A multi-sourced data and agent-based approach for complementing Time Use Surveys in the context of residential human activity and load curve simulation
This work addresses the problem of inaccurate residential energy simulation for researchers and planners by offering an incremental improvement over existing methods.
The paper tackled the limitations of Time-Use Surveys for simulating residential load curves by proposing the SMACH approach, which combines qualitative and quantitative data with agent-based simulation, resulting in better representation of weekdays and weekends, more flexible task-appliance associations, and improved load curve simulations compared to real data.
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