Abstract
Greenhouse effects caused by insulating gases with high global warming potential (GWP) pose a severe threat to the environment therefore searching for their alternatives is imperative. In this study, random forest classifiers were utilized to build a classification model for prediction of global warming potential of insulating gases using random forest classifiers. The accuracy and the predictive power of the model were thoroughly evaluated.
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All the datasets used in this study can be downloaded free of charge at this link: https://github.com/gkxiao/GAS.
Abbreviations
- DS:
-
Dielectric strengths
- GWP:
-
Global warming potential
- SF6:
-
Sulfur hexafluoride
- IPCC:
-
Inter-governmental Panel on Climate Change
- DFT:
-
Density functional theory
- RMSD:
-
Root mean squared deviation
- RF:
-
Radiative forcing
- TH:
-
Time horizon
- HOMO:
-
Highest occupied molecular orbital
- LUMO:
-
Lowest unoccupied molecular orbital
- TPR:
-
True positive rate
- FPT:
-
False positive rate
- TNR:
-
True negative rate
- FNR:
-
False negative rate
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Acknowledgements
The authors thank Dr. Bin Lin from Shenyang Pharmaceutical University for helpful discussion and advice during the preparation of the manuscript. The current work is supported by the science and technology project of China Southern Power Grid (No. GDKJXM20170043).
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Sun, D. et al. (2023). Prediction of Global Warming Potential of Insulating Gases Using Random Forest Classifiers. In: Dong, X., Yang, Q., Ma, W. (eds) The proceedings of the 10th Frontier Academic Forum of Electrical Engineering (FAFEE2022). FAFEE 2022. Lecture Notes in Electrical Engineering, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-99-3408-9_65
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