Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework
Abstract This study aims to develop an efficient model to predict the arrears behavior of electricity users by integrating multi-scale feature learning with a backpropagation (BP) neural network. The goal is to provide accurate early warning systems and enhanced risk management tools for power compa...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-024-00441-0 |
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| _version_ | 1841544246490824704 |
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| author | Liang Yu Yuanshen Hong Hua Lin Xu Jiang Ziming Song |
| author_facet | Liang Yu Yuanshen Hong Hua Lin Xu Jiang Ziming Song |
| author_sort | Liang Yu |
| collection | DOAJ |
| description | Abstract This study aims to develop an efficient model to predict the arrears behavior of electricity users by integrating multi-scale feature learning with a backpropagation (BP) neural network. The goal is to provide accurate early warning systems and enhanced risk management tools for power companies. The BP neural network algorithm adjusts weights to minimize prediction errors, while multi-scale feature learning captures the diversity and regularity of user behavior by extracting data from various time dimensions, such as daily, weekly, and monthly intervals. First, electricity usage and weather data from the UMass Smart Dataset are preprocessed, including steps such as data cleaning, standardization, and normalization. Next, features are extracted across three time scales—daily, weekly, and monthly. These features are then input into the BP neural network model using the multi-scale feature learning method. A hierarchical neural network structure is designed to address the characteristics of different scales in distinct layers. Key model parameters are optimized, and a sensitivity analysis is conducted. The experimental results demonstrate that the BP neural network model incorporating multi-scale features outperforms traditional BP neural network models and other control models in several evaluation metrics. Specifically, the Gini coefficient is 0.55, the Kolmogorov-Smirnov statistic is 0.60, the Matthews correlation coefficient is 0.45, and specificity is 0.82. These results indicate that the proposed method offers significant improvements in capturing user behavior patterns and enhancing prediction accuracy. The study concludes that the effective fusion of multi-scale features not only enhances the model’s prediction performance but also strengthens its generalization ability. This method provides an advanced risk management tool for power companies, helping to increase the operational efficiency of smart grids and encouraging further research toward greater intelligence in the field. |
| format | Article |
| id | doaj-art-cfdb264f49ec4840abd03d317bc0210f |
| institution | Kabale University |
| issn | 2520-8942 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-cfdb264f49ec4840abd03d317bc0210f2025-01-12T12:41:44ZengSpringerOpenEnergy Informatics2520-89422025-01-018112510.1186/s42162-024-00441-0Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment frameworkLiang Yu0Yuanshen Hong1Hua Lin2Xu Jiang3Ziming Song4State Grid Beijing Customer Service CenterState Grid Beijing Customer Service CenterState Grid Beijing Electric Power CompanyState Grid Beijing Electric Power CompanyState Grid Beijing Customer Service CenterAbstract This study aims to develop an efficient model to predict the arrears behavior of electricity users by integrating multi-scale feature learning with a backpropagation (BP) neural network. The goal is to provide accurate early warning systems and enhanced risk management tools for power companies. The BP neural network algorithm adjusts weights to minimize prediction errors, while multi-scale feature learning captures the diversity and regularity of user behavior by extracting data from various time dimensions, such as daily, weekly, and monthly intervals. First, electricity usage and weather data from the UMass Smart Dataset are preprocessed, including steps such as data cleaning, standardization, and normalization. Next, features are extracted across three time scales—daily, weekly, and monthly. These features are then input into the BP neural network model using the multi-scale feature learning method. A hierarchical neural network structure is designed to address the characteristics of different scales in distinct layers. Key model parameters are optimized, and a sensitivity analysis is conducted. The experimental results demonstrate that the BP neural network model incorporating multi-scale features outperforms traditional BP neural network models and other control models in several evaluation metrics. Specifically, the Gini coefficient is 0.55, the Kolmogorov-Smirnov statistic is 0.60, the Matthews correlation coefficient is 0.45, and specificity is 0.82. These results indicate that the proposed method offers significant improvements in capturing user behavior patterns and enhancing prediction accuracy. The study concludes that the effective fusion of multi-scale features not only enhances the model’s prediction performance but also strengthens its generalization ability. This method provides an advanced risk management tool for power companies, helping to increase the operational efficiency of smart grids and encouraging further research toward greater intelligence in the field.https://doi.org/10.1186/s42162-024-00441-0Electricity users’ arrears behavior predictionBP neural networkMultiscale feature learningSmart grid |
| spellingShingle | Liang Yu Yuanshen Hong Hua Lin Xu Jiang Ziming Song Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework Energy Informatics Electricity users’ arrears behavior prediction BP neural network Multiscale feature learning Smart grid |
| title | Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework |
| title_full | Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework |
| title_fullStr | Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework |
| title_full_unstemmed | Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework |
| title_short | Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework |
| title_sort | arrears behavior prediction of power users based on bp neural network and multi scale feature learning a refined risk assessment framework |
| topic | Electricity users’ arrears behavior prediction BP neural network Multiscale feature learning Smart grid |
| url | https://doi.org/10.1186/s42162-024-00441-0 |
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