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To address the major issues associated with using Time-Use Survey (TUS) forsimulating residential load curves, we present the SMACH approach, whichcombines qualitative and quantitative data with agent-based simulation. Ourmodel consists of autonomous agents assigned with daily tasks. The agents tryto accomplish their assigned tasks to the best of their abilities. Quantitativedata are used to generate tasks assignments. Qualitative studies allow us todefine how agents select, based on plausible cognitive principles, the tasks toaccomplish depending on the context. Our results show a better representationof 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 inactivity and energy simulation $\bullet$ Presentation of complementary databoth qualitative and quantitative used to complement TUS data $\bullet$Proposition of an agent-based approach that balances these limitations

A multi-sourced data and agent-based approach for complementing Time Use  Surveys in the context of residential human activity and load curve  simulation