Journal of Korean Institute of Architectural Sustainable Environment and Building Systems (한국건축친환경설비학회 논문집)

Korean Institute of Architectural Sustainable Environment and Building Systems (한국건축친환경설비학회)
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Anomaly Detection and Diagnostics (ADD) Based on Support Vector Data Description (SVDD) for Energy Consumption in Commercial Building

SVDD를 활용한 상업용 건물에너지 소비패턴의 이상현상 감지

  • Chae, Young-Tae (Department of Architectural Engineering, School of Engineering, Cheongju University)
  • 채영태 (청주대학교 공과대학 휴먼환경디자인학부 건축공학전공)
  • Received : 2018.10.31
  • Accepted : 2018.12.07
  • Published : 2018.12.31
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Abstract

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

Keywords

Acknowledgement

Supported by : 청주대학교

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