research-article

A Power Anomaly Detection Architecture Based on DNN

Authors:

Wei Geng,

Dongyu Liu,

Xiu CaoAuthors Info & Claims

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

Article No.: 30, Pages 1 - 5

https://doi.org/10.1145/3331453.3361641

Published: 22 October 2019 Publication History

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Abstract

Different from the traditional power data mining, it is proposed to use DNN (Deep Neural Networks) to deeply process the data, so as to discover power faults and abnormal behaviours in time. The result obtained ensures the normal operation of the power system; according to the characteristics of the user's electricity consumption data provided by the power company, the optimization methods such as oversampling are used to extract the maximum retained data information. At the same time, the accuracy of training is improved and the training complexity is reduced. A deep learning model is used to classify single point anomalies based on time series data. Agile AI (Artificial Intelligence) engineering architecture, offline batch data training and online model real-time detection are combined. Agile AI engineering is constantly improving accuracy. More importantly, the adaptability has been improved to ensure accuracy and efficiency in different scenarios.

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Cited By

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Hua FPeng XRuoyan X(2020)KLS-A: A Full-Life-Time Anomaly Detection Method2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE)10.1109/ICAICE51518.2020.00101(489-493)Online publication date: Oct-2020
https://doi.org/10.1109/ICAICE51518.2020.00101

Index Terms

A Power Anomaly Detection Architecture Based on DNN
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering
      2. Data stream mining

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Published In

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

October 2019

942 pages

ISBN:9781450362948

DOI:10.1145/3331453

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zeshui Xu

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2019

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CSAE 2019

CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering

October 22 - 24, 2019

Sanya, China

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Overall Acceptance Rate 368 of 770 submissions, 48%

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Hua FPeng XRuoyan X(2020)KLS-A: A Full-Life-Time Anomaly Detection Method2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE)10.1109/ICAICE51518.2020.00101(489-493)Online publication date: Oct-2020
https://doi.org/10.1109/ICAICE51518.2020.00101

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Anomaly Detection in Embedded Systems

Analysis of Deep Learning Models for Anomaly Detection in Time Series IoT Sensor Data

A cascade architecture for DoS attacks detection based on the wavelet transform