Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative Models
The design process of heating, ventilation, and air conditioning (HVAC) systems is complex and time consuming due to the need to follow design codes. Since the design standards are not fixed, the final outcome often depends on the designer’s experience. The development of building information modeli...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/14/11/3405 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850217160983445504 |
|---|---|
| author | Hongxin Wang Ruiying Jin Peng Xu Jiefan Gu |
| author_facet | Hongxin Wang Ruiying Jin Peng Xu Jiefan Gu |
| author_sort | Hongxin Wang |
| collection | DOAJ |
| description | The design process of heating, ventilation, and air conditioning (HVAC) systems is complex and time consuming due to the need to follow design codes. Since the design standards are not fixed, the final outcome often depends on the designer’s experience. The development of building information modeling (BIM) technology has made information throughout the building lifecycle more integrated. BIM-based forward design is now widely used, providing a data foundation for combining HVAC system design with machine learning. This paper proposes an unsupervised learning method based on deep graph generative models to uncover hidden design patterns and optimization strategies from the design results. We trained and validated four deep graph generative models—GAE, GNF, GAN, and diffusion—using HVAC system terminal pipeline layout data. Accuracy and precision metrics were used to compare the generated designs with automated forward design solutions, assessing the models’ ability to capture both local variations and broader changes in design logic. A graph-neural-network-based evaluation method was employed to measure the models’ capacity to detect changes. The results indicate that all four models achieved prediction accuracies exceeding 90% and precision rates above 75%. The models effectively captured both local modifications made by designers and global design changes, showing greater sensitivity to global layout adjustments than to local updates. When comparing the results generated by deep graph generative models and the actual design, it is obvious that the accuracy of the predictions varies significantly due to the complexity of the test buildings. |
| format | Article |
| id | doaj-art-af987d72120446ec9224a671e2c0a56e |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-af987d72120446ec9224a671e2c0a56e2025-08-20T02:08:08ZengMDPI AGBuildings2075-53092024-10-011411340510.3390/buildings14113405Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative ModelsHongxin Wang0Ruiying Jin1Peng Xu2Jiefan Gu3School of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaCollege of Architecture and Urban Planning, Tongji University, Shanghai 200092, ChinaThe design process of heating, ventilation, and air conditioning (HVAC) systems is complex and time consuming due to the need to follow design codes. Since the design standards are not fixed, the final outcome often depends on the designer’s experience. The development of building information modeling (BIM) technology has made information throughout the building lifecycle more integrated. BIM-based forward design is now widely used, providing a data foundation for combining HVAC system design with machine learning. This paper proposes an unsupervised learning method based on deep graph generative models to uncover hidden design patterns and optimization strategies from the design results. We trained and validated four deep graph generative models—GAE, GNF, GAN, and diffusion—using HVAC system terminal pipeline layout data. Accuracy and precision metrics were used to compare the generated designs with automated forward design solutions, assessing the models’ ability to capture both local variations and broader changes in design logic. A graph-neural-network-based evaluation method was employed to measure the models’ capacity to detect changes. The results indicate that all four models achieved prediction accuracies exceeding 90% and precision rates above 75%. The models effectively captured both local modifications made by designers and global design changes, showing greater sensitivity to global layout adjustments than to local updates. When comparing the results generated by deep graph generative models and the actual design, it is obvious that the accuracy of the predictions varies significantly due to the complexity of the test buildings.https://www.mdpi.com/2075-5309/14/11/3405building information modelingHVAC system designgenerative designmachine learningdeep graph generative models |
| spellingShingle | Hongxin Wang Ruiying Jin Peng Xu Jiefan Gu Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative Models Buildings building information modeling HVAC system design generative design machine learning deep graph generative models |
| title | Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative Models |
| title_full | Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative Models |
| title_fullStr | Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative Models |
| title_full_unstemmed | Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative Models |
| title_short | Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative Models |
| title_sort | generation method for hvac systems design schemes in office buildings based on deep graph generative models |
| topic | building information modeling HVAC system design generative design machine learning deep graph generative models |
| url | https://www.mdpi.com/2075-5309/14/11/3405 |
| work_keys_str_mv | AT hongxinwang generationmethodforhvacsystemsdesignschemesinofficebuildingsbasedondeepgraphgenerativemodels AT ruiyingjin generationmethodforhvacsystemsdesignschemesinofficebuildingsbasedondeepgraphgenerativemodels AT pengxu generationmethodforhvacsystemsdesignschemesinofficebuildingsbasedondeepgraphgenerativemodels AT jiefangu generationmethodforhvacsystemsdesignschemesinofficebuildingsbasedondeepgraphgenerativemodels |