Predicted Mean Vote of Subway Car Environment Based on Machine Learning
The thermal comfort of passengers in the carriage cannot be ignored. Thus, this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote (PM...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020028 |
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| author | Kangkang Huang Shihua Lu Xinjun Li Ke Feng Weiwei Chen Yi Xia |
| author_facet | Kangkang Huang Shihua Lu Xinjun Li Ke Feng Weiwei Chen Yi Xia |
| author_sort | Kangkang Huang |
| collection | DOAJ |
| description | The thermal comfort of passengers in the carriage cannot be ignored. Thus, this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote (PMV) index. Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro. Support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were used to build four models. This research aims to select the most appropriate input variables for the predictive model. All possible combinations of 11 input variables were used to determine the most accurate model, with variable selection for each model comprising 102 350 iterations. In the PMV prediction, the RF model was the best when using the correlation coefficients square (R2) as the evaluation indicator (R2: 0.7680, mean squared error (MSE): 0.2868). The variables include clothing temperature (CT), convective heat transfer coefficient between the surface of the human body and the environment (CHTC), black bulb temperature (BBT), and thermal resistance of clothes (TROC). The RF model with MSE as the evaluation index also had the highest accuracy (R2: 0.7676, MSE: 0.2836). The variables include clothing surface area coefficient (CSAC), CT, BBT, and air velocity (AV). The results show that the RF model can efficiently predict the PMV of the subway car environment. |
| format | Article |
| id | doaj-art-923183515b454c768e61978d18996ab3 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-923183515b454c768e61978d18996ab32025-02-02T07:53:41ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-03-01619210510.26599/BDMA.2022.9020028Predicted Mean Vote of Subway Car Environment Based on Machine LearningKangkang Huang0Shihua Lu1Xinjun Li2Ke Feng3Weiwei Chen4Yi Xia5School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210046, ChinaSchool of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210046, ChinaSchool of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210046, ChinaSchool of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210046, ChinaSchool of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210046, ChinaSchool of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210046, ChinaThe thermal comfort of passengers in the carriage cannot be ignored. Thus, this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote (PMV) index. Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro. Support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were used to build four models. This research aims to select the most appropriate input variables for the predictive model. All possible combinations of 11 input variables were used to determine the most accurate model, with variable selection for each model comprising 102 350 iterations. In the PMV prediction, the RF model was the best when using the correlation coefficients square (R2) as the evaluation indicator (R2: 0.7680, mean squared error (MSE): 0.2868). The variables include clothing temperature (CT), convective heat transfer coefficient between the surface of the human body and the environment (CHTC), black bulb temperature (BBT), and thermal resistance of clothes (TROC). The RF model with MSE as the evaluation index also had the highest accuracy (R2: 0.7676, MSE: 0.2836). The variables include clothing surface area coefficient (CSAC), CT, BBT, and air velocity (AV). The results show that the RF model can efficiently predict the PMV of the subway car environment.https://www.sciopen.com/article/10.26599/BDMA.2022.9020028predicted mean voterandom forestvariable selectionthermal comfort |
| spellingShingle | Kangkang Huang Shihua Lu Xinjun Li Ke Feng Weiwei Chen Yi Xia Predicted Mean Vote of Subway Car Environment Based on Machine Learning Big Data Mining and Analytics predicted mean vote random forest variable selection thermal comfort |
| title | Predicted Mean Vote of Subway Car Environment Based on Machine Learning |
| title_full | Predicted Mean Vote of Subway Car Environment Based on Machine Learning |
| title_fullStr | Predicted Mean Vote of Subway Car Environment Based on Machine Learning |
| title_full_unstemmed | Predicted Mean Vote of Subway Car Environment Based on Machine Learning |
| title_short | Predicted Mean Vote of Subway Car Environment Based on Machine Learning |
| title_sort | predicted mean vote of subway car environment based on machine learning |
| topic | predicted mean vote random forest variable selection thermal comfort |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020028 |
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