Abstract
Modern intelligent buildings should be designed to provide comfort and optimal quality of life for occupants. Therefore, smart home devices that allow for the control of actual indoor ambience, which is critical to minimizing dissatisfaction, and simultaneously reduce the energy consumption must be modeled. In this paper, we propose an original model of the contribution of objects, where each object contributes to a type of ambience, namely, thermal, visual, acoustic and air quality. In other words, a given indoor ambience is defined by the contribution of objects. The discrete event system specification (DEVS) is a formalism for describing simulation models in a modular way. The modular nature of the DEVS formalism is exploited in this study by forming submodels of smart objects that allow domain experts to develop simulation techniques independently and later combine their work for reducing energy consumption and improving comfort. We have incorporated fuzzy reasoning methods in the DEVS formalism to account for the human perception error in an indoor environment, allowing for improved indoor environmental quality management in buildings.
Similar content being viewed by others
References
Aid L, Zaoui L, Mostefaoui SA (2013) A multi-sensory approach integrating user preferences. IACSIT Int J Eng Technol 5:68–72
Association K (2010) Chapter 3-datapoint types. The KNX Standard v2
Bonino D, Corno F (2008) Dogont–ontology modeling for intelligent domotic environments. Springer, Berlin, Heidelberg
Bonino D, Corno FD (2010) Dogsim: a state chart simulator for domotic environments. In: International conference on pervasive computing and communications workshops (PERCOM workshops), 2010. IEEE, pp 208–213. doi:10.1109/PERCOMW.2010.5470666
Bonino D, Corno F (2012) DoMAIns: domain-based modeling for ambient intelligence. Pervasive Mob Comput 8:614–628
Bovet G, Hennebert J (2013) A web-of-things gateway for knx networks. In: Proceedings of the 2013 European conference on smart objects, systems and technologies (SmartSysTech). VDE, pp 1–8
Clarke JA (2001) Energy simulation in building design. Routledge
Cole RJ, Brown Z (2009) Reconciling human and automated intelligence in the provision of occupant comfort. Intell Build Int 1:39–55
Conte G, Scaradozzi D (2007) An approach to home automation by means of MAS theory. In: Ioannou P, Pitsillides A (eds) Modeling and control of complex systems. CRC Press, Boca Raton, FL, pp 461–484
Conte G, Scaradozzi D, Perdon A, Cesaretti M, Morganti G (2007) A simulation environment for the analysis of home automation systems. In: Proceedings of the 2007 mediterranean conference on control & automation (MED’07). IEEE, pp 1–8. doi:10.1109/MED.2007.4433913
European Committee for Standardization (2002) Light and lighting. Lighting of work places. Part 1: EN 12464-1. Indoor work places
Filippi J-B, Bisgambiglia P (2004) JDEVS: an implementation of a DEVS based formal framework for environmental modelling. Environ Model Softw 19:261–274
Freigassner R, Praehofer H, Zeigler B (2000) Systems approach to validation of simulation models. In: European meeting on cyberntics and systems research 2000, Vienna, pp 52–57
Gallissot M, Caelen J, Bonnefond N, Meillon B, Pons S (2011) Using the multicom domus dataset. LIG, Grenoble, France, Research Report RR-LIG-020
Guarracino G, Kolokotsa D, Geros V (2003) Advanced control systems for energy and environmental performance of buildings. In: Santamouris M (ed) Solar thermal technologies for buildings. CRC Press, Boca Raton, FL, pp 65–89
Gunay HB, O’Brien L, Goldstein R, Breslav S, Khan A (2013) Development of discrete event system specification (DEVS) building performance models for building energy design. In: Proceedings of the symposium on simulation for architecture & urban design, 2013, p 22. Society for Computer Simulation International
Gunay HB, O’Brien W, Beausoleil-Morrison I, Goldstein R, Breslav S, Khan A (2014) Coupling stochastic occupant models to building performance simulation using the discrete event system specification formalism. J Build Perform Simul 7:457–478
Hopfe CJ (2009) Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization. PhD diss, Eindhoven University
Hoyt T, Arens E, Zhang H (2015) Extending air temperature setpoints: simulated energy savings and design considerations for new and retrofit buildings. Build Environ 88:89–96
Inostrosa-Psijas A, Wainer G, Gil-Costa V, Marin M (2014) DEVs modeling of large scale web search engines. In: Simulation conference (WSC), 2014 Winter, 2014. IEEE, pp 3060–3071
ISO (2004) 140-14: acoustics—measurement of sound insulation in buildings and of building elements—part 14: guidelines for special situations in the field. International Organization for Standardization
ISO (2005) 15927-4: hygrothermal performance of buildings—calculation and presentation of climatic data—part 4: hourly data for assessing the annual energy use for heating and cooling. International Organization for Standardization, Geneva
Kim D-W, Park C-S (2009) Manual vs. optimal control of exterior and interior blind systems. In: Proceedings 11th international IBPSA conference Glasgow, Scotland, pp 1663–1670, 2009
Kofod-Petersen A, Aamodt A (2006) Contextualised ambient intelligence through case-based reasoning. In: Roth-Berghofer TR, Goker MH, Guvenir HA (eds) Proceedings of the eighth European conference on: advances in case-based reasoning, Oludeniz, Turkey. Springer, pp 211–225
Kolokotsa D, Tsiavos D, Stavrakakis G, Kalaitzakis K, Antonidakis E (2001) Advanced fuzzy logic controllers design and evaluation for buildings’ occupants thermal–visual comfort and indoor air quality satisfaction. Energy Build 33:531–543
Li Y, Grabham NJ, Beeby SP, Tudor M (2015) The effect of the type of illumination on the energy harvesting performance of solar cells. Sol Energy 111:21–29
Maatoug A, Belalem G (2014) Conception and validation of smart building energy management system BEMS using the discrete event system specification. DEVS J Commun Softw Syst 10:2
Mittal S, Zeigler BP, Martin J (2009) Implementation of formal standard for interoperability in M&S/systems of systems integration with DEVS/SOA. Int C2 J 3:1
Mostefaoui SAM, Zaoui L, Aid L (2014) Bayesian model of multisensory comfort and adaptation in intelligent building. Int J Smart Home 8:125–140
Muhamad WNW, Zain MYM, Wahab N, Aziz NHA, Kadir RA (2010) Energy efficient lighting system design for building. In: Intelligent systems, modelling and simulation (ISMS), 2010 international conference. IEEE, pp 282–286 doi:10.1109/ISMS.2010.59
Multicom (2015) Multicom. Equipe du LIG: Laboratoire d’Informatique de Grenoble. http://multicom.imag.fr/multicom.imag.fr/index.html. Accessed 21 Sept 2015
Preuveneers D et al (2004) Towards an extensible context ontology for ambient intelligence. In: Markopoulos P, Eggen B, Aarts E, Crowley JL (eds) EUSAI 2004. LNCS, vol 3295. Springer, Heidelberg, pp 148–159
Saade JJ, Ramadan AH, Li Q, Chen S, Xu A (2008) Fuzzy inference-based control approach for thermal–visual comfort and air quality in indoor environments. In: WSEAS international conference. Proceedings. Mathematics and computers in science and engineering, 2008, vol 8. World Scientific and Engineering Academy and Society
Serghides D, Chatzinikola C, Katafygiotou M (2015) Comparative studies of the occupants’ behaviour in a university building during winter and summer time. Int J Sustain Energy 34:528–551
Terano T, Asai K, Sugeno M (1992) Fuzzy systems theory and its applications. Academic Press Professional Inc, San Diego
Verbraeck A, Valentin EC (2008) Design guidelines for simulation building blocks. In: Proceedings of the 40th conference on winter simulation, 2008. Winter simulation conference, pp 923–932
Wainer GA (2014) Applying modeling and simulation for development of embedded systems. In: Proceedings of the 2nd ACM SIGSIM/PADS conference on principles of advanced discrete simulation, 2014. ACM, pp 1–2
Wainer GA, Mosterman PJ (2010) Discrete-event modeling and simulation: theory and applications. CRC Press, Boca Raton
Wang S, Wainer G, Goldstein R, Khan A (2013) Solutions for scalability in building information modeling and simulation-based design. In: Proceedings of the symposium on simulation for architecture & urban design, 2013. Society for Computer Simulation International, p 7
Zadeh LA (1965) Information and control. Fuzzy Sets 8:338–353
Zeigler BP, Praehofer H, Kim TG (2000) Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems. Academic press, San Diego
Acknowledgments
We thank all Multicom team, including Professor Jean Caelen who welcomed us into his laboratory, and has consecrated for us the platform DOMUS.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Aid, L., Zaoui, L. & Mostefaoui, S.A.M. Using DEVS for modeling and simulation of ambient objects in intelligent buildings. J Ambient Intell Human Comput 7, 579–592 (2016). https://doi.org/10.1007/s12652-016-0352-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-016-0352-9