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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/11/6602 |
| _version_ | 1797597897668165632 |
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| author | Zhaoxiang Huang Hangjun Wang |
| author_facet | Zhaoxiang Huang Hangjun Wang |
| author_sort | Zhaoxiang Huang |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-11T03:11:52Z |
| format | Article |
| id | doaj.art-d2298424166943279b9e46262d140a38 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:11:52Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d2298424166943279b9e46262d140a382023-11-18T07:34:16ZengMDPI AGApplied Sciences2076-34172023-05-011311660210.3390/app13116602Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power ConsumptionZhaoxiang Huang0Hangjun Wang1College of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311300, ChinaCollege of Engineering and Technology, Jiyang College of Zhejiang Agriculture and Forestry University, Shaoxing 311800, ChinaAs 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%.https://www.mdpi.com/2076-3417/13/11/6602residential electricity consumptionlean managementdeep forestnuclear familyfertility rate |
| spellingShingle | Zhaoxiang Huang Hangjun Wang Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption Applied Sciences residential electricity consumption lean management deep forest nuclear family fertility rate |
| title | Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption |
| title_full | Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption |
| title_fullStr | Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption |
| title_full_unstemmed | Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption |
| title_short | Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption |
| title_sort | nuclear family type identification based on deep forest algorithm in residential power consumption |
| topic | residential electricity consumption lean management deep forest nuclear family fertility rate |
| url | https://www.mdpi.com/2076-3417/13/11/6602 |
| work_keys_str_mv | AT zhaoxianghuang nuclearfamilytypeidentificationbasedondeepforestalgorithminresidentialpowerconsumption AT hangjunwang nuclearfamilytypeidentificationbasedondeepforestalgorithminresidentialpowerconsumption |