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Analysis of Apartment Power Consumption and Forecast of Power Consumption Based on Deep Learning
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공동주택 전력 소비 데이터 분석 및 딥러닝을 사용한 전력 소비 예측

논문 기본 정보

Type
Academic journal
Author
Namjo Yoo (Hankuk University of Foreign Studies) Eunae Lee (Hankuk University of Foreign Studies) Beom Jin Chung (Seoul National University of Science and Technology) Dong Sik Kim (Hankuk University of Foreign Studies)
Journal
Institute of Korean Electrical and Electronics Engineers Journal of IKEEE Vol.23 No.4 KCI Accredited Journals
Published
2019.12
Pages
258 - 265 (8page)

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Analysis of Apartment Power Consumption and Forecast of Power Consumption Based on Deep Learning
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Research history (3)

2020

Analysis of apartment data and electricity price effect and power consumption pattern, and forecast of power consumption using deep learning
유남조 2020.01 Thesis

2019

Analysis of Apartment Power Consumption and Forecast of Power Consumption Based on Deep Learning
Namjo Yoo ,  Eunae Lee ,  Beom Jin Chung and others 1 Journal of IKEEE 2019.12 Academic journal
Analysis of Apartment AMI Data and Power Consumption Using K-Means Algorithm
Namjo Yoo ,  Eunae Lee ,  Beom Jin Chung and others 1 대한전자공학회 학술대회 2019.11 Proceeding
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Abstract· Keywords

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In order to increase energy efficiency, developments of the advanced metering infrastructure (AMI) in the smart grid technology have recently been actively conducted. An essential part of AMI is analyzing power consumption and forecasting consumption patterns. In this paper, we analyze the power consumption and summarized the data errors. Monthly power consumption patterns are also analyzed using the k-means clustering algorithm. Forecasting the consumption pattern by each household is difficult. Therefore, we first classify the data into 100 clusters and then predict the average of the next day as the daily average of the clusters based on the deep neural network. Using practically collected AMI data, we analyzed the data errors and could successfully conducted power forecasting based on a clustering technique.
#advanced metering infrastructure #deep neural network #load data #load forecasting #k-means clustering #meter reading

Contents

Abstract
요약
Ⅰ. 서론
Ⅱ. 전력 소비 데이터 분석
Ⅲ. 전력 소비 패턴 분석
Ⅳ. 딥러닝을 이용한 전력 소비 패턴 예측
Ⅴ. 실험 결과
Ⅵ. 결론
References

References (14)

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명노길 / 2010 / 한전의 지능형 원격검침 시스템에 대한 연구 / 한국통신학회논문지B 35(8):1251~1258 google schola 정준홍 / 2018 / AMI 시스템의 효과적인 계량정보 수집방법 / 한국통신학회논문지 43(8):1311~1320 dbpia Dong Kim / 2019 / Statistical Learning for Service Quality Estimation in Broadband PLC AMI / Energies 12(4):684 Crossref S. M. Jeong / 2014 / A consideration on the value evaluation of AMI data as big data / Proc. KIEE Summer Conf. :45~46 google schola Y. I. Kim / 2014 / Apparatus and method for data processing of energy management system / Proc. KIEE Summer Conf. :192~193 google schola

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