Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors
In practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/11/10/458 |
| _version_ | 1797515140379181056 |
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| author | Yanan Zhao Zihan Zang Weirong Zhang Shen Wei Yingli Xuan |
| author_facet | Yanan Zhao Zihan Zang Weirong Zhang Shen Wei Yingli Xuan |
| author_sort | Yanan Zhao |
| collection | DOAJ |
| description | In practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing the thermal boundary conditions in practical applications. In this method, the index of contribution ratio of indoor climate (CRI), which represents the independent contribution of each heat source to the temperature distribution, has been combined with the air temperature collected by one mobile sensor at the height of the working area. Based on a typical office model, the effectiveness of using mobile sensors was discussed, and the influence of its acquisition height and acquisition distance on the prediction accuracy was analyzed as well. The results showed that the proposed prediction method was effective. When the sensors fixed on the wall were used to predict the indoor temperature distribution, the maximum average relative error was 27.7%, whereas when the mobile sensor was used to replace the fixed sensors, the maximum average relative error was 4.8%. This indicates that using mobile sensors with flexible acquisition location can help promote both reliability and accuracy of temperature prediction. In the human activity area, data from a set of mobile sensors were used to predict the temperature distribution at four heights. The prediction accuracy was 2.1%, 2.1%, 2.3%, and 2.7%, respectively. However, the influence of acquisition distance of mobile sensors on prediction accuracy cannot be ignored. The distance should be large enough to disperse the distribution of the acquisition points. Due to the influence of airflow, some distance between the acquisition points and the room boundaries should be given. |
| first_indexed | 2024-03-10T06:41:18Z |
| format | Article |
| id | doaj.art-488566fa8fde4277aa8f85f78f13f585 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-10T06:41:18Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-488566fa8fde4277aa8f85f78f13f5852023-11-22T17:38:41ZengMDPI AGBuildings2075-53092021-10-01111045810.3390/buildings11100458Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile SensorsYanan Zhao0Zihan Zang1Weirong Zhang2Shen Wei3Yingli Xuan4Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100022, ChinaKey Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100022, ChinaKey Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100022, ChinaThe Bartlett School of Construction and Project Management, University College London (UCL), 1-19 Torrington Place, London WC1E 7HB, UKJoint Usage/Research Center Wind Engineering Research Center Tokyo Polytechnic University, Tokyo Polytechnic University, Tokyo 164-8678, JapanIn practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing the thermal boundary conditions in practical applications. In this method, the index of contribution ratio of indoor climate (CRI), which represents the independent contribution of each heat source to the temperature distribution, has been combined with the air temperature collected by one mobile sensor at the height of the working area. Based on a typical office model, the effectiveness of using mobile sensors was discussed, and the influence of its acquisition height and acquisition distance on the prediction accuracy was analyzed as well. The results showed that the proposed prediction method was effective. When the sensors fixed on the wall were used to predict the indoor temperature distribution, the maximum average relative error was 27.7%, whereas when the mobile sensor was used to replace the fixed sensors, the maximum average relative error was 4.8%. This indicates that using mobile sensors with flexible acquisition location can help promote both reliability and accuracy of temperature prediction. In the human activity area, data from a set of mobile sensors were used to predict the temperature distribution at four heights. The prediction accuracy was 2.1%, 2.1%, 2.3%, and 2.7%, respectively. However, the influence of acquisition distance of mobile sensors on prediction accuracy cannot be ignored. The distance should be large enough to disperse the distribution of the acquisition points. Due to the influence of airflow, some distance between the acquisition points and the room boundaries should be given.https://www.mdpi.com/2075-5309/11/10/458temperature distributionpredictionCFDcontribution ratio of indoor climate (CRI)mobile sensors |
| spellingShingle | Yanan Zhao Zihan Zang Weirong Zhang Shen Wei Yingli Xuan Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors Buildings temperature distribution prediction CFD contribution ratio of indoor climate (CRI) mobile sensors |
| title | Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors |
| title_full | Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors |
| title_fullStr | Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors |
| title_full_unstemmed | Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors |
| title_short | Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors |
| title_sort | predicting indoor temperature distribution based on contribution ratio of indoor climate cri and mobile sensors |
| topic | temperature distribution prediction CFD contribution ratio of indoor climate (CRI) mobile sensors |
| url | https://www.mdpi.com/2075-5309/11/10/458 |
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