Open Access
Issue
Sust. Build.
Volume 2, 2017
Article Number 7
Number of page(s) 10
Section Smart Monitors and Intelligent Building Controls
DOI https://doi.org/10.1051/sbuild/2017005
Published online 28 July 2017
  1. A. Thewes, S. Maas, F. Scholzen, D. Waldmann, A. Zürbes, Field study on the energy consumption of school buildings in Luxembourg, Energy Build. 68, 460 (2014) [CrossRef] [Google Scholar]
  2. L. Pérez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energy Build. 40, 394 (2008) [CrossRef] [Google Scholar]
  3. N. Nassif, A robust CO2-based demand-controlled ventilation control strategy for multi-zone HVAC systems, Energy Build. 45, 72 (2012) [CrossRef] [Google Scholar]
  4. W.J. Fisk, A.T. De Almeida, Sensor-based demand-controlled ventilation: a review, Energy Build. 29, 35 (1998) [CrossRef] [Google Scholar]
  5. N. Nassif, Supply air CO2-based demand-controlled ventilation for multi-zone HVAC systems, ASHRAE Trans. 118, 300 (2012) [Google Scholar]
  6. A. Vaccari, S. Samouhos, MIT enernet: correlating WiFi activity to human occupancy, in Proceedings of Healthy Buildings (2009) [Google Scholar]
  7. C. Martani, D. Lee, P. Robinson, R. Britter, C. Ratti, ENERNET: studying the dynamic relationship between building occupancy and energy consumption, Energy Build. 47, 584 (2012) [CrossRef] [Google Scholar]
  8. P. Lin, Q. Li, Q. Fan, X. Gao, S. Hu, A real-time location-based services system using WiFi fingerprinting algorithm for safety risk assessment of workers in tunnels, Math. Probl. Eng. 2014, 10 (2014) [Google Scholar]
  9. B.T. Rosenblum, Collecting Occupant Presence Data for Use in Energy Management of Commercial Buildings, University of California, Berkeley, 2012 [Google Scholar]
  10. T.A. Nguyen, M. Aiello, Energy intelligent buildings based on user activity: a survey, Energy Build. 56, 244 (2013) [CrossRef] [Google Scholar]
  11. T. Teixeira, G. Dublon, A. Savvides, A survey of human-sensing: methods for detecting presence, count, location, track, and identity, ACM Comput. Surv. 5, 1 (2010) [Google Scholar]
  12. T. Yokoishi, J. Mitsugi, O. Nakamura, J. Murai, Room occupancy determination with particle filtering of networked pyroelectric infrared (PIR) sensor data, in Proc. IEEE Sensors (2012), pp. 3–6 [Google Scholar]
  13. X. Guo, D. Tiller, G. Henze, C. Waters, The performance of occupancy-based lighting control systems: a review, Light. Res. Technol. 42, 415 (2010) [Google Scholar]
  14. B. Roisin, M. Bodart, A. Deneyer, P. D'Herdt, Lighting energy savings in offices using different control systems and their real consumption, Energy Build. 40, 514 (2008) [Google Scholar]
  15. T. Labeodan, K. Aduda, W. Zeiler, F. Hoving, Experimental evaluation of the performance of chair sensors in an office space for occupancy detection and occupancy-driven control, Energy Build. 111, 195 (2016) [CrossRef] [Google Scholar]
  16. T.R. Nielsen, C. Drivsholm, Energy efficient demand controlled ventilation in single family houses, Energy Build. 42, 1995 (2010) [CrossRef] [Google Scholar]
  17. S. Kar, P.K. Varshney, Accurate estimation of indoor occupancy using gas sensors, in ISSNIP 2009 – Proc. 2009 5th Int. Conf. Intell. Sensors, Sens. Networks Inf. Process. (2009), pp. 355–360 [Google Scholar]
  18. Z. Sun, S. Wang, Z. Ma, In-situ implementation and validation of a CO2-based adaptive demand-controlled ventilation strategy in a multi-zone office building, Build. Environ. 46, 124 (2011) [CrossRef] [Google Scholar]
  19. American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. ANSI/ASHRAE Standard 62.1-2010. Ventilation for Acceptable Indoor Air Quality (ASHRAE, Atlanta, GA, 2010) [Google Scholar]
  20. D. Calì, P. Matthes, K. Huchtemann, R. Streblow, D. Müller, CO2 based occupancy detection algorithm: experimental analysis and validation for office and residential buildings, Build. Environ. 86, 39 (2015) [Google Scholar]
  21. W.J. Fisk, D.P. Sullivan, D. Faulkner, E. Eliseeva, CO2 Monitoring for Demand Controlled Ventilation in Commercial Buildings (Lawrence Berkeley National Laboratory, Berkeley, CA, USA, 2010), pp. 1–52 [Google Scholar]
  22. S. Shrestha, G.M. Maxwell, Product Testing Report Supplement: Wall Amounted Carbon Dioxide (CO2) Transmitters, Test, No. March, pp. 1–17, 2010 [Google Scholar]
  23. S. Emmerich, A. Persily, State-of-the-Art Review of CO2 Demand Controlled Ventilation Technology and Application, Natl. Inst. Stand. Technol., No. NISTIR 6729, 2001 [Google Scholar]
  24. E. Azar, C. Menassa, Agent-based modeling of occupants and their impact on energy use in commercial buildings, J. Comput. Civ. Eng. 26, 506 (2011) [CrossRef] [Google Scholar]
  25. C.A. Webber, J.A. Roberson, R.E. Brown, C.T. Payne, B. Nordman, J.G. Koomey, Field Surveys of Office Equipment Operating Patterns, Environ. Prot., No. LBNL-46930, 2001 [CrossRef] [Google Scholar]
  26. M. Sanchez, C. Webber, R. Brown, J. Busch, M. Pinckard, Space heaters, computers, cell phone chargers: how plugged in are commercial buildings? in ACEEE Summer Study Energy Effic. Build. (2006), pp. 304–315 [Google Scholar]
  27. M. Milenkovic, O. Amft, Recognizing energy-related activities using sensors commonly installed in office buildings, Procedia Comput. Sci. 19, 669 (2013) [CrossRef] [Google Scholar]
  28. K. Ng, Use of WiFi Sensor Network in Measuring Occupancy and People Circulation in Buildings, Ryerson University, 2016 [Google Scholar]
  29. A. Motamedi, M.M. Soltani, A. Hammad, Localization of RFID-equipped assets during the operation phase of facilities, Adv. Eng. Inform. 27, 566 (2013) [Google Scholar]
  30. N. Li, G. Calis, B. Becerik-Gerber, Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations, Autom. Constr. 24, 89 (2012) [CrossRef] [Google Scholar]
  31. K. Weekly, H. Zou, L. Xie, Q.S. Jia, A.M. Bayen, Indoor occupant positioning system using active rfid deployment and particle filters, in Proc. – IEEE Int. Conf. Distrib. Comput. Sens. Syst. DCOSS 2014 (2014), pp. 35–42 [CrossRef] [Google Scholar]
  32. H.M. Khoury, V.R. Kamat, Evaluation of position tracking technologies for user localization in indoor construction environments, Autom. Constr. 18, 444 (2009) [Google Scholar]
  33. CISCO, Wi-Fi Location-Based Services 4.1 Design Guide, No. 6387 (CISCO Systems, CA, USA, 2008) [Google Scholar]
  34. V. Osa, J. Matamales, J.F. Monserrat, J. López, Localization in wireless networks: the potential of triangulation techniques, Wirel. Pers. Commun. 68, 1525 (2013) [CrossRef] [Google Scholar]
  35. A. LaMarca, E. De Lara, Synth. Lect. Mob. Pervasive Comput. 3, 1 (2008) [CrossRef] [Google Scholar]
  36. A. Sevtsuk, S. Huang, F. Calabrese, C. Ratti, Mapping the MIT campus in real time using WiFi, in Handb. Res. Urban Informatics Pract. Promise Real-Time City (IGI Global, USA, 2009) [Google Scholar]
  37. C.M. El Amine, O. Mohamed, B. Boualam, The implementation of indoor localization based on an experimental study of RSSI using a wireless sensor network, Peer-to-Peer Netw. Appl. 9, 795 (2016) [CrossRef] [Google Scholar]
  38. W. Mardini, Y. Khamayseh, A.A. Almodawar, E. Elmallah, Adaptive RSSI-based localization scheme for wireless sensor networks, Peer-to-Peer Netw. Appl. 9, 991 (2016) [CrossRef] [Google Scholar]
  39. K. Christensen, R. Melfi, B. Nordman, B. Rosenblum, R. Viera, K. Christensen, R. Melfi, Using existing network infrastructure to estimate building occupancy and control plugged-in devices in user workspaces, Int. J. Commun. Netw. Distrib. Syst. 12, 4 (2014) [CrossRef] [Google Scholar]
  40. Z. Bakó-Biró, D.J. Clements-Croome, N. Kochhar, H.B. Awbi, M.J. Williams, Ventilation rates in schools and pupils' performance, Build. Environ. 48, 215 (2012) [Google Scholar]
  41. F. Van Dijken, J.E.M.H. Van Bronswijk, J. Sundell, Indoor environment and pupils' health in primary schools, Build. Res. Inf. 34, 437 (2006) [CrossRef] [Google Scholar]
  42. V. De Giuli, O. Da Pos, M. De Carli, Indoor environmental quality and pupil perception in Italian primary schools, Build. Environ. 56, 335 (2012) [Google Scholar]
  43. V. De Giuli, R. Zecchin, L. Salmaso, L. Corain, M. De Carli, Measured and perceived indoor environmental quality: Padua Hospital case study, Build. Environ. 59, 211 (2013) [CrossRef] [Google Scholar]
  44. Z.J. Yu, F. Haghighat, B.C.M. Fung, E. Morofsky, H. Yoshino, A methodology for identifying and improving occupant behavior in residential buildings, Energy 36, 6596 (2011) [CrossRef] [Google Scholar]
  45. C.M. Clevenger, J.R. Haymaker, M. Jalili, Demonstrating the impact of the occupant on building performance, J. Comput. Civ. Eng. 28, 99 (2014) [CrossRef] [Google Scholar]

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