 15 jours fournie \u00e0 la CCIAG. La courbe sup\u00e9rieure repr\u00e9sente la pr\u00e9vision FROGCAST brute, et la courbe inf\u00e9rieure montre la version corrig\u00e9e utilisant le post-traitement par analogie. L'intervalle de confiance P20-P80 est construit en utilisant les diverses corrections fournies par les jours analogues individuels. Il est remarquable que la pr\u00e9vision brute multi-mod\u00e8les simule relativement bien la temp\u00e9rature \u00e0 Grenoble, avec une MAE de 1,46 \u00b0C pour les trois premiers jours de la pr\u00e9vision. Cependant, certains jours pr\u00e9sentent des erreurs importantes, comme le 27 novembre (&gt; 3 \u00b0C). L'application de la correction par analogie am\u00e9liore consid\u00e9rablement la pr\u00e9vision en r\u00e9duisant ces erreurs. Nous obtenons une r\u00e9duction d'environ 0,74 \u00b0C de la MAE entre J+0 et J+3 et une r\u00e9duction de 0,6 \u00b0C pour une pr\u00e9vision \u00e0 J+15, ce qui correspond \u00e0 des am\u00e9liorations de 51 % et 34 % respectivement. <\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"500\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_raw_forecast.webp\" alt=\"Graphiques lin\u00e9aires de comparaison montrant les pr\u00e9visions brutes du mod\u00e8le m\u00e9t\u00e9orologique (en haut) par rapport aux pr\u00e9visions corrig\u00e9es de FROGCAST (en bas), d\u00e9montrant une r\u00e9duction de l&#039;erreur absolue moyenne (MAE) de 1,46\u00b0C \u00e0 0,70\u00b0C.\" class=\"wp-image-3068\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_raw_forecast.webp 1000w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_raw_forecast-300x150.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_raw_forecast-768x384.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_raw_forecast-18x9.webp 18w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><figcaption class=\"wp-element-caption\">Figure 3 \u2013 Visualiser l'impact. Le graphique sup\u00e9rieur montre l'instabilit\u00e9 du mod\u00e8le brut ; le graphique inf\u00e9rieur montre la correction FROGCAST, r\u00e9duisant l'erreur absolue moyenne (MAE) de plus de 50 % pour le r\u00e9seau de chauffage urbain.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Performance \u00e0 long terme<\/h3>\n\n\n\n<p>L'am\u00e9lioration de la performance a \u00e9t\u00e9 \u00e9valu\u00e9e quantitativement sur quatre hivers depuis 2019. Les erreurs (MAE) dans les premi\u00e8res 24 heures des pr\u00e9visions sont pr\u00e9sent\u00e9es dans la Figure 4 sous forme de fonction de densit\u00e9 de probabilit\u00e9. L'application du post-traitement par analogie r\u00e9duit non seulement l'erreur moyenne de 2,1 \u00b0C \u00e0 1,2 \u00b0C, mais diminue \u00e9galement l'occurrence d'erreurs tr\u00e8s importantes (&gt; 3 \u00b0C), de 10 % \u00e0 moins de 3 %. Ces erreurs tr\u00e8s significatives, particuli\u00e8rement probl\u00e9matiques pour la gestion des r\u00e9seaux de chauffage urbain, ont ainsi \u00e9t\u00e9 substantiellement r\u00e9duites, r\u00e9pondant aux attentes de la CCIAG concernant les erreurs moyennes maximales.<\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:16%\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:68%\">\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"632\" height=\"625\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_raw_forecast1-e1765372439489.webp\" alt=\"Graphique de la fonction de densit\u00e9 de probabilit\u00e9 (PDF) comparant les erreurs de pr\u00e9vision. La courbe corrig\u00e9e (rouge) est plus pointue et centr\u00e9e sur z\u00e9ro, ce qui indique une fiabilit\u00e9 sup\u00e9rieure \u00e0 celle de la courbe brute (noire).\" class=\"wp-image-3067\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_raw_forecast1-e1765372439489.webp 632w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_raw_forecast1-e1765372439489-300x297.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/heating_network_corrected_raw_forecast1-e1765372439489-12x12.webp 12w\" sizes=\"(max-width: 632px) 100vw, 632px\" \/><figcaption class=\"wp-element-caption\">Figure 4 \u2013 \u00c9liminer les erreurs extr\u00eames. La courbe rouge montre que notre post-traitement ne se contente pas de r\u00e9duire l'erreur moyenne ; il diminue drastiquement les \u00ab erreurs importantes \u00bb (&gt; 3 \u00b0C), qui sont critiques pour la s\u00e9curit\u00e9 du r\u00e9seau.<\/figcaption><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:16%\"><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Perspectives<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Am\u00e9lioration continue<\/h3>\n\n\n\n<p>Comme mentionn\u00e9 pr\u00e9c\u00e9demment, l'un des avantages de la m\u00e9thode par analogie est son enrichissement automatique au fil du temps avec l'augmentation de la profondeur des archives m\u00e9t\u00e9orologiques disponibles. Par cons\u00e9quent, nous pouvons nous attendre \u00e0 une am\u00e9lioration constante de la qualit\u00e9 des pr\u00e9visions au fil des ann\u00e9es.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Int\u00e9gration de nouvelles sources de donn\u00e9es<\/h3>\n\n\n\n<p>D'autres sources de donn\u00e9es m\u00e9t\u00e9orologiques peuvent \u00e9galement \u00eatre int\u00e9gr\u00e9es dans le syst\u00e8me de pr\u00e9vision actuel. L'une de ces sources est le produit de pr\u00e9vision imm\u00e9diate du mod\u00e8le AROME, avec des donn\u00e9es mises \u00e0 jour toutes les 3 heures, fournissant des pr\u00e9visions affin\u00e9es pour les 6 prochaines heures. L'assimilation d'un grand nombre de donn\u00e9es d'observation dans ce syst\u00e8me (stations au sol, radar, etc.) r\u00e9duit consid\u00e9rablement les biais et pourrait apporter des am\u00e9liorations significatives aux performances des pr\u00e9visions \u00e0 tr\u00e8s court terme.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Potentiel de d\u00e9ploiement d'un r\u00e9seau de capteurs<\/h3>\n\n\n\n<p>Le d\u00e9ploiement d'un r\u00e9seau de capteurs autour du bassin grenoblois, \u00e0 la fois dans la vall\u00e9e et en altitude, pourrait affiner les pr\u00e9visions et d\u00e9velopper des outils suppl\u00e9mentaires. Les ph\u00e9nom\u00e8nes locaux, tels que le passage d'averses ou l'apparition du vent du sud (f\u0153hn), impliquent des variations soudaines de temp\u00e9rature pouvant atteindre dix degr\u00e9s en quelques minutes. Un syst\u00e8me d'alerte bas\u00e9 sur des mesures prises \u00e0 quelques kilom\u00e8tres de Grenoble pourrait fournir une pr\u00e9vision pr\u00e9cise du moment de ces fluctuations rapides quelques minutes \u00e0 l'avance.<\/p>\n\n\n<ol class=\"has-white-color has-text-color has-background has-link-color wp-block-footnotes\"><li id=\"077cefe7-a2fc-48c7-9e48-801100e3b6a2\"><em>L'erreur absolue moyenne normalis\u00e9e (nMAE) est calcul\u00e9e par intervalles de demi-heure. Cela est coh\u00e9rent avec le fonctionnement habituel du r\u00e9seau \u00e9lectrique. Pour quantifier les pr\u00e9visions ou la production quotidienne, nous disposons par cons\u00e9quent d'un ensemble de 48 valeurs correspondant \u00e0 48 cr\u00e9neaux horaires de 30 minutes chacun (par exemple, la 1\u00e8re valeur est la puissance moyenne entre 00h00 et 00h30, la 2e valeur entre 00h30 et 01h00, et ainsi de suite).<\/em><br><br><em>Les puissances sont normalis\u00e9es par la puissance de cr\u00eate afin de permettre des comparaisons entre une centrale et une autre.<\/em><br><br><em> <\/em><math data-latex=\"nMAE_{\\frac{1}{2}h} = \\left| \\frac{P_{forecasted} - P_{produced}}{P_{peak}} \\right|\"><semantics><mrow><mi>n<\/mi><mi>M<\/mi><mi>A<\/mi><msub><mi>E<\/mi><mrow><mfrac><mn>1<\/mn><mn>2<\/mn><\/mfrac><mi>h<\/mi><\/mrow><\/msub><mo>=<\/mo><mrow><mo fence=\"true\" form=\"prefix\">|<\/mo><mfrac><mrow><msub><mi>P<\/mi><mrow><mi>f<\/mi><mi>o<\/mi><mi>r<\/mi><mi>e<\/mi><mi>c<\/mi><mi>a<\/mi><mi>s<\/mi><mi>t<\/mi><mi>e<\/mi><mi>d<\/mi><\/mrow><\/msub><mo>\u2212<\/mo><msub><mi>P<\/mi><mrow><mi>p<\/mi><mi>r<\/mi><mi>o<\/mi><mi>d<\/mi><mi>u<\/mi><mi>c<\/mi><mi>e<\/mi><mi>d<\/mi><\/mrow><\/msub><\/mrow><msub><mi>P<\/mi><mrow><mi>p<\/mi><mi>e<\/mi><mi>a<\/mi><mi>k<\/mi><\/mrow><\/msub><\/mfrac><mo fence=\"true\" form=\"postfix\">|<\/mo><\/mrow><\/mrow><annotation encoding=\"application\/x-tex\">nMAE_{\\frac{1}{2}h} = \\left| \\frac{P_{forecasted} &#8211; P_{produced}}{P_{peak}} \\right|<\/annotation><\/semantics><\/math><br><br><em>Une valeur nMAE quotidienne est ensuite calcul\u00e9e comme la moyenne des 48 erreurs de demi-heure pour chaque jour et pour chaque ann\u00e9e :<\/em><br><br><math data-latex=\"nMAE_{day} = (\\frac{1}{48} \\sum_{1}^{48} MAE_{\\frac{1}{2}h})\"><semantics><mrow><mi>n<\/mi><mi>M<\/mi><mi>A<\/mi><msub><mi>E<\/mi><mrow><mi>d<\/mi><mi>a<\/mi><mi>y<\/mi><\/mrow><\/msub><mo>=<\/mo><mo form=\"prefix\" stretchy=\"false\">(<\/mo><mfrac><mn>1<\/mn><mn>48<\/mn><\/mfrac><msubsup><mo movablelimits=\"false\">\u2211<\/mo><mn>1<\/mn><mn>48<\/mn><\/msubsup><mi>M<\/mi><mi>A<\/mi><msub><mi>E<\/mi><mrow><mfrac><mn>1<\/mn><mn>2<\/mn><\/mfrac><mi>h<\/mi><\/mrow><\/msub><mo form=\"postfix\" stretchy=\"false\">)<\/mo><\/mrow><annotation encoding=\"application\/x-tex\">nMAE_{day} = (\\frac{1}{48} \\sum_{1}^{48} MAE_{\\frac{1}{2}h})<\/annotation><\/semantics><\/math><br><br><em>O\u00f9 <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><msub><mi>P<\/mi><mtext>forecasted<\/mtext><\/msub><\/mrow><annotation encoding=\"application\/x-tex\">P_{\\text{forecasted}}<\/annotation><\/semantics><\/math> et <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><msub><mi>P<\/mi><mtext>produced<\/mtext><\/msub><\/mrow><annotation encoding=\"application\/x-tex\">P_{\\text{produced}}<\/annotation><\/semantics><\/math> sont respectivement la puissance moyenne planifi\u00e9e et la puissance r\u00e9elle pendant le cr\u00e9neau de 30 minutes, <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><msub><mi>P<\/mi><mtext>peak<\/mtext><\/msub><\/mrow><annotation encoding=\"application\/x-tex\">P_{\\text{peak}}<\/annotation><\/semantics><\/math> est la puissance de cr\u00eate, et \u00ab 48 \u00bb repr\u00e9sente le nombre de cr\u00e9neaux de demi-heure par jour.<\/em><br><\/br> Enfin, la pr\u00e9cision globale du syst\u00e8me de pr\u00e9vision pour une centrale est estim\u00e9e en moyennant les valeurs nMAE quotidiennes sur tous les jours disponibles :<br> <br><math data-latex=\"nMAE = \\frac{\\sum nMAE_{day}}{N_{day}}\"><semantics><mrow><mi>n<\/mi><mi>M<\/mi><mi>A<\/mi><mi>E<\/mi><mo>=<\/mo><mfrac><mrow><mo movablelimits=\"false\" lspace=\"0em\" rspace=\"0em\">\u2211<\/mo><mi>n<\/mi><mi>M<\/mi><mi>A<\/mi><msub><mi>E<\/mi><mrow><mi>d<\/mi><mi>a<\/mi><mi>y<\/mi><\/mrow><\/msub><\/mrow><msub><mi>N<\/mi><mrow><mi>d<\/mi><mi>a<\/mi><mi>y<\/mi><\/mrow><\/msub><\/mfrac><\/mrow><annotation encoding=\"application\/x-tex\">nMAE = \\frac{\\sum nMAE_{day}}{N_{day}}<\/annotation><\/semantics><\/math> <a href=\"#077cefe7-a2fc-48c7-9e48-801100e3b6a2-link\" aria-label=\"Aller \u00e0 la note de bas de page 1\">\u21a9\ufe0e<\/a><\/li><\/ol>","protected":false},"excerpt":{"rendered":"<p>FROGCAST am\u00e9liore la gestion du chauffage urbain de la CCIAG gr\u00e2ce \u00e0 des pr\u00e9visions de temp\u00e9rature multi-mod\u00e8les avanc\u00e9es et des corrections bas\u00e9es sur l'analogie pour une pr\u00e9cision in\u00e9gal\u00e9e.<\/p>","protected":false},"author":2,"featured_media":3055,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"[{\"content\":\"<em>The normalized Mean Absolute Error (nMAE) is calculated using half-hour intervals. This is consistent with the usual functioning of the electricity network. To quantify the forecasts or the daily production, we consequently have a set of 48 values corresponding to 48 time slots of 30 minutes each (e.g., the 1rst value is the average power between 00.00 am and 00.30 am, the 2nd value between 00.30 am and 01.00 am, and so forth).<\/em><br><br><em>Powers are normalized by the peak power in order to allow comparisons between a power plant and another one.<\/em><br><br><em> <\/em><math data-latex=\\\"nMAE_{\\\\frac{1}{2}h} = \\\\left| \\\\frac{P_{forecasted} - P_{produced}}{P_{peak}} \\\\right|\\\"><semantics><mrow><mi>n<\/mi><mi>M<\/mi><mi>A<\/mi><msub><mi>E<\/mi><mrow><mfrac><mn>1<\/mn><mn>2<\/mn><\/mfrac><mi>h<\/mi><\/mrow><\/msub><mo>=<\/mo><mrow><mo fence=\\\"true\\\" form=\\\"prefix\\\">|<\/mo><mfrac><mrow><msub><mi>P<\/mi><mrow><mi>f<\/mi><mi>o<\/mi><mi>r<\/mi><mi>e<\/mi><mi>c<\/mi><mi>a<\/mi><mi>s<\/mi><mi>t<\/mi><mi>e<\/mi><mi>d<\/mi><\/mrow><\/msub><mo>\u2212<\/mo><msub><mi>P<\/mi><mrow><mi>p<\/mi><mi>r<\/mi><mi>o<\/mi><mi>d<\/mi><mi>u<\/mi><mi>c<\/mi><mi>e<\/mi><mi>d<\/mi><\/mrow><\/msub><\/mrow><msub><mi>P<\/mi><mrow><mi>p<\/mi><mi>e<\/mi><mi>a<\/mi><mi>k<\/mi><\/mrow><\/msub><\/mfrac><mo fence=\\\"true\\\" form=\\\"postfix\\\">|<\/mo><\/mrow><\/mrow><annotation encoding=\\\"application\/x-tex\\\">nMAE_{\\\\frac{1}{2}h} = \\\\left| \\\\frac{P_{forecasted} - P_{produced}}{P_{peak}} \\\\right|<\/annotation><\/semantics><\/math><br><br><em>A daily nMAE value is then calculated as the average of the 48 half-hour errors for each day and for each year:<\/em><br><br><math data-latex=\\\"nMAE_{day} = (\\\\frac{1}{48} \\\\sum_{1}^{48} MAE_{\\\\frac{1}{2}h})\\\"><semantics><mrow><mi>n<\/mi><mi>M<\/mi><mi>A<\/mi><msub><mi>E<\/mi><mrow><mi>d<\/mi><mi>a<\/mi><mi>y<\/mi><\/mrow><\/msub><mo>=<\/mo><mo form=\\\"prefix\\\" stretchy=\\\"false\\\">(<\/mo><mfrac><mn>1<\/mn><mn>48<\/mn><\/mfrac><msubsup><mo movablelimits=\\\"false\\\">\u2211<\/mo><mn>1<\/mn><mn>48<\/mn><\/msubsup><mi>M<\/mi><mi>A<\/mi><msub><mi>E<\/mi><mrow><mfrac><mn>1<\/mn><mn>2<\/mn><\/mfrac><mi>h<\/mi><\/mrow><\/msub><mo form=\\\"postfix\\\" stretchy=\\\"false\\\">)<\/mo><\/mrow><annotation encoding=\\\"application\/x-tex\\\">nMAE_{day} = (\\\\frac{1}{48} \\\\sum_{1}^{48} MAE_{\\\\frac{1}{2}h})<\/annotation><\/semantics><\/math><br><br><em>Where <math xmlns=\\\"http:\/\/www.w3.org\/1998\/Math\/MathML\\\"><semantics><mrow><msub><mi>P<\/mi><mtext>forecasted<\/mtext><\/msub><\/mrow><annotation encoding=\\\"application\/x-tex\\\">P_{\\\\text{forecasted}}<\/annotation><\/semantics><\/math> and <math xmlns=\\\"http:\/\/www.w3.org\/1998\/Math\/MathML\\\"><semantics><mrow><msub><mi>P<\/mi><mtext>produced<\/mtext><\/msub><\/mrow><annotation encoding=\\\"application\/x-tex\\\">P_{\\\\text{produced}}<\/annotation><\/semantics><\/math> are the average power planned and actual power during the 30-minute time slot, <math xmlns=\\\"http:\/\/www.w3.org\/1998\/Math\/MathML\\\"><semantics><mrow><msub><mi>P<\/mi><mtext>peak<\/mtext><\/msub><\/mrow><annotation encoding=\\\"application\/x-tex\\\">P_{\\\\text{peak}}<\/annotation><\/semantics><\/math> is the peak power, and \\\"48\\\" represents the number of half-hour slots per day.<\/em><br>Finally, the overall forecast system accuracy for a power plant is estimated by averaging the daily nMAE values over all available days:<br> <br><math data-latex=\\\"nMAE = \\\\frac{\\\\sum nMAE_{day}}{N_{day}}\\\"><semantics><mrow><mi>n<\/mi><mi>M<\/mi><mi>A<\/mi><mi>E<\/mi><mo>=<\/mo><mfrac><mrow><mo movablelimits=\\\"false\\\" lspace=\\\"0em\\\" rspace=\\\"0em\\\">\u2211<\/mo><mi>n<\/mi><mi>M<\/mi><mi>A<\/mi><msub><mi>E<\/mi><mrow><mi>d<\/mi><mi>a<\/mi><mi>y<\/mi><\/mrow><\/msub><\/mrow><msub><mi>N<\/mi><mrow><mi>d<\/mi><mi>a<\/mi><mi>y<\/mi><\/mrow><\/msub><\/mfrac><\/mrow><annotation encoding=\\\"application\/x-tex\\\">nMAE = \\\\frac{\\\\sum nMAE_{day}}{N_{day}}<\/annotation><\/semantics><\/math>\",\"id\":\"077cefe7-a2fc-48c7-9e48-801100e3b6a2\"}]"},"categories":[29],"tags":[],"class_list":["post-3066","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-energy"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Case study CCIAG \u2013 Optimizing district heating | FROGCAST<\/title>\n<meta name=\"description\" content=\"CCIAG Grenoble improves energy efficiency and reduces emissions with FROGCAST&#039;s accurate temperature forecasts and analogy-based corrections.\" \/>\n<meta name=\"robots\" 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