JouleSense: A Simulation based Platform for Proactive Feedback on Building Occupants’ Energy Use

Georgios Lilis, Shubham Bansal, Maher Kayal

2016

Abstract

A significant amount of energy in the buildings can be saved by inducing efficient occupant behavior. The occupant’s awareness tools that have been shown to be effective in achieving energy efficiency gains depend on various computational and estimation algorithms. This paper proposes an energy feedback scheme that relies on a model based, building thermal simulation in order to identify the areas for efficiency improvement. By leveraging the specific mathematical formulation of those models and a dedicated open-source solver, improved computational speed, reduced cost and enhanced interoperability is obtained. This combined with the integration into a building management system (BMS), permits real-time sensing and feedback. Unlike similar studies, this work’s outcome allows the creation of the energy awareness tools that rely solely on validated thermal model simulation, thus increasing their accuracy and potential in the future smart buildings.

References

  1. Birt, B. J., Newsham, G. R., Beausoleil-Morrison, I., Armstrong, M. M., Saldanha, N., and Rowlands, I. H. (2012). Disaggregating categories of electrical energy end-use from whole-house hourly data. Energy and Buildings, 50:93-102.
  2. Chen, C. and Cook, D. J. (2012). Behavior-based home energy prediction. Proceedings - 8th International Conference on Intelligent Environments, IE 2012, pages 57-63.
  3. Darby, S. (2010). Smart metering: what potential for householder engagement? Building Research & Information, 38(5):442-457.
  4. Fischer, C. (2008). Feedback on household electricity consumption: a tool for saving energy? Energy efficiency, 1(1):79-104.
  5. Fraisse, G., Viardot, C., Lafabrie, O., and Achard, G. (2002). Development of a simplified and accurate building model based on electrical analogy. Energy and Buildings, 34:1017-1031.
  6. Gupta, M., Intille, S. S., and Larson, K. (2009). Adding GPS-control to traditional thermostats: An exploration of potential energy savings and design challenges. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5538 LNCS:95- 114.
  7. Gwerder, M. and Gyalistras, D. (2013). Final Report : Use of Weather And Occupancy Forecasts For Optimal Building Climate Control Part II : Demonstration ( OptiControl-II ). Technical Report September, ETH Zürich.
  8. Gyalistras, D. and Gwerder, M. (2009). Use of weather and occupancy forecasts for optimal building climate control (OptiControl): Two years progress report. Technical Report September, ETH, Zurich.
  9. Lehmann, B., Gyalistras, D., Gwerder, M., Wirth, K., and Carl, S. (2013). Intermediate complexity model for Model Predictive Control of Integrated Room Automation. Energy and Buildings, 58:250-262.
  10. Lilis, G., Conus, G., and Kayal, M. (2015). A Distributed, Event-driven Building Management Platform on Web Technologies. In 1st International Conference on Event-Based Control, Communication, and Signal Processing, Krakow.
  11. Linear Technology (2015). LTspice.
  12. Maasoumy, M., Pinto, A., and Sangiovanni-Vincentelli, A. (2011). Model-Based Hierarchical Optimal Control Design for HVAC Systems. ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Volume 1, pages 271-278.
  13. Mattern, F., Staake, T., and Weiss, M. (2010). ICT for green How Computers Can Help Us to Conserve Energy. Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking - eEnergy 7810, page 1.
  14. Spagnolli, A., Corradi, N., Gamberini, L., Hoggan, E., Jacucci, G., Katzeff, C., Broms, L., and Jonsson, L. (2011). Eco-feedback on the go: Motivating energy awareness. Computer, 44(5):38-45.
  15. Sturzenegger, D., Gyalistras, D., Semeraro, V., Morari, M., and Smith, R. S. (2014). BRCM Matlab Toolbox : Model Generation for Model Predictive Building Control. American Control Conference.
  16. Wood, G. and Newborough, M. (2003). Dynamic energyconsumption indicators for domestic appliances: Environment, behaviour and design. Energy and Buildings, 35(8):821-841.
  17. Yu Zhun Jerry, Z. J., Haghighat, F., Fung, B. C. M., Morofsky, E., and Yoshino, H. (2011). A methodology for identifying and improving occupant behavior in residential buildings. Energy, 36(11):6596-6608.
  18. Zeifman, M. (2012). Disaggregation of home energy display data using probabilistic approach. IEEE Transactions on Consumer Electronics, 58(1):23-31.
Download


Paper Citation


in Harvard Style

Lilis G., Bansal S. and Kayal M. (2016). JouleSense: A Simulation based Platform for Proactive Feedback on Building Occupants’ Energy Use . In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-184-7, pages 279-285. DOI: 10.5220/0005778602790285


in Bibtex Style

@conference{smartgreens16,
author={Georgios Lilis and Shubham Bansal and Maher Kayal},
title={JouleSense: A Simulation based Platform for Proactive Feedback on Building Occupants’ Energy Use},
booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2016},
pages={279-285},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005778602790285},
isbn={978-989-758-184-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - JouleSense: A Simulation based Platform for Proactive Feedback on Building Occupants’ Energy Use
SN - 978-989-758-184-7
AU - Lilis G.
AU - Bansal S.
AU - Kayal M.
PY - 2016
SP - 279
EP - 285
DO - 10.5220/0005778602790285