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다수 가전기기 유효전력의 스팩토그램 분석 및 LSTM기반의 전력 분해 알고리즘

Spectogram analysis of active power of appliances and LSTM-based Energy Disaggregation

김현철|김승윤|김임규|신상용
한국융합학회 (Korea Convergence Society)|2021.02|한국융합학회논문지 (Journal of the Korea Convergence Society)|제12권 제2호|pp. 21-28|DOI https://doi.org/10.15207/JKCS.2021.12.2.021

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키워드

LSTM에너지 분해Energy disaggregation비침입 부하 모니터링(NILM)스마트 그리드 시스템스팩토그램NILM(Non-Intrusive Load Monitoring)Smart Grid SystemSpectogram
주제분류학제간연구

초록

본 연구에서는 가전기기 5종에 대해 실제 측정 전력 데이터를 이용하여 딥러닝 기반의 NILM 기법을 제안하고 그 효용성을 검증 하고자 한다. 약 3주간 중앙 전력 측정 장치 및 5종 가전기기(냉장고, 인덕션, TV, 세탁기, 공기청정기)의 유효전력을 개별 측정하였다. 실측 데이터의 전처리 방법을 소개하고 Spectogram 분석을 통해 가전 기기별 특징을 분석하였다. 가전기기별 특징을 학습 데이터셋으로 구성하였다. 중앙 전력 측정 기기와 가전기기 5종에서 측정된 모든 전력 데이터를 시계열 매핑하여 시계열 데이터 분석에 우수한 RNN 계열의 LSTM 신경망을 이용해 학습을 수행하였다. 메인 중앙 전력 측정 장치의 전력 데이터만으로도 5종 전력 신호를 분해해낼 수 있는 알고리즘을 제안하였다.

Abstract

In this study, we propose a deep learning-based NILM technique using actual measured power data for 5 kinds of home appliances and verify its effectiveness. For about 3 weeks, the active power of the central power measuring device and five kinds of home appliances (refrigerator, induction, TV, washing machine, air cleaner) was individually measured. The preprocessing method of the measured data was introduced, and characteristics of each household appliance were analyzed through spectogram analysis. The characteristics of each household appliance are organized into a learning data set. All the power data measured by the central power measuring device and 5 kinds of home appliances were time-series mapping, and training was performed using a LSTM neural network, which is excellent for time series data prediction. An algorithm that can disaggregate five types of energies using only the power data of the main central power measuring device is proposed.

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