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      딥 러닝 기반 스마트 미터기 구현 = Implementation of Smart Metering System Based on Deep Learning

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      https://www.riss.kr/link?id=A105903031

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      다국어 초록 (Multilingual Abstract)

      Recently, studies have been actively conducted to reduce spare power that is unnecessarily generated or wasted in existing power systems and to improve energy use efficiency. In this study, smart meter, which is one of the element technologies of smart grid, is implemented to improve the efficiency of energy use by controlling power of electric devices, and predicting trends of energy usage based on deep learning. We propose and develop an algorithm that controls the power of the electric devices by comparing the predicted power consumption with the real-time power consumption. To verify the performance of the proposed smart meter based on the deep running, we constructed the actual power consumption environment and obtained the power usage data in real time, and predicted the power consumption based on the deep learning model. We confirmed that the unnecessary power consumption can be reduced and the energy use efficiency increases through the proposed deep learning-based smart meter.
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      Recently, studies have been actively conducted to reduce spare power that is unnecessarily generated or wasted in existing power systems and to improve energy use efficiency. In this study, smart meter, which is one of the element technologies of smar...

      Recently, studies have been actively conducted to reduce spare power that is unnecessarily generated or wasted in existing power systems and to improve energy use efficiency. In this study, smart meter, which is one of the element technologies of smart grid, is implemented to improve the efficiency of energy use by controlling power of electric devices, and predicting trends of energy usage based on deep learning. We propose and develop an algorithm that controls the power of the electric devices by comparing the predicted power consumption with the real-time power consumption. To verify the performance of the proposed smart meter based on the deep running, we constructed the actual power consumption environment and obtained the power usage data in real time, and predicted the power consumption based on the deep learning model. We confirmed that the unnecessary power consumption can be reduced and the energy use efficiency increases through the proposed deep learning-based smart meter.

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      참고문헌 (Reference)

      1 I. Sutskever, "Training recurrent neural networks" University of Toronto 2013

      2 J. Zheng, "Smart meters in smart grid: an overview" 57-64, 2013

      3 Q. Xiaoyun, "Short-term prediction of wind power based on deep long short-term memory" 1148-1152, 2016

      4 H. Salehinejad, "Recent advances in recurrent neural networks"

      5 Y. He, "Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism" 8 (8): 2505-2516, 2017

      6 Y. Bottou, "Large-Scale Kernel Machines" MIT Press 2007

      7 A. Pulver, "LSTM with working memory" 846-851, 2017

      8 I. Colak, "Introduction to smart grid" 30-34, 2016

      9 S. Yao, "Deep learning for the internet of things" 51 (51): 32-41, 2018

      10 I. Goodfellow, "Deep Learning" MIT Press 2016

      1 I. Sutskever, "Training recurrent neural networks" University of Toronto 2013

      2 J. Zheng, "Smart meters in smart grid: an overview" 57-64, 2013

      3 Q. Xiaoyun, "Short-term prediction of wind power based on deep long short-term memory" 1148-1152, 2016

      4 H. Salehinejad, "Recent advances in recurrent neural networks"

      5 Y. He, "Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism" 8 (8): 2505-2516, 2017

      6 Y. Bottou, "Large-Scale Kernel Machines" MIT Press 2007

      7 A. Pulver, "LSTM with working memory" 846-851, 2017

      8 I. Colak, "Introduction to smart grid" 30-34, 2016

      9 S. Yao, "Deep learning for the internet of things" 51 (51): 32-41, 2018

      10 I. Goodfellow, "Deep Learning" MIT Press 2016

      11 G. N. Srinivasa Prasanna, "Data communication over the smart grid" 273-279, 2009

      12 R. Morello, "A smart power meter to monitor energy flow in smart grid: The role of advanced sensing and IoT in the electric grid of the future" 17 (17): 7828-7837, 2017

      13 H. Yang, "A practical pricing approach to smart gird demand response based load classification" 9 (9): 179-190, 2018

      14 "A Joint Project of the EEI and AEIC Meter Committees, Smart meters and smart meter systems: A metering industry perspective"

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      유사연구자 (20) 활용도상위20명

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2024 평가예정 재인증평가 신청대상 (재인증)
      2021-01-01 평가 등재학술지 선정 (계속평가) KCI등재
      2020-12-01 평가 등재후보로 하락 (재인증) KCI등재후보
      2017-01-01 평가 등재학술지 선정 (계속평가) KCI등재
      2016-01-01 평가 등재후보학술지 유지 (계속평가) KCI등재후보
      2015-12-01 평가 등재후보로 하락 (기타) KCI등재후보
      2011-01-01 평가 등재 1차 FAIL (등재유지) KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2006-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2005-10-17 학술지명변경 외국어명 : 미등록 -> Journal of IKEEE KCI등재후보
      2005-05-30 학술지등록 한글명 : 전기전자학회논문지
      외국어명 : 미등록
      KCI등재후보
      2005-03-25 학회명변경 한글명 : (사) 한국전기전자학회 -> 한국전기전자학회
      영문명 : 미등록 -> Institute of Korean Electrical and Electronics Engineers
      KCI등재후보
      2005-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2004-01-01 평가 등재후보 1차 FAIL (등재후보1차) KCI등재후보
      2003-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.3 0.3 0.29
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.24 0.22 0.262 0.17
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