  To address the major issues associated with using Time-Use Survey (TUS) for
simulating residential load curves, we present the SMACH approach, which
combines qualitative and quantitative data with agent-based simulation. Our
model consists of autonomous agents assigned with daily tasks. The agents try
to accomplish their assigned tasks to the best of their abilities. Quantitative
data are used to generate tasks assignments. Qualitative studies allow us to
define how agents select, based on plausible cognitive principles, the tasks to
accomplish depending on the context. Our results show a better representation
of weekdays and weekends, a more flexible association of tasks with appliances,
and an improved simulation of load curves compared to real data. Highlights
$\bullet$ Discussion about Time-Use Surveys (TUS) limits and the use of TUS in
activity and energy simulation $\bullet$ Presentation of complementary data
both qualitative and quantitative used to complement TUS data $\bullet$
Proposition of an agent-based approach that balances these limitations

Artificial Intelligence;Human-Computer Interaction;Multiagent Systems