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Research on the provincial-level centralized function of integrating neural networks with electricity billing data analysis

Author

Listed:
  • Fangchu Zhao
  • Xin Su
  • Xiaoxiao Lu
  • Feiya Si
  • Lingling Lang
  • Wenlei Sun

Abstract

This paper proposes a provincial-scale system integrating neural networks with electricity rate data analysis to enhance prediction accuracy and anomaly detection efficiency while ensuring user privacy. At its core is the electric rate analysis neural network (EANN), which combines LSTM and GCN to effectively capture the temporal dynamics of billing data and the relational structure among users. The system also introduces a privacy protection scheme based on personalized federated learning for secure cross-regional analysis. Experiments show that EANN improves prediction accuracy by 2.3% and reduces computational latency by 6.3% compared to traditional CNN–LSTM methods.

Suggested Citation

  • Fangchu Zhao & Xin Su & Xiaoxiao Lu & Feiya Si & Lingling Lang & Wenlei Sun, 2025. "Research on the provincial-level centralized function of integrating neural networks with electricity billing data analysis," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 1-22.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:1-22.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctae283
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