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Thermal load prediction using surrogate models in district heating systems.

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Title: Thermal load prediction using surrogate models in district heating systems.
Authors: Zouloumis, Leonidas1 (AUTHOR), Krallis, Xristos2 (AUTHOR), Panaras, Giorgos1 (AUTHOR) gpanaras@auth.gr, Ploskas, Nikolaos2 (AUTHOR)
Source: Advances in Building Energy Research. Jun2026, Vol. 20 Issue 3, p339-365. 27p.
Subject Terms: *Heating load, *Prediction models, *Algorithms, *Heating from central stations, *Energy consumption of buildings, *Statistical models
Abstract: The optimization of surrogate modelling used in building thermal load management of District Heating Network (DHN) layouts for heating load prediction is crucial to reducing the contribution of building energy consumption globally. However, state-of-the-art surrogate models often struggle to capture the dynamic thermal mechanics of DHNs that occur in hourly intervals without sacrificing their low computational cost. Consequently, the model modification should be expanded beyond the present literature scope of conventional archetypes or multi-surrogate structures only. To this aim, this work proposes a novel surrogate model construction methodology, which emphasizes bolstering surrogate model performance through the combination of a model archetype, a multi-surrogate structure using rule-based data splitting and a criterion-based restriction of training data. Applying the modelling methodology on a real substation shows that the predictive performance of surrogate models depends on dataset restriction on single-surrogate cases, across all archetypes. Contrarily, in multi-surrogate cases, it depends on the model archetype used, as well as insufficient data quality and thermal mechanics manifesting in certain periods of the operation of the DHN substation. In general, this novel methodology can assist in future heat load prediction endeavours, by detecting surrogate modelling limitations posed in each DHN case and encouraging further improvements. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Availability: 0
Header DbId: enr
DbLabel: Energy & Power Source
An: 194058471
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Thermal load prediction using surrogate models in district heating systems.
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  Data: <searchLink fieldCode="AR" term="%22Zouloumis%2C+Leonidas%22">Zouloumis, Leonidas</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Krallis%2C+Xristos%22">Krallis, Xristos</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Panaras%2C+Giorgos%22">Panaras, Giorgos</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gpanaras@auth.gr</i><br /><searchLink fieldCode="AR" term="%22Ploskas%2C+Nikolaos%22">Ploskas, Nikolaos</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Advances+in+Building+Energy+Research%22">Advances in Building Energy Research</searchLink>. Jun2026, Vol. 20 Issue 3, p339-365. 27p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Heating+load%22">Heating load</searchLink><br />*<searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Heating+from+central+stations%22">Heating from central stations</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption+of+buildings%22">Energy consumption of buildings</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The optimization of surrogate modelling used in building thermal load management of District Heating Network (DHN) layouts for heating load prediction is crucial to reducing the contribution of building energy consumption globally. However, state-of-the-art surrogate models often struggle to capture the dynamic thermal mechanics of DHNs that occur in hourly intervals without sacrificing their low computational cost. Consequently, the model modification should be expanded beyond the present literature scope of conventional archetypes or multi-surrogate structures only. To this aim, this work proposes a novel surrogate model construction methodology, which emphasizes bolstering surrogate model performance through the combination of a model archetype, a multi-surrogate structure using rule-based data splitting and a criterion-based restriction of training data. Applying the modelling methodology on a real substation shows that the predictive performance of surrogate models depends on dataset restriction on single-surrogate cases, across all archetypes. Contrarily, in multi-surrogate cases, it depends on the model archetype used, as well as insufficient data quality and thermal mechanics manifesting in certain periods of the operation of the DHN substation. In general, this novel methodology can assist in future heat load prediction endeavours, by detecting surrogate modelling limitations posed in each DHN case and encouraging further improvements. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194058471
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/17512549.2025.2612103
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 339
    Subjects:
      – SubjectFull: Heating load
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Heating from central stations
        Type: general
      – SubjectFull: Energy consumption of buildings
        Type: general
      – SubjectFull: Statistical models
        Type: general
    Titles:
      – TitleFull: Thermal load prediction using surrogate models in district heating systems.
        Type: main
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          Name:
            NameFull: Zouloumis, Leonidas
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            NameFull: Krallis, Xristos
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            NameFull: Panaras, Giorgos
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            NameFull: Ploskas, Nikolaos
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 17512549
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              Value: 20
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              Value: 3
          Titles:
            – TitleFull: Advances in Building Energy Research
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