A simulation approach of indoor temperature in existing buildings driven by short-term field measured data
Simulation of indoor temperature provides important references for thermal environment not only for buildings at design stage but also for existing buildings. The current thermal environment simulation software tools suit for buildings at design stage, however not for an existing building. A model i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-05-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2022.2085714 |
| _version_ | 1797803648906952704 |
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| author | Yulan Yang Huixin Tai Lingzhi Liu Beier Yu Wenlong Song |
| author_facet | Yulan Yang Huixin Tai Lingzhi Liu Beier Yu Wenlong Song |
| author_sort | Yulan Yang |
| collection | DOAJ |
| description | Simulation of indoor temperature provides important references for thermal environment not only for buildings at design stage but also for existing buildings. The current thermal environment simulation software tools suit for buildings at design stage, however not for an existing building. A model is proposed to simulate indoor temperature combining Optimization multivariable grey prediction model (OGM(1,N)) and Elman neural network. The proposed model is trained by short-term field measured data. A unit is assembled to measure and record thermal parameters in a case natural ventilated building at half-hourly intervals during 7:00 May 29 and 6:30 June 2010. Programming in Matlab implements the proposed model and referenced models. The maximum mean deviation is 0.46°C, the maximum standard mean square deviation is 0.65°C. Three referenced indoor temperature simulation models, OGM(1,N), Elman neural network, and Designer’s Simulation Toolkit are executed, respectively, in case building to provide comparison. Compared with referenced models, the proposed model has higher accuracy and stronger robustness. It is expected that this study provides important references for thermal environment assessment in existing buildings using short-term field measured data. |
| first_indexed | 2024-03-13T05:24:01Z |
| format | Article |
| id | doaj.art-26af6f89b0d94b9a9e61acc6117f5eae |
| institution | Directory Open Access Journal |
| issn | 1347-2852 |
| language | English |
| last_indexed | 2024-03-13T05:24:01Z |
| publishDate | 2023-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj.art-26af6f89b0d94b9a9e61acc6117f5eae2023-06-15T09:22:31ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522023-05-012231343136010.1080/13467581.2022.20857142085714A simulation approach of indoor temperature in existing buildings driven by short-term field measured dataYulan Yang0Huixin Tai1Lingzhi Liu2Beier Yu3Wenlong Song4Zhejiang University of TechnologyZhejiang University of TechnologyZhejiang University of TechnologyZhejiang University of TechnologyZhejiang University of TechnologySimulation of indoor temperature provides important references for thermal environment not only for buildings at design stage but also for existing buildings. The current thermal environment simulation software tools suit for buildings at design stage, however not for an existing building. A model is proposed to simulate indoor temperature combining Optimization multivariable grey prediction model (OGM(1,N)) and Elman neural network. The proposed model is trained by short-term field measured data. A unit is assembled to measure and record thermal parameters in a case natural ventilated building at half-hourly intervals during 7:00 May 29 and 6:30 June 2010. Programming in Matlab implements the proposed model and referenced models. The maximum mean deviation is 0.46°C, the maximum standard mean square deviation is 0.65°C. Three referenced indoor temperature simulation models, OGM(1,N), Elman neural network, and Designer’s Simulation Toolkit are executed, respectively, in case building to provide comparison. Compared with referenced models, the proposed model has higher accuracy and stronger robustness. It is expected that this study provides important references for thermal environment assessment in existing buildings using short-term field measured data.http://dx.doi.org/10.1080/13467581.2022.2085714indoor temperaturesimulationdata drivengrey predictionelman neural network |
| spellingShingle | Yulan Yang Huixin Tai Lingzhi Liu Beier Yu Wenlong Song A simulation approach of indoor temperature in existing buildings driven by short-term field measured data Journal of Asian Architecture and Building Engineering indoor temperature simulation data driven grey prediction elman neural network |
| title | A simulation approach of indoor temperature in existing buildings driven by short-term field measured data |
| title_full | A simulation approach of indoor temperature in existing buildings driven by short-term field measured data |
| title_fullStr | A simulation approach of indoor temperature in existing buildings driven by short-term field measured data |
| title_full_unstemmed | A simulation approach of indoor temperature in existing buildings driven by short-term field measured data |
| title_short | A simulation approach of indoor temperature in existing buildings driven by short-term field measured data |
| title_sort | simulation approach of indoor temperature in existing buildings driven by short term field measured data |
| topic | indoor temperature simulation data driven grey prediction elman neural network |
| url | http://dx.doi.org/10.1080/13467581.2022.2085714 |
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