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A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data

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  • Wen, Hanguan
  • Liu, Xiufeng
  • Yang, Ming
  • Lei, Bo
  • Xu, Cheng
  • Chen, Zhe

Abstract

Demand-side management (DSM) is crucial to smart energy systems. This paper presents a data-driven approach for understanding the relationship between energy consumption patterns and household characteristics to better provide DSM services. The proposed method uses a robust learning fuzzy c-Means clustering algorithm to automatically generate the optimal number of customer groups for DSM, and then uses symmetric uncertainty techniques to identify the identified load patterns and socio-demographic characteristics as the features for training a deep learning model. The model obtained can be used to predict the possibility of DSM group membership for a given household. This approach can be applied even in situations where smart meter data are not available, such as when new customers are added to the system or when residents change, or due to privacy concerns. The proposed model is evaluated comprehensively, including prediction accuracy, comparison with other baselines, and case studies for DSM. The results demonstrate the usefulness of weekly energy consumption data and associated household socio-demographic information for distinguishing between different consumer groups, the effectiveness of the proposed model, and the potential for targeted DSM strategies such as time-of-use pricing, energy efficiency measures, and demand response programs.

Suggested Citation

  • Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223029870
    DOI: 10.1016/j.energy.2023.129593
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    2. Wen, Hanguan & Liu, Xiufeng & Lei, Bo & Yang, Ming & Cheng, Xu & Chen, Zhe, 2025. "A privacy-preserving heterogeneous federated learning framework with class imbalance learning for electricity theft detection," Applied Energy, Elsevier, vol. 378(PA).

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