View in EDS

The application of the attention deep learning model based on Particle Swarm Optimization in heating load prediction.

Saved in:
Bibliographic Details
Title: The application of the attention deep learning model based on Particle Swarm Optimization in heating load prediction.
Authors: Zhang, Xin1,2 (AUTHOR) zhangqqqf@126.com, Han, Jiliang1 (AUTHOR), Wang, Bo1 (AUTHOR), Pan, Qiang1 (AUTHOR), Zhao, Fang1 (AUTHOR), Zhang, Zhaowei3 (AUTHOR), Wang, Yida4 (AUTHOR)
Source: Journal of Building Performance Simulation. Jun2026, Vol. 19 Issue 4, p638-653. 16p.
Subjects: Particle swarm optimization, Heating load, Energy conservation, Meteorological databases, Long short-term memory, Ensemble learning, Deep learning
Geographic Terms: Harbin (China)
Abstract: How to predict heating load more accurately plays an important role in energy conservation and promoting scientific and green development of society. This study analyzes heating load data from two consecutive seasons (October 2021–April 2023) in Harbin, integrated with meteorological data, and proposes a novel hybrid model combining a Long Short-Term Memory Network (LSTM) with a Particle Swarm Optimization (PSO) algorithm and an Attention Mechanism. This model leverages LSTM's strength in extracting temporal features, the Attention Mechanism's capability for optimal feature weight allocation, and PSO for hyperparameter optimization. Comparative analysis against K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Recurrent Neural Network (RNN), Attention-LSTM, PSO-LSTM, GA-LSTM, and DE-LSTM demonstrates that the proposed PSO-Attention-LSTM achieves superior performance, yielding the lowest Mean Squared Error (MSE) and Mean Absolute Error (MAE) while maintaining a comparable Coefficient of Determination (R²). Furthermore, an integrated model integrating the optimal KNN and PSO-Attention-LSTM was developed. This integrated model further enhances accuracy, reducing MAE and MSE by approximately 14.40% and 18.93% compared to KNN, and by 7.82% and 11.71% compared to the standalone PSO-Attention-LSTM, respectively. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Building Performance Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 194221739
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: The application of the attention deep learning model based on Particle Swarm Optimization in heating load prediction.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Xin%22">Zhang, Xin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zhangqqqf@126.com</i><br /><searchLink fieldCode="AR" term="%22Han%2C+Jiliang%22">Han, Jiliang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Bo%22">Wang, Bo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Qiang%22">Pan, Qiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Fang%22">Zhao, Fang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zhaowei%22">Zhang, Zhaowei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yida%22">Wang, Yida</searchLink><relatesTo>4</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Building+Performance+Simulation%22">Journal of Building Performance Simulation</searchLink>. Jun2026, Vol. 19 Issue 4, p638-653. 16p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Heating+load%22">Heating load</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+conservation%22">Energy conservation</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorological+databases%22">Meteorological databases</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Harbin+%28China%29%22">Harbin (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: How to predict heating load more accurately plays an important role in energy conservation and promoting scientific and green development of society. This study analyzes heating load data from two consecutive seasons (October 2021–April 2023) in Harbin, integrated with meteorological data, and proposes a novel hybrid model combining a Long Short-Term Memory Network (LSTM) with a Particle Swarm Optimization (PSO) algorithm and an Attention Mechanism. This model leverages LSTM's strength in extracting temporal features, the Attention Mechanism's capability for optimal feature weight allocation, and PSO for hyperparameter optimization. Comparative analysis against K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Recurrent Neural Network (RNN), Attention-LSTM, PSO-LSTM, GA-LSTM, and DE-LSTM demonstrates that the proposed PSO-Attention-LSTM achieves superior performance, yielding the lowest Mean Squared Error (MSE) and Mean Absolute Error (MAE) while maintaining a comparable Coefficient of Determination (R²). Furthermore, an integrated model integrating the optimal KNN and PSO-Attention-LSTM was developed. This integrated model further enhances accuracy, reducing MAE and MSE by approximately 14.40% and 18.93% compared to KNN, and by 7.82% and 11.71% compared to the standalone PSO-Attention-LSTM, respectively. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Building Performance Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194221739
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/19401493.2026.2640405
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 638
    Subjects:
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Heating load
        Type: general
      – SubjectFull: Energy conservation
        Type: general
      – SubjectFull: Meteorological databases
        Type: general
      – SubjectFull: Long short-term memory
        Type: general
      – SubjectFull: Ensemble learning
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Harbin (China)
        Type: general
    Titles:
      – TitleFull: The application of the attention deep learning model based on Particle Swarm Optimization in heating load prediction.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhang, Xin
      – PersonEntity:
          Name:
            NameFull: Han, Jiliang
      – PersonEntity:
          Name:
            NameFull: Wang, Bo
      – PersonEntity:
          Name:
            NameFull: Pan, Qiang
      – PersonEntity:
          Name:
            NameFull: Zhao, Fang
      – PersonEntity:
          Name:
            NameFull: Zhang, Zhaowei
      – PersonEntity:
          Name:
            NameFull: Wang, Yida
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19401493
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Journal of Building Performance Simulation
              Type: main
ResultId 1