Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption
Abstract
:1. Introduction
2. Family Type Identification Model
2.1. Stratified Sampling
2.2. Multi-Grained Scanning
2.3. Cascade Forest
2.4. Identification Process
3. Experimental
3.1. Data Cleaning
3.2. Feature Engineering
3.3. Identification
4. Example Analysis
4.1. Different Time Periods’ Experiment
4.2. Improved Algorithms’ Experiment
5. Discussion and Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Family Type | 2 Adults and 0 Child | 2 Adults and 1 Child | 2 Adults and 2 Child | 2 Adults and 3 Child | Other Families |
---|---|---|---|---|---|
Number of households | 682 | 127 | 74 | 61 | 100 |
Label | 0 | 1 | 2 | 3 | 4 |
Actual | Predicted | |
---|---|---|
Positive | Negative | |
Positive | TP | FN |
Negative | FP | TN |
Models | Acc (%) |
---|---|
Random Forest | 88.9 |
Deep Forest | 93.3 |
Deep Forest Based On Stratified Sampling | 93.6 |
Improved Deep Forest | 93.8 |
Deep Forest Based On Improved Stratified Sampling | 94.0 |
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Huang, Z.; Wang, H. Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption. Appl. Sci. 2023, 13, 6602. https://doi.org/10.3390/app13116602
Huang Z, Wang H. Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption. Applied Sciences. 2023; 13(11):6602. https://doi.org/10.3390/app13116602
Chicago/Turabian StyleHuang, Zhaoxiang, and Hangjun Wang. 2023. "Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption" Applied Sciences 13, no. 11: 6602. https://doi.org/10.3390/app13116602
APA StyleHuang, Z., & Wang, H. (2023). Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption. Applied Sciences, 13(11), 6602. https://doi.org/10.3390/app13116602