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A Study on Occupancy Estimation Method of a Private Room Using IoT Sensor Data Based Decision Tree Algorithm

IoT 센서 데이터를 이용한 단위실의 재실추정을 위한 Decision Tree 알고리즘 성능분석

Seok-Ho Kim1
,
Dong-Hyun Seo1*
김 석호1
,
서 동현1*
1Department of Architectural Engineering, Chungbuk National University
1충북대학교 건축공학과
*교신저자. *Corresponding Author. 
April 2017. pp. 23-33
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Abstract

Accurate prediction of stochastic behavior of occupants is a well known problem for improving prediction performance of building energy use. Many researchers have been tried various sensors that have information on the status of occupant such as CO2 sensor, infrared motion detector, RFID etc. to predict occupants, while others have been developed some algorithm to find occupancy probability with those sensors or some indirect monitoring data such as energy consumption in spaces. In this research, various sensor data and energy consumption data are utilized for decision tree algorithms (C4.5 & CART) for estimation of sub-hourly occupancy status. Although the experiment is limited by space (private room) and period (cooling season), the prediction result shows good agreement of above 95% accuracy when energy consumption data are used instead of measured CO2 value. This result indicates potential of IoT data for awareness of indoor environmental status.

Keywords
Occupancy information
Occupancy estimation
IoT data
Decision tree
Data mining

키워드
재실정보
재실추정
사물 인터넷데이터
의사결정나무
데이터 마이닝
References
1
User’s Manual for ANSI/ASHRAE/IESNA Standard 90.1-2004, Appendix G. Building Performance Rating Method, pp. G39-G47.
2
Kim, H. I. and Yoon, G. Y., Effect of Occupancy and Lighting System Use Patterns on Building Energy Consumption, Journal of the Architectural Institute of Korea, Vol. 27, No. 5, pp. 229-236, 2011.
3
Park, S. L., Kim, J. H., Kim, D. W., and Park, C. S., Control Strategies of Cooling Systems in a Smart Home, Journal of Korean Institute of Architectural Sustainable Environment and Building Systems, pp. 257-260, 2011.
4
Simona D’Oca, Tianzhen Hong, Occupancy schedules learning process through a data mining framework, Energy and Buildings, Volume 88, pp.395-408, 2015.
5
Xin Liang, Liang X., Hong T., and Shen G. Q., Occupancy data analytics and prediction: A case study, Building and Environment, Volume 102, pp.179-192, 2016.
6
Han, H. T., Han, C. H., and Baek, C. I., Occupancy Estimation Based on Carbon Dioxide Concentration Using Dynamic Neural Network, Korean Journal of Air-Conditioning and Refrigeration Engineering, pp. 269-272, 2012.
7
Bae, W. B., Kim, Y. J., Mun, S. H., and Huh, J. H. , Prediction of Occupants based on Existing Wi-Fi Infrastructure, Journal of the Architectural Institute of Korea, Vol. 31, No. 11, pp. 211-219, 2015.
8
DOE, M&V Guidelines: Measurement and Verification for Performance-Based Contracts Version 4.0, Federal Energy Management Program, 2015.
9
Ryu, S. Ho. and Moon, H. J., Development of an Occupancy Prediction Model Using Indoor Environmental Data Based on Machine Learning Techniques, Building and Environment, Vol. 107, pp.1-9, 2016.
10
ISO, ISO 8601 Data Elements and Interchange Formats–Information Interchange – Representation of dates and times, 2004.
11
Jiawei Han, Micheline Kamber, Jian Pei, Data Mining Concept and Techniques, The Third Edition, Morgan Kaufmann, pp. 330-350, 2012.
12
Kevin P. Murphy, Machine Learning A probabilistic Perspective, The MIT Press, pp.544-551, 2012.
13
Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
14
Breiman, Leo, Friedman, J. H., Olshen, R. A., Stone, C. J. (1984). Classification and Regression Trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. ISBN 978-0-412-04841-8.
15
Stone, R. J., Improved Statistical Procedure for the Evaluation of Solar Radiation Estimation Models, Solar Energy, Vol. 51, No. 4, pp. 289-291, 1993.
16
Zhang, R., Lam, K. P., Chiou, Y. S., and Dong, B., Information-theoretic Environment Features Selection for Occupancy Detection in Open Office Spaces, Building Simulation, 5, pp. 179-188, 2012.
Information
  • Publisher :Korean Solar Energy Society
  • Publisher(Ko) :한국태양에너지학회
  • Journal Title :Journal of the Korean Solar Energy Society
  • Journal Title(Ko) :한국태양에너지학회 논문집
  • Volume : 37
  • No :2
  • Pages :23-33
  • Received Date : 2017-02-06
  • Revised Date : 2017-04-21
  • Accepted Date : 2017-04-25
  • DOI :https://doi.org/10.7836/kses.2017.37.2.023
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