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A Multitiered Solution for Anomaly Detection in Edge Computing for Smart Meters.

Darmawan Utomo1,2, Pao-Ann Hsiung1

  • 1Computer Science and Information Engineering, National Chung Cheng University, No. 168, Sec. 1, University Rd., Minhsiung, Chiayi 62102, Taiwan.

Sensors (Basel, Switzerland)
|September 15, 2020
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Summary

This study introduces prediction techniques like Deep Neural Networks (DNN) for smart grids to forecast electricity usage anomalies weeks in advance. This allows for timely user checks and utility supply preparation, improving grid management.

Keywords:
AMIDNNHDBSCANK-meansLSTMSVRanomaly detectioncloudedgefogimbalancedk-NNsmart meter

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Data Science

Background:

  • Smart grids require real-time anomaly detection from smart meters.
  • Current technology faces communication and storage bottlenecks, preventing sub-minute data transmission.
  • This limits real-time anomaly mitigation.

Purpose of the Study:

  • To propose and evaluate prediction techniques for forecasting electricity usage anomalies.
  • To enable proactive user checks and utility supply management.
  • To reduce latency in smart grid data analysis through edge and cloud computing.

Main Methods:

  • Utilized Deep Neural Network (DNN), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN) for prediction.
  • Employed daily prediction timesteps with sliding windows.
  • Evaluated clustering algorithms including K-means, intersection K-means, and HDBSCAN.
  • Implemented an Edge Meter Data Management System (MDMS) and Cloud-MDMS architecture.

Main Results:

  • Deep Neural Network (DNN) demonstrated the best performance on Raspberry Pi.
  • DNN achieved the shortest latency of 1.25 ms.
  • DNN resulted in a persistent file size of 159 kB at 128 timesteps.

Conclusions:

  • DNN is an effective technique for predicting electricity usage anomalies in smart grids.
  • The proposed edge and cloud MDMS architecture successfully reduces latency.
  • The findings support proactive anomaly management in smart grid systems.