Abstract
Building energy consumption and environmental emission are significantly influenced by end-users, and building energy simulations tools are used to optimize the performance of the building. Currently, most of the simulation tools considered oversimplified behaviour and contribute to the energy gap between the predicted and actual consumption. However, the building energy performance also depends on occupant dynamic behaviours and this tools fails to capture the dynamic occupant behaviour. To overcome this, developing a co-simulation platform is an effective approach to integrate an occupant behaviour modelling using a multi-agent-based simulation with building energy simulation tools. The co-simulation process is conducted in Building Control Virtual Testbed (BCVTB), a virtual simulation coupling tool that integrates the two separate simulations on a time step basis. This method is applied to a case study of a multi-occupant office building within an engineering school in France. The result shows the applicability and relevance of the developed platform.
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References
De Silva, M. N. K., Sandanayake, Y.G.: Building energy consumption factors: a literature review and future research agenda, Digital library University of Moratuwa, Sri Lanka, pp. 90–99, 2012. https://dl.lib.mrt.ac.lk/handle/123/12050
Jia, M., Srinivasan, R.S., Ries, R., Bharathy, G.: Exploring the validity of occupant behavior model for improving office building energy simulation. In: 2018 Winter Simulation Conference (WSC), Gothenburg, Sweden, 2018, pp. 3953–3964. https://doi.org/10.1109/WSC.2018.8632278
Paone, A., Bacher, J.P.: The impact of building occupant behavior on energy efficiency and methods to influence it: a review of the state of the art, Energies, 11, 4 (2018)
Pérez-Lombard, L., Ortiz, J., Pout, C.: A review on buildings energy consumption information. Energy Build. 40(3), 394–398 (2008)
Wang, C., Yan, D., Ren, X.: Modeling individual’s light switching behavior to understand lighting energy use of office building. Energy Procedia 88, 781–787 (2016)
Delzendeh, E., Wu, S., Lee, A., Zhou, Y.: The impact of occupants’ behaviours on building energy analysis: A research review. Renew. Sustain. Energy Rev. 80, 1061–1071 (2017)
Schaumann, D., Putievsky, N., Sopher, H., Yahav, J., Kalay, Y.E.: Simulating multi-agent narratives for pre-occupancy evaluation of architectural designs. Autom. Constr. 106, 102896 (2018)
Gilani, S., O’Brien, W.: Best Practices Guidebook on Advanced Occupant Modelling. Carleton University, Ottawa, Canada (2018)
Turner, W., Hong, T.: A technical framework to describe occupant behavior for building energy simulations, Lawrence Berkeley National Laboratory (2013)
Sait, H.H.: Auditing and analysis of energy consumption of an educational building in hot and humid area. Energy Convers. Manag. 66, 143–152 (2013)
Piotrowska, E., Borchert, A.: Energy consumption of buildings depends on the daylight. In: E3S Web Conference, vol. 14 (2017)
Rijal, H.B., Humphreys, M.A., Nicol, J.F.: Development of a window opening algorithm based on adaptive thermal comfort to predict occupant behavior in Japanese dwellings. Japan Archit. Rev. 1(3), 310–321 (2018)
Chapman, J., Siebers, P.O., Robinson, D.: On the multi-agent stochastic simulation of occupants in buildings. J. Build. Perform. Simul. 11(5), 604–621 (2018)
Chapman, J., Siebers, P., Robinson, D.: Coupling multi-agent stochastic simulation of occupants with building simulation, Envir. Phys. Des. (ePAD), The University of Nottingham, no. 2004 (2011)
Li, R., Wei, F., Zhao, Y., Zeiler, W.: Implementing occupant behaviour in the simulation of building energy performance and energy flexibility: development of co-simulation framework and case study. In: Proceedings 15th IBPSA Conference, October 2017
Jia, M., Srinivasan, R.S., Ries, R., Bharathy, G.: A framework of occupant behavior modeling and data sensing for improving building energy simulation. Simul. Ser. 50(7), 110–117 (2018)
Chen, Y., Liang, X., Hong, T.: Simulation and visualization of energy- related occupant behavior in office buildings. Build. Simulations 10, 785–798 (2017). https://doi.org/10.1007/s12273-017-0355-2
Raad, A., Reinbold, V., Delinchant, B., Wurtz, F.: FMU software component orchestration strategies for co-simulation of building energy systems. In: 3rd International Conference Technology Advance Electronic Electron Computer Engineering TAEECE 2015, pp. 7–11 (2015)
Kashif, A., Ploix, S., Dugdale, J., Le, X.H.B.: Simulating the dynamics of occupant behaviour for power management in residential buildings. Energy Build. 56, 85–93 (2013)
Lez-Briones, A.G., De La Prieta, F., Mohamad, M.S., Omatu, S., Corchado, J.M.: Multi-agent systems applications in energy optimization problems: A state-of-the-art review. Energies 11(8), 1–28 (2018)
Sousa, J.: Energy Simulation Software for Buildings : Review and Comparison, Technical Report, University of Porto. https://ceur-ws.org/Vol-923/paper08.pdf
Crawley, D.B., et al.: EnergyPlus: Creating a new-generation building energy simulation program. Energy Build. 33(4), 319–331 (2001)
Acknowledgments
The author acknowledge the financial support of the CPER FORBOIS 2–2016-2020 project. Secondly, the author also acknowledge Campus France and the Ethiopian Ministry of Science and Higher Education for their financial support.
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Ebuy, H.T., Bril El-Haouzi, H., Pannequin, R., Benelmir, R. (2021). Multi-agent Simulation of Occupant Behaviour Impact on Building Energy Consumption. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Lamouri, S. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2020. Studies in Computational Intelligence, vol 952. Springer, Cham. https://doi.org/10.1007/978-3-030-69373-2_25
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