기계학습 기반 전력 사용 이상 상태 탐지
Machine Learning-based Power Usage Abnormality Detection
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초록
본 논문에서는 연구에서는 국내 프랜차이즈 가맹 편의점의 전력 사용 이상 상태를 탐지하기 위하여, 편의점의 냉장 또는 냉동 장비의 온도가 비정상 범위로 가동되는 경우를 탐지하고, 세부적인 비정상 상황을 분류하는 방법을 제안한다. 정상 데이터에 비하여 비정상 데이터는 매우 적으며, 비정상 유형에 따라 데이터의 분량이 서로 달라 데이터 불균형 문제를 내포하고 있다. 제안하는 방법은 시계열 분류 알고리즘과 kNN을 결합한 계층적 구조로 되어 있으며, 데이터 불균형 문제를 해결하고 비교적 적은 데이터를 이용하여 분류 문제를 해결할 수 있다. 본 논문에서는 제안된 방법론을 검증하기 위하여 자체적으로 데이터 집합을 구성하여 실험을 수행하였다.
Abstract
In this paper, we propose a method to detect abnormal power usage conditions in domestic franchise convenience stores, by detecting cases where the temperature of the refrigeration or freezer equipment operates outside the normal range and classifying detailed abnormal situations. Compared to normal data, abnormal data is very small, and the amount of data varies depending on the type of abnormality, leading to a data imbalance issue. The proposed method employs a hierarchical structure that combines a time series classification algorithm with kNN, addressing the data imbalance problem and enabling classification using relatively small amounts of data. In this paper, we conducted an experiment by independently constructing our own dataset to validate the proposed methodology.
참고문헌 (12)
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