Abstract
Occupancy detection is very interesting research problem which may help in understanding ambient dynamics of the environment, resource utilisation, energy conservation and consumption, electricity usages and patterns, security and privacy related aspects. In addition to this, achieving good accuracy for occupancy detection problem in the home and commercial buildings can help in cost reduction substantially. In this paper, we explain one experiment in which data for occupancy and ambient attributes have been collected. This paper develops machine learning-based intelligent occupancy detection model and compare the results with several machine learning techniques in a detailed manner.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Brooks, J., Goyal, S., Subramany, R., Lin, Y., Middelkoop, T., Arpan, L., Carloni, L., Barooah, P.: An experimental investigation of occupancy-based energy-efficient control of commercial building indoor climate. In: 53rd IEEE Conference on Decision and Control, pp. 5680–5685. IEEE (2014)
Brooks, J., Kumar, S., Goyal, S., Subramany, R., Barooah, P.: Energy-efficient control of under-actuated HVAC zones in commercial buildings. Energy Build. 93, 160–168 (2015)
Candanedo, L.M., Feldheim, V.: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Energy Build. 112, 28–39 (2016)
Dong, B.: Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings (2009)
Ebadat, A., Bottegal, G., Varagnolo, D., Wahlberg, B., Johansson, K.H.: Estimation of building occupancy levels through environmental signals deconvolution. In: Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings, BuildSys 2013, pp. 8:1–8:8. ACM, New York (2013)
Carreira-Perpin, M., Erickson, V.L., Cerpa, A.E.: Observe: occupancy-based system for efficient reduction of HVAC energy. In: 10th Information Processing in Sensor Networks (IPSN). IEEE (2011)
Erickson, V.L., Carreira-Perpiñán, M.Á., Cerpa, A.E.: Occupancy modeling and prediction for building energy management. ACM Trans. Sen. Netw. 10(3), 42:1–42:28 (2014)
Erickson, V.L., Lin, Y., Kamthe, A., Brahme, R., Surana, A., Cerpa, A.E., Sohn, M.D., Narayanan, S.: Energy efficient building environment control strategies using real-time occupancy measurements. In: Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BuildSys 2009, pp. 19–24. ACM, New York (2009)
Freedman, D.: Statistical Models: Theory and Practice. Cambridge University Press, Cambridge (2009)
Lichman, M., Bache, K.: UCI machine learning repository. University of California (2013)
Kumar, S., Rai, S., Singh, R., Pal, S.K.: ELM variants comparison on applications of time series data forecasting. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1404–1409, September 2016
Kumar, S., Pal, S.K.: Empirically developed integrated ICT framework for PDS in developing countries. In: 2013 Third World Congress on Information and Communication Technologies (WICT), vol. 3, pp. 235–240. IEEE (2013). ISBN: 978-1-4799-3230-6/13
Kumar, S., Pal, S.K., Singh, R.P.: Intelligent energy conservation: indoor temperature forecasting with extreme learning machine, pp. 977–988. Springer International Publishing, Cham (2016)
Kumar, S., Shobha, R., Pal, S.K.: A new sustainable prototype USP for education information system. In: International Conference on Futuristic Trends in Computational Analysis and Knowledge Management, vol. 1. IEEE (2015). ISBN: 978-1-4799-8432-9
Kumar, S., Singh, R., Pal, S.K.: A conceptual architectural design for intelligent health information system: Case study on India. In: Proceedings of 7th International Conference On Quality, Reliability, Infocom Technology and Business Operations (Trends And Future Directions). Springer (2017)
Martinez, A.M., Kak, A.C.: PCA versus lDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)
Richardson, I., Thomson, M., Infield, D.: A high-resolution domestic building occupancy model for energy demand simulations. Energy Build. 40(8), 1560–1566 (2008)
Lin, Y., Oggianu, S.M., Narayanan, S., Frewen, T.A., Meyn, S., Surana, A.: A sensor-utility-network method for estimation of occupancy in buildings. In: Proceedings of the 48th IEEE Conference on Decision and Control (CDC 2009) and 28th Chinese Control Conference CCC 2009, Shanghai, P.R. China, pp. 1494–1500. IEEE (2009)
Kumar, S., Pal, S.K.: An approach to ensure superior and sustainable software development performance. In: International Conference on Computing for Sustainable Global Development, pp. 115–117. IEEE Explorer Conference Id 32410 (2014). ISBN: 978-93-80544-10-6
Kumar, S., Pal, S.K.: ICT integrated social media framework for consumer awareness in society using ICT tools. In: IEEE/ACIS 13th International Conference on Computer and Information Science (ICIS), vol. 13, pp. 229–233. IEEE (2014)
Welling, M.: Fisher linear discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Kumar, S., Rai, S., Singh, R., Pal, S.K. (2018). Machine Learning-Based Method and Its Performance Analysis for Occupancy Detection in Indoor Environment. In: Thampi, S., Krishnan, S., Corchado Rodriguez, J., Das, S., Wozniak, M., Al-Jumeily, D. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2017. Advances in Intelligent Systems and Computing, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-319-67934-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-67934-1_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67933-4
Online ISBN: 978-3-319-67934-1
eBook Packages: EngineeringEngineering (R0)