Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter Data
<b>Background:</b> Accurately identifying the socio-demographic information of customers is crucial for utilities. It enables them to efficiently deliver personalized energy services and manage distribution networks. In recent years, machine learning-based data-driven methods have gained...
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MDPI AG
2025-01-01
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| author | Zhirui Luo Qingqing Li Ruobin Qi Jun Zheng |
| author_facet | Zhirui Luo Qingqing Li Ruobin Qi Jun Zheng |
| author_sort | Zhirui Luo |
| collection | DOAJ |
| description | <b>Background:</b> Accurately identifying the socio-demographic information of customers is crucial for utilities. It enables them to efficiently deliver personalized energy services and manage distribution networks. In recent years, machine learning-based data-driven methods have gained popularity compared to traditional survey-based approaches, owing to their time and cost efficiency, as well as the availability of a large amount of high-frequency smart meter data. <b>Methods:</b> In this paper, we propose a new method that harnesses the power of neural architecture search to automatically design deep neural network architectures tailored for identifying various socio-demographic information of customers using smart meter data. We designed a search space based on a novel channel attention fully convolutional network architecture. Furthermore, we developed a search algorithm based on Bayesian optimization to effectively explore the space and identify high-performing architectures. <b>Results:</b> The performance of the proposed method was evaluated and compared with a set of machine learning and deep learning baseline methods using a smart meter dataset widely used in this research area. Our results show that the deep neural network architectures designed automatically by our proposed method significantly outperform all baseline methods in addressing the socio-demographic questions investigated in our study. |
| format | Article |
| id | doaj-art-d6d69b9876fb4cafa65bb6571afd65d6 |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-d6d69b9876fb4cafa65bb6571afd65d62025-01-24T13:17:22ZengMDPI AGAI2673-26882025-01-0161910.3390/ai6010009Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter DataZhirui Luo0Qingqing Li1Ruobin Qi2Jun Zheng3Department of Computer Science and Engineering, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USADepartment of Computer and Information Sciences, Towson University, Towson, MD 21252, USADepartment of Computer Science, California State University Northridge, Northridge, CA 91330, USADepartment of Computer Science and Engineering, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA<b>Background:</b> Accurately identifying the socio-demographic information of customers is crucial for utilities. It enables them to efficiently deliver personalized energy services and manage distribution networks. In recent years, machine learning-based data-driven methods have gained popularity compared to traditional survey-based approaches, owing to their time and cost efficiency, as well as the availability of a large amount of high-frequency smart meter data. <b>Methods:</b> In this paper, we propose a new method that harnesses the power of neural architecture search to automatically design deep neural network architectures tailored for identifying various socio-demographic information of customers using smart meter data. We designed a search space based on a novel channel attention fully convolutional network architecture. Furthermore, we developed a search algorithm based on Bayesian optimization to effectively explore the space and identify high-performing architectures. <b>Results:</b> The performance of the proposed method was evaluated and compared with a set of machine learning and deep learning baseline methods using a smart meter dataset widely used in this research area. Our results show that the deep neural network architectures designed automatically by our proposed method significantly outperform all baseline methods in addressing the socio-demographic questions investigated in our study.https://www.mdpi.com/2673-2688/6/1/9neural architecture searchfully convolutional networkattentionBayesian optimizationsocio-demographic information identificationsmart meter |
| spellingShingle | Zhirui Luo Qingqing Li Ruobin Qi Jun Zheng Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter Data AI neural architecture search fully convolutional network attention Bayesian optimization socio-demographic information identification smart meter |
| title | Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter Data |
| title_full | Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter Data |
| title_fullStr | Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter Data |
| title_full_unstemmed | Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter Data |
| title_short | Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter Data |
| title_sort | designing channel attention fully convolutional networks with neural architecture search for customer socio demographic information identification using smart meter data |
| topic | neural architecture search fully convolutional network attention Bayesian optimization socio-demographic information identification smart meter |
| url | https://www.mdpi.com/2673-2688/6/1/9 |
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