Study of Human Activity Related to Residential Energy Consumption Using Multi-level Simulations

In this paper, we illustrate how multi-agent multi-level modeling can help energy experts to better understand and anticipate residential energy consumption. The problem we study is the anticipation of electricity consumption peaks. We explain in this context the benefit of the coexistence of microscopic (human activity) and macroscopic (social characteristics, overall consumption) levels of representation. We present briefly the SIMLAB model (Huraux et al., 2014) that extends the SMACH simulator (Amouroux et al., 2013) with coexisting levels on different modeling axes. We then present a model of the households activity and its electrical consumption consistent with energy experts’ observations in the residential sector. We show the impact of different social factors, such as individual sensitivity to price or to personal comfort, on the apparition of peaks on the consumption. We illustrate the contribution of multi-level modeling in the understanding of macroscopic phenomena.

[1]  Mike Hazas,et al.  The significance of difference:understanding variation in household energy consumption , 2011 .

[2]  Stéphane Ploix,et al.  Simulating the dynamics of occupant behaviour for power management in residential buildings , 2013 .

[3]  Anand S. Rao,et al.  BDI Agents: From Theory to Practice , 1995, ICMAS.

[4]  Gildas Morvan,et al.  IRM4MLS: The Influence Reaction Model for Multi-Level Simulation , 2010, MABS.

[5]  Arnaud Grandjean,et al.  Introduction de non linéarités et de non stationnarités dans les modèles de représentation de la demande électrique résidentielle , 2013 .

[6]  G. Rizzoni,et al.  A highly resolved modeling technique to simulate residential power demand , 2013 .

[7]  François Sempé,et al.  Simulating Human Activities to Investigate Household Energy Consumption , 2013, ICAART.

[8]  Marc Relieu,et al.  Introduction au numéro spécial « Activité et Action/ Cognition Située » , 2004 .

[9]  Philippe Mathieu,et al.  IODA: an interaction-oriented approach for multi-agent based simulations , 2010, Autonomous Agents and Multi-Agent Systems.

[10]  Laurent Navarro,et al.  Dynamic level of detail for large scale agent-based urban simulations , 2011, AAMAS.

[11]  Gildas Morvan,et al.  Multi-level agent-based modeling - Bibliography , 2012, ArXiv.

[12]  Vincent Chevrier,et al.  Multi-level modeling as a society of interacting models , 2013, SpringSim.

[13]  Yun Kyu Yi,et al.  Simulating human behavior and its impact on energy uses , 2011 .

[14]  Guillaume Deffuant,et al.  An Iterative Approach for Generating Statistically Realistic Populations of Households , 2010, PloS one.

[15]  Nicolas Sabouret,et al.  A Multi-level Model for Multi-agent based Simulation , 2014, ICAART.

[16]  Ben Croxford,et al.  Using agent-based modelling to simulate occupants' behaviours in response to summer overheating , 2014, ANSS 2014.

[17]  Joe H. Chow,et al.  Agent-based simulation of electricity markets: a survey of tools , 2007, Artificial Intelligence Review.

[18]  Philippe Mathieu,et al.  An Interaction-Oriented Model for Multi-Scale Simulation , 2011, IJCAI.

[19]  Lingfeng Wang,et al.  Development of multi-agent system for building energy and comfort management based on occupant behaviors , 2013 .

[20]  van J Joost Hoof,et al.  Forty years of Fanger’s model of thermal comfort: comfort for all? , 2008 .

[21]  Nelson Minar,et al.  The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations , 1996 .