Abstract
This paper proposes the SMACH multi-agent simulation framework that allows energy experts to run scenario-based experiments to investigate the link between residential electricity consumption and inhabitants behaviour. We first present the proposed meta-model and the associated simulator. We illustrate their use by specialist on concrete examples featuring classical household activities. We also put an emphasis on the systems adaptation mechanism that permits to outline emergent habits and other behavioural patterns.
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Notes
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When different individuals can perform the same task, each one is associated with a different instance.
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Minimal factor enabling complete differentiation between actions with similar priorities.
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The implementation detail of this mechanism is not detailed in this paper but it is very similar to the one presented in the previous subsection.
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In addition, another simulation analysis GUI mode is also available in order to compare several simulations: overall evolution and specific period of time can be compared with side by side diagrams.
References
Ha, D.L., Ploix, S., Zamaï, E., Jacomino, M.: A home automation system to improve household energy control. In: Proceedings of The 12th IFAC Symposium INCOM2006 (2006)
European Environment Agency. Energy and environment report. Technical report 6 (2008)
FIPA consortium. FIPA Communicative Act Library Specification and FIPA ACL Message Structure Specification. Technical report (2003)
Freire, R.Z., Oliveira, G.H., Mendes, N.: Predictive controllers for thermal comfort optimization and energy savings. Energ. Build. 40(7), 1353–1365 (2008)
Gil-Quijano, J., Sabouret, N.: Prediction of humans’ activity for learning the behaviors of electrical appliances in an intelligent ambiant environment. In: Proceedings of the IEEE/WIC/ACM IAT’10 International Conference, pp. 283–286 (2010)
Haradji, Y., Poizat, G., Sempé, F.: Human activity and social simulation. In: Duffy, V.G. (ed.) Advances in Applied Human Modeling and Simulation, pp. 416–425. CRC Press, Boca Raton (2012)
Kashif, A., Ploix, S., Dugdale, J., Le, X.H.B.: Simulating the dynamics of occupant behaviour for power management in residential buildings. Energ. Build. (2012, to appear)
Mahdavi, A., Pröglhöf, C.: Toward empirically-based models of people’s presence and actions in buildings. In: Proceedings of Building Simulation 09, pp. 537–544 (2009)
Massoud, A.S., Wollenberg, B.: Toward a smart grid: power delivery for the 21st century. IEEE Power Energ. Mag. 3(5), 34–41 (2005)
Newsham, G.R., Bowker, B.G.: The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: a review. Energ. Policy 38(7), 3289–3296 (2010)
North, M., Howe, T., Collier, N., Vos, J.: A declarative model assembly infrastructure for verification and validation. In: Takahashi, S., Sallach, D., Rouchier, J. (eds.) Advancing Social Simulation: The First World Congress, pp. 129–140. Springer, Kanazawa (2007)
Poquet, G., Dujin, A.: Pour les ménages, la recherche du confort prime encore sur les économies d’énergie. Consommation & Modes de Vie (CREDOC), 210 (2008)
Rogers, A., Maleki, S., Ghosh, S., Nicholas, R.J: Adaptive home heating control through Gaussian process prediction and mathematical programming. In: ATES’10, pp. 71–78 (2011)
Searle, J.: Speech Acts. Cambridge University Press, Cambridge (1969)
Sempé, F., Gil-Quijano, J.: Incremental and situated modeling for multi-agent based simulations. In Proceedings of the IEEE RIVF’10 International Conference, pp. 1–6 (2010)
Sierhuis, M., Clancey, W.J., van Hoof, R., de Hoog, R.: Modeling and simulating human activity. In: Intelligent Agents for Computer Supported Co-operative Work Workshop (2000)
Taillandier, P., Vo, D.-A., Amouroux, E., Drogoul, A.: GAMA: a simulation platform that integrates geographical information data, agent-based modeling and multi-scale control. In: Desai, N., Liu, A., Winikoff, M. (eds.) PRIMA 2010. LNCS, vol. 7057, pp. 242–258. Springer, Heidelberg (2012)
Widén, J., Wäckelgard, E.: A high-resolution stochastic model of domestic activity patterns and electricity demand. Appl. Energ. 87(6), 1880–1892 (2010)
Wilensky, U., Evanston, I.: Netlogo center for connected learning and computer based modeling. Technical report, Northwestern University (1999)
Yamaguchi, Y., Fujimoto, T., Shimoda, Y.: Occupant behavior model for households to estimate high-temporal resolution residential electricity demand profile. In: Proceedings of BS2011, pp. 1548–1555, Australia, Sydney (2011)
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Amouroux, É., Huraux, T., Sempé, F., Sabouret, N., Haradji, Y. (2014). SMACH: Agent-Based Simulation Investigation on Human Activities and Household Electrical Consumption. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2013. Communications in Computer and Information Science, vol 449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44440-5_12
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DOI: https://doi.org/10.1007/978-3-662-44440-5_12
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