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Load Forecasting and Optimization of District Heating System Based on GAN Data Augmentation and LSTM–Prophet †.

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Title: Load Forecasting and Optimization of District Heating System Based on GAN Data Augmentation and LSTM–Prophet †.
Authors: Zheng, Xuejing1 (AUTHOR), Yan, Shisong1,2 (AUTHOR), Wang, Yaran1,2 (AUTHOR) yaran_wang@tju.edu.cn, Shi, Zhiyuan1,2 (AUTHOR), Tang, Zhiyun1 (AUTHOR), Wu, Yuyang1 (AUTHOR), Hu, Xiaguo1 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2551. 26p.
Subject Terms: *Data augmentation, *Load forecasting (Electric power systems), *Cost functions, *K-means clustering, *Heating from central stations, *Particle swarm optimization, *Energy consumption
Geographic Terms: Tianjin (China)
Abstract: Efficient and precise control of district heating (DH) networks is a critical pathway for achieving energy optimization and carbon emission reduction. This study proposes a systematic approach integrating data augmentation, hybrid model forecasting, and cost optimization. First, a Generative Adversarial Network (GAN) is employed to generate scenarios from limited meteorological and operational data, constructing an expanded dataset. Based on this, a personalized load forecasting model utilizing a dynamically weighted LSTM–Prophet combination is developed. This model assigns personalized weights to each heating station to accommodate the operational requirements of different functional zones. Validated using a district heating network in Tianjin, the results indicate that with an optimal weight of w = 0.9, the average relative error for load forecasting at Heating Station566 is −0.65%. Furthermore, the K-means algorithm is used to cluster the scenario database. The resulting typical scenarios are input into the LSTM–Prophet model to obtain real-time loads for each station, and a cost optimization model based on the APSO algorithm is subsequently constructed. Evaluated using a representative day, the optimized system achieves a reduction in distribution-stage cost of approximately 270,600 RMB, with a saving rate of 38%. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194587939
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Load Forecasting and Optimization of District Heating System Based on GAN Data Augmentation and LSTM–Prophet †.
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  Data: <searchLink fieldCode="AR" term="%22Zheng%2C+Xuejing%22">Zheng, Xuejing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Shisong%22">Yan, Shisong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yaran%22">Wang, Yaran</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> yaran_wang@tju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Shi%2C+Zhiyuan%22">Shi, Zhiyuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Zhiyun%22">Tang, Zhiyun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Yuyang%22">Wu, Yuyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Xiaguo%22">Hu, Xiaguo</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2551. 26p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br />*<searchLink fieldCode="DE" term="%22Load+forecasting+%28Electric+power+systems%29%22">Load forecasting (Electric power systems)</searchLink><br />*<searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink><br />*<searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br />*<searchLink fieldCode="DE" term="%22Heating+from+central+stations%22">Heating from central stations</searchLink><br />*<searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Tianjin+%28China%29%22">Tianjin (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Efficient and precise control of district heating (DH) networks is a critical pathway for achieving energy optimization and carbon emission reduction. This study proposes a systematic approach integrating data augmentation, hybrid model forecasting, and cost optimization. First, a Generative Adversarial Network (GAN) is employed to generate scenarios from limited meteorological and operational data, constructing an expanded dataset. Based on this, a personalized load forecasting model utilizing a dynamically weighted LSTM–Prophet combination is developed. This model assigns personalized weights to each heating station to accommodate the operational requirements of different functional zones. Validated using a district heating network in Tianjin, the results indicate that with an optimal weight of w = 0.9, the average relative error for load forecasting at Heating Station566 is −0.65%. Furthermore, the K-means algorithm is used to cluster the scenario database. The resulting typical scenarios are input into the LSTM–Prophet model to obtain real-time loads for each station, and a cost optimization model based on the APSO algorithm is subsequently constructed. Evaluated using a representative day, the optimized system achieves a reduction in distribution-stage cost of approximately 270,600 RMB, with a saving rate of 38%. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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        Value: 10.3390/en19112551
    Languages:
      – Code: eng
        Text: English
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        PageCount: 26
        StartPage: 2551
    Subjects:
      – SubjectFull: Data augmentation
        Type: general
      – SubjectFull: Load forecasting (Electric power systems)
        Type: general
      – SubjectFull: Cost functions
        Type: general
      – SubjectFull: K-means clustering
        Type: general
      – SubjectFull: Heating from central stations
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Tianjin (China)
        Type: general
    Titles:
      – TitleFull: Load Forecasting and Optimization of District Heating System Based on GAN Data Augmentation and LSTM–Prophet †.
        Type: main
  BibRelationships:
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          Name:
            NameFull: Zheng, Xuejing
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            NameFull: Yan, Shisong
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            NameFull: Wang, Yaran
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            NameFull: Shi, Zhiyuan
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            NameFull: Tang, Zhiyun
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            NameFull: Wu, Yuyang
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            NameFull: Hu, Xiaguo
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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            – Type: volume
              Value: 19
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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