Machine Learning-Based Method and Its Performance Analysis for Occupancy Detection in Indoor Environment

1. Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)

   MathSciNet  Google Scholar 

2. 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)

   Google Scholar 

3. 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)

   Article  Google Scholar 

4. 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)

   Article  Google Scholar 

5. Dong, B.: Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings (2009)

   Google Scholar 

6. 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)

   Google Scholar 

7. 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)

   Google Scholar 

8. 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)

   Article  Google Scholar 

9. 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)

   Google Scholar 

10. Freedman, D.: Statistical Models: Theory and Practice. Cambridge University Press, Cambridge (2009)

    Book  MATH  Google Scholar 

11. Lichman, M., Bache, K.: UCI machine learning repository. University of California (2013)

    Google Scholar 

12. 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

    Google Scholar 

13. 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

    Google Scholar 

14. 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)

    Google Scholar 

15. 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

    Google Scholar 

16. 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)

    Google Scholar 

17. Martinez, A.M., Kak, A.C.: PCA versus lDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)

    Article  Google Scholar 

18. Richardson, I., Thomson, M., Infield, D.: A high-resolution domestic building occupancy model for energy demand simulations. Energy Build. 40(8), 1560–1566 (2008)

    Article  Google Scholar 

19. 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)

    Google Scholar 

20. 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

    Google Scholar 

21. 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)

    Google Scholar 

22. Welling, M.: Fisher linear discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell

    Google Scholar 

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