IoT 센서 데이터를 이용한 단위실의 재실추정을 위한 Decision Tree 알고리즘 성능분석
A Study on Occupancy Estimation Method of a Private Room Using IoT Sensor Data Based Decision Tree Algorithm
1
조회
0
다운로드
3
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (16)
- "User’s Manual for ANSI", ASHRAE, pp.2004, G39~G47
- 김효인, "거주자의 인공조명 사용행태와 재실스케줄이 건물의 에너지 소비에 미치는 영향", 대한건축학회논문집 계획계, 27(05), pp.229~236 (2011), 대한건축학회
- Park, S. L., "Control Strategies of Cooling Systems in a Smart Home", Journal of Korean Institute of Architectural Sustainable Environment and Building Systems, pp.257~260 (2011)
- Simona D’Oca, "Occupancy schedules learning process through a data mining framework", Energy and Buildings, 88, pp.395~408 (2015)
- Xin Liang, "Occupancy data analytics and prediction: A case study", Building and Environment, 102, pp.179~192 (2016)
- Han, H. T., "Occupancy Estimation Based on Carbon Dioxide Concentration Using Dynamic Neural Network", Korean Journal of Air-Conditioning and Refrigeration Engineering, pp.269~272 (2012)
- 배우빈, "기존 Wi-Fi 인프라 기반의 재실 인원 예측", 대한건축학회논문집 계획계, 31(4), pp.221~228 (2015), 대한건축학회[DOI]
- DOE, "M&V Guidelines: Measurement and Verification for Performance-Based Contracts Version 4.0", Federal Energy Management Program, ~ (2015)
- Ryu, S. Ho, "Development of an Occupancy Prediction Model Using Indoor Environmental Data Based on Machine Learning Techniques", Building and Environment, 107, pp.1~9 (2016)
- ISO, "ISO 8601 Data Elements and Interchange Formats–Information Interchange – Representation of dates and times", ~ (2004)
- Jiawei Han, "Data Mining Concept and Techniques", pp.330~350 (2012), Morgan Kaufmann
- Kevin P. Murphy, "Machine Learning A probabilistic Perspective", pp.544~551 (2012), The MIT Press
- Quinlan, J. R., "C4.5: Programs for Machine Learning" (1993), Morgan Kaufmann Publishers
- Breiman, Leo, "Classification and Regression Trees", pp.1984, Wadsworth & Brooks
- Stone, R. J., "Improved Statistical Procedure for the Evaluation of Solar Radiation Estimation Models", Solar Energy, 51(4), pp.289~291 (1993)
- Zhang, R., "Information-theoretic Environment Features Selection for Occupancy Detection in Open Office Spaces", Building Simulation, 5, pp.179~188 (2012)