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

翻译：为克服时间使用调查（TUS）在模拟居民负荷曲线时的关键局限，我们提出SMACH方法，该方法将定性与定量数据相结合，并辅以智能体仿真。该模型由承担日常任务的自主智能体构成，智能体尝试以最佳能力完成分配的任务。定量数据用于生成任务分配，而定性研究则定义了智能体如何根据合理的认知原则，依据情境选择待执行任务。实验结果表明，相较于真实数据，该方法能更精确地表征工作日与周末差异，更灵活地建立任务与电器的关联，并显著提升负荷曲线的模拟效果。亮点：● 讨论时间使用调查（TUS）的局限性及其在活动与能源仿真中的应用；● 提出用于补充TUS数据的定性与定量互补数据；● 提出可克服上述局限性的基于智能体的方法。