Top

Published in:

2019 | OriginalPaper | Chapter

Occupancy Inference Through Energy Consumption Data: A Smart Home Experiment

Authors : Adamantia Chouliara, Konstantinos Peppas, Apostolos C. Tsolakis, Thanasis Vafeiadis, Stelios Krinidis, Dimitrios Tzovaras

Published in: Computer Vision Systems

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This work is addressing the problem of occupancy detection in domestic environments, which is considered crucial in the aspect of increasing energy efficiency in buildings. In particular, in contrast with most previous researches, which obtained occupancy data through dedicated sensors, this study is investigating the possibility of using total consumption solely obtained from central smart meters installed in the examined buildings. In order to evaluate the feasibility of this simplified approach, the supervised machine learning classifier Random Forest was trained and tested on the experimental dataset. Repeated simulation tests show encouraging results achieving a high average performance with accuracy of 85%.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Can I Shift My Load? Optimizing the Selection of the Best Electrical Tariff for Tertiary Buildings

next chapter A Dynamic Convergence Algorithm for Thermal Comfort Modelling

Kleiminger, W., Beckel, C., Santini S.: Household occupancy monitoring using electricity meters, Japan, vol. 25, no. 3, pp. 273–291. ACM (2015). https://doi.org/10.1145/2750858.2807538

Pereira, D.F.: Occupancy Prediction from Electricity Consumption Data in Smart Homes. Instituto Superior Técnico, Universidade de Lisboa, Portugal (2017)

Chen, D., Barker, S., Subbaswamy, A., Irwin, D., Shenoy, P.: Non-Intrusive Occupancy Monitoring using Smart Meters. University of Massachusetts Amherst Vanderbilt University (2013)

Kleiminger, W.: Occupancy Sensing and Prediction for Automated Energy Savings. ETH Zurich (2015). https://doi.org/10.3929/ethz-a-010450096

Ardakanian, O., Bhattacharya, A., Cullere, D.: Non-intrusive occupancy monitoring for energy conservation in commercial buildings. Energy Build. 179, 311–323 (2018). https://doi.org/10.1016/j.enbuild.2018.09.033CrossRef

Kolter, J.Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. ACM, USA (2011)

Beckel, C., Kleiminger, W., Cicchetti, R.: The ECO data set and the performance of non-intrusive load monitoring algorithms. ACM, USA (2014)

Kleiminger, W., Beckel, C., Staake, T., Santini, S.: Occupancy detection from electricity consumption data. ACM, Italy (2013)

Molina-Markham, A., Shenoy, P., Fu, K., Cecchet, E., Irwin, D.: Private memoirs of a smart meter, pp. 61–66. ACM, Switzerland (2010)

10.

Nguyen, T.A., Aiello, M.: Energy intelligent buildings based on user activity: a survey. Energy Build. 56, 244–257 (2012). https://doi.org/10.1016/j.enbuild.2012.09.005CrossRef

11.

Agarwal, Y., Balaji, B., Gupta, R., Lyles, J., Wei, M., Weng, T.: Occupancy-driven energy management for smart building automation. In: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, pp. 1–6. ACM, Switzerland (2010)

12.

Akbar, A., Nati, M., Carrez, F.: Contextual occupancy detection for smart office by pattern recognition of electricity consumption data. In: IEEE International Conference on Communications, UK (2015)

13.

Lu, J., et al.: The smart thermostat: using occupancy sensors to save energy in homes, pp. 211–224. ACM, Switzerland (2010)

14.

Kleiminger, W., Mattern, F., Santini., S. : Predicting household occupancy for smart heating control: a comparative performance analysis of state-of-the-art approaches. Energy Build. 85, 493–505 (2014)CrossRef

15.

Erickson, V.L., Achleitner, S., Cerpa, A.E.: POEM: power-efficient occupancy-based energy management system. In: Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN 2013), pp. 203–216. ACM/IEEE, Germany (2013)

16.

Dodier, R.H., Henze, G.P., Tiller, D.K., Guo, X.: Building occupancy detection through sensor belief networks. Energy Build. 38(9), 1033–1043 (2006)CrossRef

17.

Padmanabh, K., et al.: iSense: a wireless sensor network based conference room management system. In: Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys 2009). ACM, USA (2009)

18.

Monachi, A.: GREEND: an energy consumption dataset of households in Italy and Austria. In: Proceedings of IEEE SmartGridComm, Italy (2014)

19.

Froehlich, J., Larson, E., Gupta, S., Cohn, G., Reynolds, M., Patel, S.: Disaggregated end-use energy sensing for the smart grid. IEEE Pervasive Comput. 10(1), 28–39 (2011)CrossRef

20.

Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)CrossRef

21.

Breiman, L.: Random forests –random features. Statistics Department, University of California, Berkeley (1999). ftp://ftp.stat.berkeley.edu/pub/users/breiman

22.

Vafeiadis, T., et al.: Machine learning based occupancy detection via the use of smart meters. In: International Conference on Energy Science and Electrical Engineering (2017). https://doi.org/10.1109/ISCSIC.2017.15

23.

Vafeiadis, A., et al.: Energy-based decision engine for household human activity recognition. In: IEEE International Conference on Pervasive Computing Pervasive Computing and Communications Workshops (PerCom Workshops), Greece (2018)

Title: Occupancy Inference Through Energy Consumption Data: A Smart Home Experiment
Authors: Adamantia Chouliara
Konstantinos Peppas
Apostolos C. Tsolakis
Thanasis Vafeiadis
Stelios Krinidis
Dimitrios Tzovaras
Publisher: Springer International Publishing
Book: Computer Vision Systems
Print ISBN: 978-3-030-34994-3

Electronic ISBN: 978-3-030-34995-0

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-34995-0_61

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner