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Wireless, collaborative virtual sensors for thermal comfort

Published: 02 November 2010 Publication History

Abstract

Monitoring building performance data for energy-efficiency is an increasing market in which especially wireless sensor networks benefit from their easy installation in existing buildings. Nonetheless, large scale installations are still hampered by sheer installation costs. This paper introduces a virtual sensor approach that utilizes available information in a collaborative network to compute new sensor data. An example is provided in which the approach estimates the thermal comfort with only low-cost temperature sensors in most rooms.

References

[1]
Special issue on thermal comfort standards. Energy and Buildings, 34(6):529--685, 2002.
[2]
Enocean equipment profiles, July 2009.
[3]
A. Ahmed, J. Ploennigs, Y. Gao, and K. Menzel. Analyse building performance data for energy-efficient building operation. In CIB W78 - 26th Int. Conf. on Managing IT in Construction, pages 211--220, Istanbul, Turkey, Oct. 2009.
[4]
G. Augenbroe and C.-S. Park. Quantification methods of technical building performance. Building Research and Information, 33(2):159--172, 2005.
[5]
D. Claridge, J. Haberl, M. Liu, J. Houcek, and A. Athar. Can you achieve 150 percent predicted retrofit savings: Is it time for recommissioning? In ACEEE 1994 Summer Study on Energy Efficiency in Buildings, pages 73--88, Washington D.C., US, 1994.
[6]
A. Dementjev, B. Hensel, H. Kubin, H.-D. Ribbecke, and K. Kabitzsch. Control of a vacuum coating process with long dead-time and an integrator: a case study. In TDS - 9th IFAC Workshop on Time Delay Systems, Prague, Czech Republic, June 2010.
[7]
H. Demuth, M. Beale, and M. Hagan. Neural Network Toolbox 6: Users Guide. The MathWorks, March 2010.
[8]
F. Felgner. Design of Virtual Airflow Sensors for Thermal Comfort Control. Shaker Verlag, 2009.
[9]
M. T. Hagan and M. B. Benhaj. Training feedforward networks with the marquardt algorithm. IEEE Trans. Neural Netw., 5(6):989--993, Nov. 1994.
[10]
ISO 7730 - ergonomics of the thermal environment - analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria, 2005.
[11]
L. Jagemar and D. Olsson. The EPBD and continuous commissioning. Technical report, CIT Energy Management AB, 2007.
[12]
A. P. Jayasumana, Q. Han, and T. H. Illangasekare. Virtual sensor networks - a resource efficient approach for concurrent applications. In 3rd Int. Conf. on Information Technology: New Generations, pages 111--115, Los Alamitos, CA, USA, 2007. IEEE Comp. Soc. Press.
[13]
W. Kastner, G. Neugschwandtner, S. Soucek, and H. M. Newman. Communication systems for building automation and control. Proc. IEEE, 93(6):1178--1203, 2005.
[14]
G. Kats, L. Alevantis, A. Berman, E. Mills, and J. Perlman. The costs and financial benefits of green buildings. Technical report, Californias Sustainable Building Task Force, USA, 2003.
[15]
S. Kirkpatrick, C. Gelatt Jr, and M. Vecchi. Optimization by simulated annealing. Science, 220(4598):671, 1983.
[16]
A. Kulakov and D. Davcev. Distributed data processing in wireless sensor networks based on artificial neural-networks algorithms. In ISCC - 10th IEEE Symposium on Computers and Communications, pages 353--358, Washington, DC, USA, 2005. IEEE Comp. Soc. Press.
[17]
P. Kunze and J. Grunewald. Suitable algorithms for practical assessment of indoor climates in hospital wards. In Central European Symposium on Building Physics, Cracow, Poland, 13.--15. Sept. 2010.
[18]
M. Levine, D. Ürge Vorsatz, K. Blok, L. Geng, D. Harvey, S. Lang, G. Levermore, A. M. Mehlwana, S. Mirasgedis, A. Novikova, J. Rilling, and H. Yoshino. Climate Change 2007: Mitigation. 4th Assessment Report of the Intergovernmental Panel on Climate Change, chapter Residential and commercial buildings, pages 387--446. Cambridge University Press, 2007.
[19]
K. Menzel, D. Pesch, B. O'Flynn, M. Keane, and C. O'Mathuna. Towards a wireless sensor platform for energy efficient building operation. Tsinghua Science & Technology, 13(S1):381--386, Oct. 2008.
[20]
J. Park, M. Moon, S. Hwang, and K. Yeom. CASS: A context-aware simulation system for smart home. In SERA - 5th ACIS Int. Conf. on Software Engineering Research, Management & Applications.
[21]
J. U. Pfafferott, S. Herkel, D. E. Kalz, and A. Zeuschner. Comparison of low-energy office buildings in summer using different thermal comfort criteria. Energy and Buildings, 39(7):750--757, 2007.
[22]
J. Ploennigs, A. Ahmed, P. Stack, and K. Menzel. Model-based virtual sensors for room heat metering and energy consumption analysis in buildings with renewable energy sources. In ICCCBE - 13th Int. Conf. on Comp. in Civil and Building Eng., Nottingham, UK, June 2010.
[23]
J. Ploennigs, U. Ryssel, and K. Kabitzsch. Performance analysis of the EnOcean wireless sensor network protocol. In ETFA - 15th IEEE Int. Conf. on Emerging Technol. and Factory Autom., Bilbao, Spain, Sept. 2010.
[24]
J. Ploennigs, V. Vasyutynskyy, and K. Kabitzsch. Comparison of energy-efficient sampling methods for WSNs in building automation scenarios. IEEE Trans. Ind. Informat., 6(3), Aug. 2010.
[25]
A. Schumann, M. Burillo, and N. Wilson. Predicting the desired thermal comfort conditions for shared offices. In ICCCBE - 13th Int. Conf. on Comp. in Civil and Building Eng., Nottingham, UK, June 2010.
[26]
T. Ueno, F. Sano, O. Saeki, and K. Tsuji. Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data. Applied Energy, 83:166--183, 2006.
[27]
M. Willis, C. D. Massimo, G. Montague, M. Tham, and A. Morris. Artificial neural networks in process engineering. IEE Proc. Control Theory and Appl., 138(3):256--266, 1991.

Cited By

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  • (2019)Occupancy detection systems for indoor environments: A survey of approaches and methodsIndoor and Built Environment10.1177/1420326X1987562129:8(1053-1069)Online publication date: 16-Sep-2019
  • (2016)Semantic models for physical processes in CPS at the example of occupant thermal comfort2016 IEEE 25th International Symposium on Industrial Electronics (ISIE)10.1109/ISIE.2016.7745039(1061-1066)Online publication date: Jun-2016
  • (2014)Occupancy Modeling and Prediction for Building Energy ManagementACM Transactions on Sensor Networks10.1145/259477110:3(1-28)Online publication date: 6-May-2014
  • Show More Cited By

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Published In

cover image ACM Conferences
BuildSys '10: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
November 2010
93 pages
ISBN:9781450304580
DOI:10.1145/1878431
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 02 November 2010

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Author Tags

  1. artificial neural networks
  2. collaborative sensor networks
  3. thermal comfort
  4. virtual sensors

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Overall Acceptance Rate 148 of 500 submissions, 30%

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Cited By

View all
  • (2019)Occupancy detection systems for indoor environments: A survey of approaches and methodsIndoor and Built Environment10.1177/1420326X1987562129:8(1053-1069)Online publication date: 16-Sep-2019
  • (2016)Semantic models for physical processes in CPS at the example of occupant thermal comfort2016 IEEE 25th International Symposium on Industrial Electronics (ISIE)10.1109/ISIE.2016.7745039(1061-1066)Online publication date: Jun-2016
  • (2014)Occupancy Modeling and Prediction for Building Energy ManagementACM Transactions on Sensor Networks10.1145/259477110:3(1-28)Online publication date: 6-May-2014
  • (2013)Enabling advanced environmental conditioning with a building application stack2013 International Green Computing Conference Proceedings10.1109/IGCC.2013.6604519(1-10)Online publication date: Jun-2013
  • (2013)Economic and technical influences on feedback controller designIECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society10.1109/IECON.2013.6699691(3498-3504)Online publication date: Nov-2013
  • (2013)Experiences with Sensors for Energy Efficiency in Commercial BuildingsReal-World Wireless Sensor Networks10.1007/978-3-319-03071-5_23(231-243)Online publication date: 20-Dec-2013
  • (2012)Sensors, models and platform for ambient controlIECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society10.1109/IECON.2012.6389581(4853-4859)Online publication date: Oct-2012
  • (2011)Virtual sensors for estimation of energy consumption and thermal comfort in buildings with underfloor heatingAdvanced Engineering Informatics10.1016/j.aei.2011.07.00425:4(688-698)Online publication date: 1-Oct-2011

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