Browse > Article

A Study on the Algorithm for the Occupancy Inference in Residential Buildings using Indoor CO2 Concentration and PIR Signals  

Rhee, Kyu-Nam (부경대학교 건축공학과)
Jung, Gun-Joo (부경대학교 건축공학과)
Publication Information
Journal of the Regional Association of Architectural Institute of Korea / v.20, no.6, 2018 , pp. 113-119 More about this Journal
Abstract
Occupancy-based heating control is effective in reducing heating energy by preventing unnecessary heating during unoccupied period. Various technologies on detecting human occupancy have been developed using complicated machine learning algorithm and stochastic methodologies. This study aims at deriving low-cost and simple algorithm of occupancy inference that can be implemented to residential buildings. The core concept of the algorithm is to combine the occupancy probabilities based on indoor CO2 concentration and PIR(passive infrared) signals. The probability was estimated by applying different levels of decrement ratio depending on CO2 concentration change rate and aggregated PIR signals. The developed algorithm was validated by comparing the inference results with the occupancy schedule in a real residential building. The results showed that the inference algorithm can achieve the accuracy of 75~99%, which would be successfully implemented to the control of residential heating systems.
Keywords
Occupancy Inference; $CO_2$ Concentration; PIR; Occupancy Probability Function; Decrement Ratio; Residential Building;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Bae, W., Ko, J., Mun, S., Huh, J. (2015). Occupants' Location-Driven Control of Radiant Floor Heating Using Fingerprinting Method in Residential Buildings. Journal of Architectural Institute of Korea (Design and Planning). 31(11), pp.211-219.
2 Lee, Y., Rhee, K., Jin, W., Yeo, M., Kim, K. (2005). A Study on the Optimal Start Control of Radiant Floor Heating System in Apartment Buildings. In proceedings: Summer conference of Society of Air-conditioning, Refrigeration Engineers of Korea, pp.3-8.
3 Kim, S. (2010). Understanding Kalman Filter using MATLAB (in Korean). A-Jin, Seoul.
4 Kim, J., Rhee, K., Jung, K. (2017). A study on occupancy detection in residential space using $CO_2$ concentration and PIR sensing data. In proceedings: Annual conference of Architectural Institute of Korea, 37(2), pp.548-549.
5 Adafruit Industries, https://cdn-learn.adafruit.com/downloads/pdf/pir-passive-infrared-proximity-motion-sensor.pdf, Retrieved October 15, 2018
6 Duarte, R., Gomes, M. G., Rodrigues, A. M. (2018). Estimating ventilation rates in a window-aired room using Kalman filtering and considering uncertain measurements of occupancy and $CO_2$ concentration. Building and Environment, 143, pp.691-700.   DOI
7 Amayri, M., Arora, A., Ploix, S., Bandhyopadyay, S., Ngo, Q. D., Badarla, V. R. (2016). Estimating occupancy in heterogeneous sensor environment. Energy and Buildings, 129, pp.46-58.   DOI
8 Candanedo, L. M., & Feldheim, V. (2016). Accurate occupancy detection of an office room from light, temperature, humidity and $CO_2$ measurements using statistical learning models. Energy and Buildings, 112, pp.28-39.   DOI
9 Calì, D., Matthes, P., Huchtemann, K., Streblow, R., Müller, D. (2015). $CO_2$ based occupancy detection algorithm: Experimental analysis and validation for office and residential buildings. Building and Environment, 86, pp.39-49.   DOI
10 Erickson, V. L., Carreira-Perpinan, M. A., Cerpa, A. E. (2014). Occupancy modeling and prediction for building energy management. ACM Transactions on Sensor Networks (TOSN), 10(3), 42.
11 Hailemariam, E., Goldstein, R., Attar, R., Khan, A. (2011). Real-time occupancy detection using decision trees with multiple sensor types. In Proceedings of the 2011 Symposium on Simulation for Architecture and Urban Design, pp.141-148.
12 T&D Corp., https://www.tandd.com/product/tr7ui_series.html, Retrieved October 15, 2018.
13 Labeodan, T., Zeiler, W., Boxem, G., Zhao, Y. (2015). Occupancy measurement in commercial office buildings for demand-driven control applications-A survey and detection system evaluation. Energy and Buildings, 93, pp.303-314.   DOI
14 Mahyuddin, N., Awbi, H. (2010). The spatial distribution of carbon dioxide in an environmental test chamber. Building and Environment, 45(9), pp.1993-2001.   DOI
15 Pedersen, T. H., Nielsen, K. U., Petersen, S. (2017). Method for room occupancy detection based on trajectory of indoor climate sensor data. Building and Environment, 115, pp.147-156.   DOI
16 Peng, Y., Rysanek, A., Nagy, Z., Schluter, A. (2018). Using machine learning techniques for occupancyprediction- based cooling control in office buildings. Applied Energy, 211, pp.1343-1358.   DOI
17 Scott, J., Bernheim Brush, A. J., Krumm, J., Meyers, B., Hazas, M., Hodges, S., Villar, N. (2011). PreHeat: controlling home heating using occupancy prediction. In Proceedings of the 13th international conference on Ubiquitous computing. pp.281-290.
18 Zhang, R., Lam, K. P., Chiou, Y. S., Dong, B. (2012). Information-theoretic environment features selection for occupancy detection in open office spaces. Building Simulation, 5(2), pp.179-188.   DOI
19 Peffer, T., Pritoni, M., Meier, A., Aragon, C., Perry, D. (2011). How people use thermostats in homes: A review. Building and Environment, 46(12), pp.2529-2541.   DOI