{"id":42921,"date":"2025-02-24T09:02:32","date_gmt":"2025-02-24T08:02:32","guid":{"rendered":"https:\/\/spacewell.com\/okategoriserad\/artificial-intelligence-in-energy-management-new-forecast-tool\/"},"modified":"2025-07-23T11:05:20","modified_gmt":"2025-07-23T09:05:20","slug":"artificial-intelligence-in-energy-management-new-forecast-tool","status":"publish","type":"post","link":"https:\/\/spacewell.com\/sv\/resurser\/blogg\/artificial-intelligence-in-energy-management-new-forecast-tool\/","title":{"rendered":"Artificiell intelligens inom energihantering: Nytt verktyg f\u00f6r prognoser"},"content":{"rendered":"<div class=\"gb-container gb-container-aae68dfd gb-container-5731d80c\">\n\n<p>Artificiell intelligens (AI) har skapat rubriker \u00f6verallt &#8211; fr\u00e5n individer som anv\u00e4nder ChatGPT f\u00f6r dagliga uppgifter till f\u00f6retag som utvecklar sina egna AI-motorer. Det f\u00f6rbises dock ofta att AI och maskininl\u00e4rning (ML) har funnits ganska l\u00e4nge, med m\u00e5nga verkliga till\u00e4mpningar som f\u00f6regick ChatGPT:s lansering.   <\/p>\n\n\n\n<p>N\u00e4r det inte sker via en chatt kan vi ibland inte identifiera om det vi ser \u00e4r ber\u00e4knat genom AI-drivna processer, eftersom dessa system i grunden \u00e4r beroende av statistiska och matematiska modeller.<\/p>\n\n\n\n<p>I den h\u00e4r artikeln f\u00e5r du se n\u00e5gra verkliga exempel p\u00e5 vad AI inneb\u00e4r f\u00f6r energihanteringen, bland annat v\u00e5r nya prognosmodul, och du har m\u00f6jlighet att anm\u00e4la dig till v\u00e5rt kommande webbinarium.  <\/p>\n\n\n\n<!--HubSpot Call-to-Action Code --><span class=\"hs-cta-wrapper\" id=\"hs-cta-wrapper-d98b9fc1-6dfc-4e8b-b5b3-caed4a492b95\"><span class=\"hs-cta-node hs-cta-d98b9fc1-6dfc-4e8b-b5b3-caed4a492b95\" id=\"hs-cta-d98b9fc1-6dfc-4e8b-b5b3-caed4a492b95\"><!--[if lte IE 8]><div id=\"hs-cta-ie-element\"><\/div><![endif]--><a href=\"https:\/\/cta-redirect.hubspot.com\/cta\/redirect\/437281\/d98b9fc1-6dfc-4e8b-b5b3-caed4a492b95\"><img decoding=\"async\" class=\"hs-cta-img\" id=\"hs-cta-img-d98b9fc1-6dfc-4e8b-b5b3-caed4a492b95\" style=\"border-width:0px;\" src=\"https:\/\/no-cache.hubspot.com\/cta\/default\/437281\/d98b9fc1-6dfc-4e8b-b5b3-caed4a492b95.png\" alt=\"Webbinarium Energiprognos\"\/><\/a><\/span><script charset=\"utf-8\" src=\"https:\/\/js.hscta.net\/cta\/current.js\"><\/script><script type=\"text\/javascript\"> hbspt.cta.load(437281, 'd98b9fc1-6dfc-4e8b-b5b3-caed4a492b95', {\"useNewLoader\":\"true\",\"region\":\"na1\"}); <\/script><\/span><!-- end HubSpot Call-to-Action Code -->\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-machine-learning-and-ai-in-energy-management\">Maskininl\u00e4rning och AI inom energihantering<\/h2>\n\n\n\n<p>En av de enklaste formerna av ML \u00e4r en linj\u00e4r regressionsmodell &#8211; y = Ax + B &#8211; som de flesta ingenj\u00f6rer k\u00e4nner till. Som du kanske kan f\u00f6rest\u00e4lla dig str\u00e4cker sig disciplinen till ett brett utbud av mer sofistikerade tillv\u00e4gag\u00e5ngss\u00e4tt, inklusive neurala n\u00e4tverk. <\/p>\n\n\n\n<p>Med tanke p\u00e5 detta, hur \u00e4r artificiell intelligens kopplad till energihantering?<\/p>\n\n\n\n<p><strong>Energihanteringssektorn kan dra stor nytta av AI-drivna l\u00f6sningar<\/strong>. Bland de nyckelomr\u00e5den d\u00e4r AI-modeller visar sig vara viktiga \u00e4r: <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Skapande av baslinje<\/strong> f\u00f6r att f\u00f6lja protokoll som International Performance Measurement and Verification Protocol (IPMVP) f\u00f6r att p\u00e5visa energibesparingar.<\/li>\n\n\n\n<li><strong>Prognostisering<\/strong> av energif\u00f6rbrukning eller energiproduktion.<\/li>\n\n\n\n<li><strong>Uppt\u00e4cka avvikelser och ineffektivitet<\/strong> i byggnader.<\/li>\n\n\n\n<li><strong>Inl\u00e4rning av byggnaders beteende<\/strong> under specifika omgivningsf\u00f6rh\u00e5llanden.<\/li>\n<\/ul>\n\n\n\n<p>Med tanke p\u00e5 att dessa olika anv\u00e4ndningsomr\u00e5den har funnits i m\u00e5nga \u00e5r och ofta har hanterats med hj\u00e4lp av <em>enkla <\/em>modeller uppst\u00e5r en relevant fr\u00e5ga: n\u00e4r ska vi v\u00e4lja dessa <em>enkla<\/em> modeller som linj\u00e4ra regressioner och n\u00e4r ska vi anv\u00e4nda mer avancerade metoder?<\/p>\n\n\n\n<p>Vissa regleringar dikterar ofta anv\u00e4ndningen av specifika modeller. Enligt IPMVP:s riktlinjer m\u00e5ste till exempel multipla linj\u00e4ra modeller (Ax+By+Cz+&#8230;+D) anv\u00e4ndas f\u00f6r att kvantifiera besparingar, \u00e4ven om andra metoder kan ge b\u00e4ttre resultat, dvs. ingen avancerad maskininl\u00e4rning \u00e4r \u00e4nnu till\u00e5ten. F\u00f6r att underl\u00e4tta denna process har Spacewell Energy utvecklat ABC-appen, som hj\u00e4lper till att effektivisera skapandet av s\u00e5dana ekvationer.  <\/p>\n\n\n\n<p>N\u00e4r det inte finns n\u00e5gra r\u00e4ttsliga begr\u00e4nsningar \u00e4r det dock viktigt att g\u00f6ra en avv\u00e4gning mellan noggrannhet och de resurser som investeras. Det skulle till exempel vara ineffektivt att anv\u00e4nda en mycket komplex modell f\u00f6r att f\u00f6rklara ett samband mellan tv\u00e5 variabler, som att kontrollera om det finns ett samband mellan uppv\u00e4rmningsgraddagar (DD) och elf\u00f6rbrukning. <strong>Mer avancerade modeller blir<\/strong> i st\u00e4llet <strong>v\u00e4rdefulla n\u00e4r f\u00f6rh\u00e5llandet mellan input och output inte \u00e4r okomplicerat<\/strong>, t.ex. vid energiprognoser.    <\/p>\n\n\n\n<p>Trots detta har vissa kunder uttryckt oro \u00f6ver att inte kunna <em>kontrollera <\/em>den modell som ber\u00e4knar resultaten. Med en enkel baslinje kan de v\u00e4lja indata, de kan kalibrera modellerna sj\u00e4lva och de kan enkelt se vilken effekt en f\u00f6r\u00e4ndring av indata kommer att f\u00e5 p\u00e5 utdata. Med komplexa ML-modeller \u00e4r detta d\u00e4remot inte m\u00f6jligt, eftersom de ibland fungerar som en svart l\u00e5da. Eftersom modellerna i sig kan ha flera dimensioner kan de inte representeras grafiskt eller genom en ekvation och ibland \u00e4r det sv\u00e5rt att veta vilken effekt varje indata har p\u00e5 utdata.     <\/p>\n\n\n\n<p>D\u00e4rf\u00f6r <strong>till\u00e4mpar vi p\u00e5 Spacewell Energy avancerad maskininl\u00e4rning i de fall d\u00e4r det kr\u00e4vs f\u00f6r att l\u00f6sa ett specifikt problem eller d\u00e4r det ger betydligt b\u00e4ttre resultat.<\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-a-practical-example-new-forecasting-tool-in-spacewell-energy\">Ett praktiskt exempel: Nytt prognosverktyg i Spacewell Energy<\/h2>\n\n\n\n<p>Spacewell Energy lanserade nyligen ett prognosverktyg som kan f\u00f6rutse resursf\u00f6rbrukning och produktion (el, gas, vatten) under de kommande 10 dagarna, med hj\u00e4lp av meteorologiska data.  <\/p>\n\n\n\n<p>Med h\u00e4nsyn till all information som har delats i f\u00f6reg\u00e5ende avsnitt ville vi se <strong>skillnaden i resultat mellan enkla och komplexa modeller.<\/strong> F\u00f6r att g\u00f6ra det j\u00e4mf\u00f6rde vi fyra prediktionsmetoder under 12 m\u00e5nader: <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Enklast m\u00f6jliga: <\/strong>Linj\u00e4r regression (Ax + By + Cz + (&#8230;) + D)<\/li>\n\n\n\n<li>Polynomregression (Ax + Bxy + <sup>Cz2<\/sup> + (&#8230;) + D)<\/li>\n\n\n\n<li><strong>Mest komplexa: <\/strong>Tv\u00e5 mer komplexa AI-modeller som erbjuds inom v\u00e5rt prognosverktyg:\n<ul class=\"wp-block-list\">\n<li>AI-modell med b\u00e4st resultat i verktyget (den som visar l\u00e4gst fel).<\/li>\n\n\n\n<li>S\u00e4mst presterande AI-modell i verktyget (den som uppvisar det h\u00f6gsta felet).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>Resultaten visade att den AI-modell som gav b\u00e4st resultat ledde till en <strong>minskning<\/strong> av prognosfelen med <strong>28%<\/strong> j\u00e4mf\u00f6rt med andra metoder. Dessutom var det den modell som gav den b\u00e4sta f\u00f6ruts\u00e4gelsen under alla de 12 m\u00e5nader d\u00e5 de fyra modellerna utv\u00e4rderades: <\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXeSOMe7W6uUoNI6dSIW9P9nTV53H77fsBYVWQg_9SitxPnasUYNT4mWKG5X8PI-ittMyy2uoe6CZfPyJ57M3fdtiicBQpooOJN9gXvOynm950sEKiruc1oT5uvlGzW-hvAmeHvBQg?key=LZz4WR7USqkKPfhnv4WlD3iZ\" alt=\"\"\/><\/figure>\n\n\n\n<p>Felprocenten kanske inte hj\u00e4lper dig att f\u00f6rst\u00e5 hur mycket b\u00e4ttre den ena modellen \u00e4r j\u00e4mf\u00f6rt med den andra. F\u00f6r att vara tydligare visade vi ocks\u00e5 den grafiska skillnaden mellan modellerna. Nedan kan du se skillnaden mellan den b\u00e4st presterande AI-modellen och de andra, eftersom den \u00e4r den f\u00f6rsta och den enda som kan f\u00f6lja den verkliga konsumtionskurvan:    <\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXeHG9CNXZebOdA5XG0Sfv99dCXCWFeKltqMrWw_PB-WaY46Zn8_RzYMbsVWDtQBWgY-HaJ1Z7cFPoO9Tmf33Sma9fGoLkKkoFm04hW5d_K3AlbUK5Iwn9-K2DcfJ5b-hOixXW-Q?key=LZz4WR7USqkKPfhnv4WlD3iZ\" alt=\"\"\/><\/figure>\n\n\n\n<p>I det h\u00e4r fallet best\u00e4mde vi oss f\u00f6r att anv\u00e4nda de komplexa AI-modellerna eftersom de gav betydligt b\u00e4ttre resultat \u00e4n de enklare modellerna.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-other-applications-of-artificial-intelligence-in-energy-management\"><strong>Andra till\u00e4mpningar av artificiell intelligens inom energihantering<\/strong><\/h3>\n\n\n\n<p>P\u00e5 Spacewell Energy har vi inte bara prognosfunktionen utan \u00e4ven fler AI-baserade produkter:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Optimise<\/strong>: v\u00e5r motor f\u00f6r uppt\u00e4ckt av anomalier<\/li>\n\n\n\n<li><strong>Detect<\/strong>: v\u00e5rt benchmarkingverktyg som hj\u00e4lper till att identifiera l\u00e5gt h\u00e4ngande frukter inom ett f\u00f6retags portf\u00f6lj<\/li>\n\n\n\n<li><strong>Energioptimering<\/strong> (f\u00f6r n\u00e4rvarande endast i Spanien): V\u00e5rt verktyg som hj\u00e4lper anv\u00e4ndare att identifiera den b\u00e4sta kontrakterade energin f\u00f6r sina byggnader f\u00f6r att minimera energikostnaderna<\/li>\n\n\n\n<li><strong>ABC<\/strong> (Automatisk skapande av baslinjer): V\u00e5rt verktyg som hj\u00e4lper till att skapa baslinjer som \u00e4r kompatibla med IPMVP-protokollet.<\/li>\n<\/ul>\n\n\n\n<p>Du kan l\u00e4ra dig mer om v\u00e5rt prognosverktyg Optimise, Detect och ABC under v\u00e5rt n\u00e4sta webbseminarium den 20 mars.<\/p>\n\n\n\n<p>AI har varit en del av energihanteringslandskapet under mycket l\u00e4ngre tid \u00e4n m\u00e5nga inser, och ligger till grund f\u00f6r allt fr\u00e5n skapande av baslinjer till uppt\u00e4ckt av avvikelser. \u00c4ven om regleringar som IPMVP kr\u00e4ver enklare metoder som multipel linj\u00e4r regression, finns det gott om bevis f\u00f6r att <strong>mer komplexa modeller kan \u00f6vertr\u00e4ffa dessa traditionella metoder n\u00e4r det inte finns n\u00e5gra r\u00e4ttsliga begr\u00e4nsningar<\/strong>. Att balansera komplexiteten i dessa l\u00f6sningar med behovet av transparens \u00e4r fortfarande en viktig utmaning, eftersom avancerade maskininl\u00e4rningsmodeller ofta \u00e4r sv\u00e5ra att visualisera och tolka.  <\/p>\n\n\n\n<p>Trots detta <strong>illustrerar verkliga resultat de tydliga f\u00f6rdelarna med att anamma AI-driven innovation.<\/strong> Spacewell Energys prognosverktyg uppvisade till exempel en anm\u00e4rkningsv\u00e4rd f\u00f6rb\u00e4ttring p\u00e5 28% i prognosprecision n\u00e4r man anv\u00e4nde en mer avancerad modell ist\u00e4llet f\u00f6r enklare metoder. \u00c4ven om vissa anv\u00e4ndare inledningsvis kan tveka inf\u00f6r l\u00f6sningar som de inte helt kan dekonstruera, bekr\u00e4ftar de tydliga vinsterna i effektivitet och tillf\u00f6rlitlighet att dessa avancerade AI-tekniker i m\u00e5nga fall erbjuder den mest effektiva v\u00e4gen fram\u00e5t.  <\/p>\n\n\n\n<p>F\u00f6r att forts\u00e4tta prata om AI inom energihantering och den nya funktionen Forecast h\u00e5ller vi ett nytt webbseminarium den 20 mars. Registrera dig idag f\u00f6r att h\u00e5lla dig uppdaterad om de senaste trenderna och innovationerna.   <\/p>\n\n\n\n<!--HubSpot Call-to-Action Code --><span class=\"hs-cta-wrapper\" id=\"hs-cta-wrapper-87d938df-6566-449a-9792-de9b1b973f13\"><span class=\"hs-cta-node hs-cta-87d938df-6566-449a-9792-de9b1b973f13\" id=\"hs-cta-87d938df-6566-449a-9792-de9b1b973f13\"><!--[if lte IE 8]><div id=\"hs-cta-ie-element\"><\/div><![endif]--><a href=\"https:\/\/cta-redirect.hubspot.com\/cta\/redirect\/437281\/87d938df-6566-449a-9792-de9b1b973f13\"><img decoding=\"async\" class=\"hs-cta-img\" id=\"hs-cta-img-87d938df-6566-449a-9792-de9b1b973f13\" style=\"border-width:0px;\" src=\"https:\/\/no-cache.hubspot.com\/cta\/default\/437281\/87d938df-6566-449a-9792-de9b1b973f13.png\" alt=\"Webbinarium AI inom energihantering\"\/><\/a><\/span><script charset=\"utf-8\" src=\"https:\/\/js.hscta.net\/cta\/current.js\"><\/script><script type=\"text\/javascript\"> hbspt.cta.load(437281, '87d938df-6566-449a-9792-de9b1b973f13', {\"useNewLoader\":\"true\",\"region\":\"na1\"}); <\/script><\/span><!-- end HubSpot Call-to-Action Code -->\n\n\n\n<p>Anm\u00e4rkning fr\u00e5n redakt\u00f6ren: Den h\u00e4r artikeln har skrivits av J\u00falia Bayascas, produktchef p\u00e5 Spacewell Energy.<\/p>\n\n<\/div>\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificiell intelligens (AI) har skapat rubriker \u00f6verallt &#8211; fr\u00e5n individer som anv\u00e4nder ChatGPT f\u00f6r dagliga uppgifter till f\u00f6retag som utvecklar sina egna AI-motorer. Det f\u00f6rbises dock ofta att AI och maskininl\u00e4rning (ML) har funnits ganska l\u00e4nge, med m\u00e5nga verkliga till\u00e4mpningar som f\u00f6regick ChatGPT:s lansering. N\u00e4r det inte sker via en chatt kan vi ibland inte &#8230; <a title=\"Artificiell intelligens inom energihantering: Nytt verktyg f\u00f6r prognoser\" class=\"read-more\" href=\"https:\/\/spacewell.com\/sv\/resurser\/blogg\/artificial-intelligence-in-energy-management-new-forecast-tool\/\" aria-label=\"L\u00e4s mer om Artificiell intelligens inom energihantering: Nytt verktyg f\u00f6r prognoser\">L\u00e4s mer<\/a><\/p>\n","protected":false},"author":33,"featured_media":30487,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[558,37,408,418,32,105],"tags":[554],"class_list":["post-42921","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-anomalidetektering","category-blogg","category-energi","category-energianalys","category-resurser","category-webinarier","tag-energihantering","generate-columns","tablet-grid-50","mobile-grid-100","grid-parent","grid-50","no-featured-image-padding"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.0 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Artificiell intelligens inom energihantering: Nytt verktyg f\u00f6r prognoser<\/title>\n<meta name=\"description\" content=\"Vill du l\u00e4ra dig vad artificiell intelligens egentligen inneb\u00e4r i samband med energihantering? 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