Improving building heat load forecasting models with automated identification and attribution of day types

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dc.contributor.authorLumbreras Mugaguren, Mikel
dc.contributor.authorGaray Martínez, Roberto
dc.contributor.authorDiarce Belloso, Gonzalo
dc.contributor.authorMartín Escudero, Koldobika
dc.contributor.authorArregi, Beñat
dc.date.accessioned2025-12-01T12:12:09Z
dc.date.available2025-12-01T12:12:09Z
dc.date.issued2025-10-08
dc.date.updated2025-12-01T12:12:09Z
dc.description.abstractThis paper introduces a comprehensive methodology for predicting hourly heat loads in buildings. The approach employs unsupervised learning to identify distinct day types based on daily load profiles. A classification process then assigns each day to one of these day types, followed by the application of various supervised learning techniques to forecast heat loads. The methodology is both simple and robust, facilitating its use in load prediction across a wide range of buildings. The process is validated using data from three distinct building types (Residential, Educational, and Commercial) located in Tartu, Estonia. The results indicate that the day type identification and attribution process significantly reduce model complexity and computational time while achieving high prediction accuracy (MAPE ~<2%) with minimal computational requirements.en
dc.identifier.citationLumbreras, M., Garay-Martinez, R., Diarce, G., Martin-Escudero, K., & Arregi, B. (2025). Improving building heat load forecasting models with automated identification and attribution of day types. Buildings, 15(19). https://doi.org/10.3390/BUILDINGS15193604
dc.identifier.doi10.3390/BUILDINGS15193604
dc.identifier.eissn2075-5309
dc.identifier.urihttps://hdl.handle.net/20.500.14454/4508
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights© 2025 by the authors
dc.subject.otherDistrict-heating networks
dc.subject.otherHeat load in buildings
dc.subject.otherLoad prediction
dc.subject.otherPattern recognition
dc.titleImproving building heat load forecasting models with automated identification and attribution of day typesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue19
oaire.citation.titleBuildings
oaire.citation.volume15
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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