Artificial Neural Network Modeling for Predicting and Evaluating the Mean Radiant Temperature around Buildings on Hot Summer Days
In recent years, the phenomenon of urban warming has become increasingly serious, and with the number of urban residents increasing, the risk of heatstroke in extreme weather has become higher than ever. In order to mitigate urban warming and adapt to it, many researchers have been paying increasing...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/12/5/513 |
| _version_ | 1797501172919042048 |
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| author | Yuquan Xie Wen Hu Xilin Zhou Shuting Yan Chuancheng Li |
| author_facet | Yuquan Xie Wen Hu Xilin Zhou Shuting Yan Chuancheng Li |
| author_sort | Yuquan Xie |
| collection | DOAJ |
| description | In recent years, the phenomenon of urban warming has become increasingly serious, and with the number of urban residents increasing, the risk of heatstroke in extreme weather has become higher than ever. In order to mitigate urban warming and adapt to it, many researchers have been paying increasing attention to outdoor thermal comfort. The mean radiant temperature (MRT) is one of the most important variables affecting human thermal comfort in outdoor urban spaces. The purpose of this paper is to predict the distribution of MRT around buildings based on a commonly used multilayer neural network (MLNN) that is optimized by genetic algorithms (GA) and backpropagation (BP) algorithms. Weather data from 2014 to 2018 together with the related indexes of the grid were selected as the input parameters for neural network training, and the distribution of the MRT around buildings in 2019 was predicted. This study obtained very high prediction accuracy, which can be combined with sensitivity analysis methods to analyze the important input parameters affecting the MRT on hot summer days (the days with the highest air temperature over 30 °C). This has significant implications for the optimization strategies for future building and urban designers to improve the thermal conditions around buildings. |
| first_indexed | 2024-03-10T03:14:33Z |
| format | Article |
| id | doaj.art-6da3f5beb1634c28849bbff052df007d |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-10T03:14:33Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-6da3f5beb1634c28849bbff052df007d2023-11-23T10:19:09ZengMDPI AGBuildings2075-53092022-04-0112551310.3390/buildings12050513Artificial Neural Network Modeling for Predicting and Evaluating the Mean Radiant Temperature around Buildings on Hot Summer DaysYuquan Xie0Wen Hu1Xilin Zhou2Shuting Yan3Chuancheng Li4School of Civil Engineering and Architecture, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, ChinaDepartment of Architecture and Building Science, Graduate School of Engineering, Tohoku University, Sendai 980-8579, JapanSchool of Civil Engineering and Architecture, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, 693 Xiongchu Road, Wuhan 430205, ChinaSchool of Civil Engineering and Architecture, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, ChinaIn recent years, the phenomenon of urban warming has become increasingly serious, and with the number of urban residents increasing, the risk of heatstroke in extreme weather has become higher than ever. In order to mitigate urban warming and adapt to it, many researchers have been paying increasing attention to outdoor thermal comfort. The mean radiant temperature (MRT) is one of the most important variables affecting human thermal comfort in outdoor urban spaces. The purpose of this paper is to predict the distribution of MRT around buildings based on a commonly used multilayer neural network (MLNN) that is optimized by genetic algorithms (GA) and backpropagation (BP) algorithms. Weather data from 2014 to 2018 together with the related indexes of the grid were selected as the input parameters for neural network training, and the distribution of the MRT around buildings in 2019 was predicted. This study obtained very high prediction accuracy, which can be combined with sensitivity analysis methods to analyze the important input parameters affecting the MRT on hot summer days (the days with the highest air temperature over 30 °C). This has significant implications for the optimization strategies for future building and urban designers to improve the thermal conditions around buildings.https://www.mdpi.com/2075-5309/12/5/513backpropagation algorithmsensitivity analysisoutdoor thermal environmentmean radiant temperaturegenetic algorithms |
| spellingShingle | Yuquan Xie Wen Hu Xilin Zhou Shuting Yan Chuancheng Li Artificial Neural Network Modeling for Predicting and Evaluating the Mean Radiant Temperature around Buildings on Hot Summer Days Buildings backpropagation algorithm sensitivity analysis outdoor thermal environment mean radiant temperature genetic algorithms |
| title | Artificial Neural Network Modeling for Predicting and Evaluating the Mean Radiant Temperature around Buildings on Hot Summer Days |
| title_full | Artificial Neural Network Modeling for Predicting and Evaluating the Mean Radiant Temperature around Buildings on Hot Summer Days |
| title_fullStr | Artificial Neural Network Modeling for Predicting and Evaluating the Mean Radiant Temperature around Buildings on Hot Summer Days |
| title_full_unstemmed | Artificial Neural Network Modeling for Predicting and Evaluating the Mean Radiant Temperature around Buildings on Hot Summer Days |
| title_short | Artificial Neural Network Modeling for Predicting and Evaluating the Mean Radiant Temperature around Buildings on Hot Summer Days |
| title_sort | artificial neural network modeling for predicting and evaluating the mean radiant temperature around buildings on hot summer days |
| topic | backpropagation algorithm sensitivity analysis outdoor thermal environment mean radiant temperature genetic algorithms |
| url | https://www.mdpi.com/2075-5309/12/5/513 |
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