Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption

As the fertility rate declines, it becomes increasingly necessary for governments to guide power companies in introducing preferential tariffs to encourage nuclear families to have children. However, traditional household statistics for residential households are time-consuming and insufficient for...

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Bibliographic Details
Published in:Applied sciences Vol. 13; no. 11; p. 6602
Main Authors: Huang, Zhaoxiang, Wang, Hangjun
Format: Journal Article
Language:English
Published: Basel MDPI AG 29.05.2023
Subjects:
Accuracy
Algorithms
Artificial intelligence
Computational linguistics
Cost control
Datasets
deep forest
Deep learning
Electricity
Energy consumption
Energy management
Energy use
Family
Fertility
fertility rate
Households
Language processing
lean management
Load
Machine learning
Natural language interfaces
Neural networks
Nuclear family
Public utilities
residential electricity consumption
Subsidies
Tariffs
Wavelet transforms
China
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:As the fertility rate declines, it becomes increasingly necessary for governments to guide power companies in introducing preferential tariffs to encourage nuclear families to have children. However, traditional household statistics for residential households are time-consuming and insufficient for enterprises seeking to adopt intelligent marketing schemes for different types of households. To address these issues, this paper proposes a nuclear family type identification method for residential electricity consumption based on a deep forest algorithm. The method first classifies nuclear households according to the number of children in them. Then, features are selected by combining the daily 48-point load and prior knowledge of nuclear families. The Pearson correlation coefficient and random forest importance ranking are used to remove features with low correlation and low importance. Additionally, features are classified based on their importance, and the number of features is balanced by stratified sampling to optimize the multi-granularity scan results and improve the model’s generalization. Finally, the improved cascade forest with feature input replacement base learner is trained, and the model is evaluated using accuracy evaluation metrics.The experimental results demonstrate that the proposed model accurately recognizes the number of children in different nuclear families and can be used in power companies to improve lean management. The results show that the improved method is effective in improving recognition com-pared to the original deep forest method, with recognition accuracy 5.1% higher than the random forest method and 0.7% higher than the deep forest method, reaching 94%.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app13116602