Smart Homes are currently one of the hottest topics in the area of Internet of Things or Augmented Living. In order to provide high-level intelligent solutions, algorithms for identifying which activities the inhabitants intend to perform are necessary. Sensor data plays here an essential role, for testing, for learning underlying rules, for classifying and connecting sensor patterns and to inhabitant activities, etc. However, only few and limited data sets are currently available. We present concepts and solutions for generating high-quality data using a flexible agent-based simulation tool. The basic idea is to integrate the simulation of a sensorized apartment with human behavior modelling based on constraint-based planning that produces a sequence of daily activities. The overall set-up is shown to generate data that exhibits the same relevant properties as data from a comparable real-world apartment.