Regression Analysis of Heat Release Rate for Box-Type Power Bank Based on Experimental and Machine Learning Methods
In recent years, new fire loads dominated by power banks have caused multiple fire incidents in transportation hubs, posing severe challenges to fire safety. This study uses experimental testing and machine learning regression analysis to explore the heat release rate (HRR) characteristics and influ...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Fire |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-6255/8/6/209 |
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| Summary: | In recent years, new fire loads dominated by power banks have caused multiple fire incidents in transportation hubs, posing severe challenges to fire safety. This study uses experimental testing and machine learning regression analysis to explore the heat release rate (HRR) characteristics and influencing factors of box-type power banks under fire conditions. A 1 MW calorimeter was used to conduct four sets of experiments involving a total of 15 box-type power banks, measuring the HRR and analyzing its correlation with oxygen consumption, carbon dioxide generation, smoke temperature, and the fire growth rate. Based on the experimental data, HRR prediction models were constructed using decision tree regression (DT), K-nearest neighbor regression (KNN), and linear regression (LR). The results indicate that the DT model performs best in HRR prediction (MAE = 0.4889, MSE = 0.7414, RMSE = 0.8571, R<sup>2</sup> = 0.9991), effectively capturing the nonlinear variation patterns in the HRR. The correlation analysis and regression analysis conducted in this study provide crucial data support for fire combustion characteristics research, fire risk assessment, and fire safety optimization. Furthermore, the findings provide crucial data support for research on fire combustion characteristics and data-driven fire risk assessment, which may serve as a foundation for future AI-based real-time fire detection applications. |
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| ISSN: | 2571-6255 |