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Research on large-scale data anomaly detection based on edge computing and LSTM method

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  • Dan Ji

Abstract

This study proposes a comprehensive solution that integrates edge computing and deep learning techniques to address the anomaly detection problem in large-scale power load data. The model introduced herein combines Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Random Forest (RF) models to efficiently capture the spatiotemporal characteristics of power load data. Moreover, by conducting data preprocessing and feature extraction on edge devices, this approach significantly reduces data transmission latency and bandwidth requirements. The research findings indicate that the proposed model achieves an anomaly detection accuracy of 93% on large-scale datasets.

Suggested Citation

  • Dan Ji, 2025. "Research on large-scale data anomaly detection based on edge computing and LSTM method," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 1292-1299.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:1292-1299.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf086
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