Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters

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dc.contributor.authorLumbreras, Mikel
dc.contributor.authorGaray-Martinez, Roberto
dc.contributor.authorArregi, BeƱat
dc.contributor.authorMartin-Escudero, Koldobika
dc.contributor.authorDiarce, Gonzalo
dc.contributor.authorRaud, Margus
dc.contributor.authorHagu, Indrek
dc.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionEDIFICACIƓN DE ENERGƍA POSITIVA
dc.date.issued2022-01-15
dc.descriptionPublisher Copyright: Ā© 2021 The Authors
dc.description.abstractAn accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R2 values from 0.70 to 0.99 are obtained for daily data resolution and R2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study.en
dc.description.statusPeer reviewed
dc.format.extent1
dc.format.extent3724196
dc.format.extent169676
dc.identifier.citationLumbreras , M , Garay-Martinez , R , Arregi , B , Martin-Escudero , K , Diarce , G , Raud , M & Hagu , I 2022 , ' Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters ' , Energy , vol. unknown , 122318 , pp. 122318 . https://doi.org/10.1016/j.energy.2021.122318
dc.identifier.doi10.1016/j.energy.2021.122318
dc.identifier.issn0360-5442
dc.identifier.otherresearchoutputwizard: 11556/1217
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85117568402&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofEnergy
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsLoad forecasting
dc.subject.keywordsHeat meters
dc.subject.keywordsData-driven model
dc.subject.keywordsBuilding
dc.subject.keywordsDistrict Heating
dc.subject.keywordsLoad forecasting
dc.subject.keywordsHeat meters
dc.subject.keywordsData-driven model
dc.subject.keywordsBuilding
dc.subject.keywordsDistrict Heating
dc.subject.keywordsCivil and Structural Engineering
dc.subject.keywordsBuilding and Construction
dc.subject.keywordsModeling and Simulation
dc.subject.keywordsRenewable Energy, Sustainability and the Environment
dc.subject.keywordsFuel Technology
dc.subject.keywordsEnergy Engineering and Power Technology
dc.subject.keywordsPollution
dc.subject.keywordsGeneral Energy
dc.subject.keywordsMechanical Engineering
dc.subject.keywordsIndustrial and Manufacturing Engineering
dc.subject.keywordsManagement, Monitoring, Policy and Law
dc.subject.keywordsElectrical and Electronic Engineering
dc.subject.keywordsSDG 7 - Affordable and Clean Energy
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/768567/EU/REnewable Low TEmperature District/RELaTED
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/768567/EU/REnewable Low TEmperature District/RELaTED
dc.subject.keywordsFunding Info
dc.subject.keywordsEuropean Commission, RELaTED: h2020, GA nĀŗ 768567
dc.subject.keywordsEuropean Commission, RELaTED: h2020, GA nĀŗ 768567
dc.titleData driven model for heat load prediction in buildings connected to District Heating by using smart heat metersen
dc.typejournal article
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