이상 전력 탐지를 위한 TCN-USAD
(TCN-USAD for Anomaly Power Detection)
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참고문헌
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초록
에너지 사용량의 증가와 친환경 정책으로 인해 건물 에너지를 효율적으로 소비할 필요가 있으며, 이를 위해 딥러닝 기반 이상 전력 탐지가 수행되고 있다. 수집이 어려운 이상치 데이터의 특징으로 인해 Recurrent Neural Network(RNN) 기반 오토인코더를 활용한 복원 에러 기반으로 이상 탐지가 수행되고 있으나, 시계열 특징을 온전히 학습하는데 시간이 오래 걸리고 학습 데이터의 노이즈에 민감하다는 단점이 있다. 본 논문에서는 이러한 한계를 극복하기 위해 Temporal Convolutional Network(TCN)과 UnSupervised Anomaly Detection for multivariate time series(USAD)를 결합한 TCN-USAD를 제안한다. 제안된 모델은 TCN 기반 오토인코더와 두 개의 디코더와 적대적 학습을 사용하는 USAD 구조를 활용하여 빠르게 시계열 특징을 온전히 학습할 수 있고 강건한 이상 탐지가 가능하다. TCN-USAD의 성능을 입증하기 위해 2개의 건물 전력 사용량 데이터 세트를 사용하여 비교 실험을 수행한 결과, TCN 기반 오토인코더는 RNN 기반 오토인코더 대비 빠르고 복원 성능이 우수하였으며, 이를 활용한 TCN-USAD는 다른 이상 탐지 모델 대비 약 20% 개선된 F1-Score를 달성하여 뛰어난 이상 탐지 성능을 보였다.
Abstract
Due to the increase in energy consumption, and eco-friendly policies, there is a need for efficient energy consumption in buildings. Anomaly power detection based on deep learning are being used. Because of the difficulty in collecting anomaly data, anomaly detection is performed using reconstruction error with a Recurrent Neural Network(RNN) based autoencoder. However, there are some limitations such as the long time required to fully learn temporal features and its sensitivity to noise in the train data. To overcome these limitations, this paper proposes the TCN-USAD, combined with Temporal Convolution Network(TCN) and UnSupervised Anomaly Detection for multivariate data(USAD). The proposed model using TCN-based autoencoder and the USAD structure, which uses two decoders and adversarial training, to quickly learn temporal features and enable robust anomaly detection. To validate the performance of TCN-USAD, comparative experiments were performed using two building energy datasets. The results showed that the TCN-based autoencoder can perform faster and better reconstruction than RNN-based autoencoder. Furthermore, TCN-USAD achieved 20% improved F1-Score over other anomaly detection models, demonstrating excellent anomaly detection performance.
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