{"id":36122,"date":"2023-08-10T12:35:26","date_gmt":"2023-08-10T12:35:26","guid":{"rendered":"https:\/\/beacontower.io\/cases\/race\/"},"modified":"2023-08-11T07:54:54","modified_gmt":"2023-08-11T07:54:54","slug":"race","status":"publish","type":"page","link":"https:\/\/beacontower.io\/da\/cases\/race\/","title":{"rendered":"RACE"},"content":{"rendered":"

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Case<\/h4>\n

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RACE<\/h1>\n

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Anvendelse af AI og real-time computing i energisektoren<\/p>\n

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Goal<\/h4>\n

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Real-Time AI Computing i Energisektoren<\/h2>\n

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Glaze bruger Beacon Tower sammen med Energy Cluster Denmark, Develco, Br\u00f8nderslev Forsyning A\/S, Agerkrantz Controls ApS og Aalborg Universitet til at udvikle n\u00e6ste generation af fjernvarmeoptimeringssystemer. Projektet hedder RACE (R<\/strong>eal-Time A<\/strong>I C<\/strong>omputing in the E<\/strong>nergy Sector) og har fokus p\u00e5 \u00e5bne datamodeller, fleksible og l\u00f8st-koblede l\u00f8sningsmoduler samt en Open Source tilgang.<\/p>\n

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Overblik<\/h5>\n

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Energiforbrug i bygninger udg\u00f8r n\u00e6sten 40% af det samlede energiforbrug i Danmark. Energien bruges prim\u00e6rt til opvarmning, ventilation og belysning. Optimering af fjernvarmenet er \u00e9n af mulighederne for at opn\u00e5 energibesparelser p\u00e5 dette omr\u00e5de. Virkningen ved at s\u00e6nke returtemperaturen med kun 1 grad Celsius sparer energi til 10.000 huse eller 9.500 ton CO2. At energieffektivisere energiforbrug i og til bygninger spiller en afg\u00f8rende rolle for, at vi kan n\u00e5 det politiske m\u00e5l om at v\u00e6re uafh\u00e6ngig af fossile br\u00e6ndstoffer i 2050 og n\u00e5 70% m\u00e5let i 2030.<\/p>\n

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Baggrund<\/span><\/h3>\n

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I dag er udfordringen i fjernvarmesektoren, at data kun kan anvendes i isolerede siloer eller dedikerede systemer, der ikke er forberedt til dataudveksling, som er n\u00f8dvendig for at dele data og udnytte Machine Learning mellem systemer. Eksisterende leverand\u00f8rer i fjernvarmesektoren anvender i dag propriet\u00e6re l\u00f8sninger med leverand\u00f8r lock-in, hvilket reducerer den fleksibilitet og systemkobling, som er n\u00f8dvendig. Dette er en barriere for opn\u00e5else af energieffektivitet og energibesparelser i hele fjernvarmesystemet, da opn\u00e5else af det n\u00e6ste niveau af optimeringer kr\u00e6ver realtidsdataanalyser.<\/span><\/p>\n


Realtidsdata, cloud computing, Machine Learning og AI-kontrol muligg\u00f8r realisering af den n\u00e6ste generation af fjernvarmesystemer, som har v\u00e6ret meget efterspurgt i de senere \u00e5r. At vide, hvordan man skal anvende disse teknologier p\u00e5 en fleksibel, sikker og s\u00e5ledes skal\u00e9rbar m\u00e5de, vil ogs\u00e5 v\u00e6re afg\u00f8rende ift. til at eksportere fjernvarmesystemer (s\u00e5vel som k\u00f8lel\u00f8sninger) som moderne og fremtidssikrede energil\u00f8sninger til nye markeder.<\/span><\/p>\n


Optimering af fjernvarmesystemer i fremtiden kr\u00e6ver en kombination af avancerede teknologil\u00f8sninger, fleksible integrationer og ekspertise inden for energistyring og systemteknik. For at opn\u00e5 disse optimeringer kr\u00e6ver det en digitalisering af fjernvarmesektoren ved at have realtidsindgang til hele nettet og en systemkobling til forskellige energi- og datakilder. Dette \u00f8ger m\u00e6ngden af data, der skal behandles, gemmes og analyseres og dermed p\u00e5l\u00e6gges der krav om \u00e5benhed og fleksibilitet til datalagringen.
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Projektbeskrivelse<\/span><\/strong><\/h3>\n

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Dette projekt adresserer udfordringen med at hente realtidstrykm\u00e5linger og flowdata fra adskillige nye m\u00e5lepunkter i fjernvarmenettet og skaber indsigt med Machine Learning genererede tv\u00e6rsektorale Digital Twins.
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Projektets samlede m\u00e5l er at:<\/span><\/p>\n