Prediction of the lighting energy‐saving effect by human detection control with an office worker agent model

The energy‐saving effect of lighting control in the absence of workers in an office is predicted by using simulations. We divided an office space into meshes and constructed an agent model of an office worker who move between meshes by using the A* algorithm. By examining many office worker models, we calculated the time a worker spent in each office's mesh. We controlled the lighting fixtures in this virtual office room based on human sensors. We quantitatively evaluated the influence of conditions such as the occupancy rate, retention time, fade‐out time, and minimum output rate on the lighting rate of the lighting fixtures.